Information processing apparatus, information processing method, and program

Abstract

To provide an information processor capable of appropriately supporting material development.SOLUTION: The information processing device includes a memory and a processor, and the processor executes a step of generating a list image for displaying compound information associated with each of a plurality of pieces of experiment data in a list format, a step of receiving selection of one piece of compound information displayed in the list image, a step of acquiring experiment data including a process condition and characteristic information corresponding to the selected compound information, and a step of displaying the acquired experiment data on a display unit.SELECTED DRAWING: Figure 12

Description

[Technical Field]

[0001] The present disclosure relates to techniques for displaying material properties. [Background technology]

[0002] In conventional materials development, in order to search for a composition formula or process conditions with desired properties, experiments have been conducted by changing the blending ratio of raw materials to identify a composition formula or a range of process conditions with good properties. Property display methods have been proposed that display experimental or predicted values ​​of material properties (see, for example, Patent Document 1 and Non-Patent Document 1). [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Patent No. 4009670 [Non-patent literature]

[0004] [Non-Patent Document 1] Kei Terayama, Koji Tsuda, Ryo Tamura. Efficient recommendation tool of materials by an executable file based on machine learning. Japanese Journal of Applied Physics, 58(098001)2019. Summary of the Invention

[0005] However, it is difficult for the above-mentioned conventional techniques to adequately support material development.

[0006] The present disclosure provides a property display method, a property display device, an information processing device, and a program for executing the property display method, which can appropriately support material development. [Means for solving the problem]

[0007] An information processing device according to one embodiment of the present disclosure is an information processing device including a memory and a processor, wherein the processor uses the memory to perform the steps of generating a list image that displays compound information associated with each of a plurality of experimental data in list format, accepting selection of one of the compound information displayed in the list image, acquiring the experimental data including process conditions and characteristic information corresponding to the selected compound information, and displaying the acquired experimental data on a display unit.

[0008] a fourth step of generating a map showing the predicted property values ​​of the compound corresponding to the determined compound structure at positions corresponding to the determined compound structure, and superimposing the acquired experimental property values ​​on the map at positions corresponding to the compound structure corresponding to the acquired experimental property values, thereby generating a first image and outputting the first image to a display unit; a fifth step of acquiring compound identification information corresponding to the experimental property value selected on the map; a sixth step of acquiring compound information data associated with the compound identification information and related to the selected experimental property value; and a seventh step of generating a second image including a compound data image showing the compound information data and the first image, and outputting the second image to a display unit.

[0009] A property display device according to one aspect of the present disclosure includes a first acquisition unit that acquires a plurality of variables that determine a compound configuration and a plurality of option data items that indicate values ​​or elements that the variables can take for each of the plurality of variables; a second acquisition unit that determines the compound configuration by selecting one option data item from the plurality of option data items for each of the plurality of variables and acquires a predicted property value of the compound corresponding to the determined compound configuration; a third acquisition unit that acquires an experimental property value of the compound corresponding to the determined compound configuration; and a property display unit that displays the property value of the compound corresponding to the determined compound configuration at a position according to the determined compound configuration. The system includes a first image processing unit that generates a map indicating predicted values, and for the acquired characteristic experimental values, generates a first image by superimposing the acquired characteristic experimental values ​​at positions on the map according to the structure of the compound corresponding to the characteristic experimental values, and outputs the first image to a display unit; a fourth acquisition unit that acquires compound identification information corresponding to the characteristic experimental value selected on the map; a fifth acquisition unit that acquires compound information data associated with the compound identification information and related to the selected characteristic experimental value; and a second image processing unit that generates a compound data image indicating the compound information data and a second image including the first image, and outputs the second image to a display unit.

[0010] A program according to one aspect of the present disclosure includes a first step of acquiring a plurality of variables that determine a compound configuration and a plurality of choice data items that indicate values ​​or elements that the variables can take for each of the plurality of variables; a second step of determining the compound configuration by selecting one choice data item from the plurality of choice data items for each of the plurality of variables and acquiring a predicted property value of the compound corresponding to the determined compound configuration; a third step of acquiring an experimental property value of the compound corresponding to the determined compound configuration; and a matrix indicating the predicted property value of the compound corresponding to the determined compound configuration at a position according to the determined compound configuration. a fourth step of generating a map, and for the acquired experimental characteristic value, generating a first image by superimposing the experimental characteristic value at a position on the map according to the structure of the compound corresponding to the experimental characteristic value, and outputting the generated image to a display unit; a fifth step of acquiring compound identification information corresponding to the experimental characteristic value selected on the map; a sixth step of acquiring compound information data associated with the compound identification information and related to the selected experimental characteristic value; and a seventh step of generating a second image in which a compound data image showing the compound information data and the first image are displayed on a single screen, and outputting the generated image to a display unit.

[0011] These comprehensive or specific aspects may be realized as a system, an integrated circuit, or a computer-readable recording medium, or as any combination of a method, an apparatus, a system, a method, an integrated circuit, a computer program, and a recording medium. Computer-readable recording media include non-volatile recording media such as CD-ROMs (Compact Disc-Read Only Memory). [Effects of the Invention]

[0012] According to the present disclosure, material development can be appropriately supported.

[0013] Further advantages and benefits of certain aspects of the present disclosure will become apparent from the specification and drawings. Such advantages and / or benefits may be provided by some of the embodiments and features described in the specification and drawings, respectively, but not necessarily all of them may be provided to obtain one or more identical features. [Brief explanation of the drawings]

[0014] [Figure 1] FIG. 1 is a block diagram showing a functional configuration of a display system according to embodiments 1-1 and 1-2. [Figure 2] FIG. 10 is a diagram illustrating an example of a search range. [Figure 3] FIG. 10 is a diagram illustrating an example of experimental data. [Figure 4A] FIG. 2 is a diagram showing an example of a first image. [Figure 4B] FIG. 10 is a diagram showing another example of the first image. [Figure 4C] FIG. 5C is a diagram showing the legend of the first image in FIGS. 4B and 5B. FIG. [Figure 5A] FIG. 10 is a diagram showing an example of highlighting of characteristic experimental values. [Figure 5B] FIG. 10 is a diagram showing another example of highlighting of characteristic experimental values. [Figure 6] 1A and 1B are diagrams showing examples of compound structure data and compound graph data; [Figure 7] 1A and 1B are diagrams showing examples of a compound configuration data image and a compound graph data image. [Figure 8] FIG. 10 is a diagram showing an example of a second image. [Figure 9] FIG. 10 is a diagram showing an example in which a selected characteristic experimental value is highlighted in a second image. [Figure 10A] FIG. 10 is a diagram showing an example of transition of a display screen according to embodiment 1-1. [Figure 10B] FIG. 10 is a diagram showing another example of transition of the display screen according to embodiment 1-1. [Figure 10C] FIG. 10 is a diagram showing another example of transition of the display screen according to embodiment 1-1. [Figure 11]FIG. 10 is a diagram showing another example of transition of the display screen according to embodiment 1-1. [Figure 12] FIG. 10 is a diagram showing an example of a compound data image in which a plurality of characteristic experimental values ​​are selected. [Figure 13] FIG. 10 is a diagram showing another example of a compound data image in a case where a plurality of characteristic experimental values ​​are selected. [Figure 14] FIG. 10 is a diagram showing an example of transition of a display screen according to embodiment 1-2. [Figure 15] FIG. 10 is a diagram showing another example of transition of the display screen according to embodiment 1-2. [Figure 16] 10 is a flowchart showing an example of the overall flow of processing by the characteristic display device according to the embodiments 1-1 and 1-2. [Figure 17] FIG. 10 is a block diagram showing a functional configuration of a display system according to a second embodiment. [Figure 18] FIG. 10 is a diagram illustrating an example of input of editing information. [Figure 19] FIG. 10 is a diagram illustrating an example of an edited image. [Figure 20] 10 is a flowchart showing an example of an overall flow of processing by the characteristic display device according to the second embodiment. [Figure 21] FIG. 10 is a block diagram showing a functional configuration of a display system according to a third embodiment. [Figure 22] FIG. 10 is a diagram showing an example of a first image having a data read button and a data save button. [Figure 23] 11 is a flowchart showing an example of an overall flow of processing by the characteristic display device according to the third embodiment. [Figure 24] FIG. 10 is a block diagram showing a functional configuration of a display system according to a fourth embodiment. [Figure 25] FIG. 13 is a diagram showing an example of a second image having a data correction button according to the fourth embodiment. [Figure 26A] FIG. 13 is a diagram showing an example of a transition from a first image to a second image according to the fourth embodiment. [Figure 26B] FIG. 13 is a diagram showing an example of a transition from a second image to a fifth image due to correction according to the fourth embodiment. [Figure 27] 10 is a flowchart showing an example of an overall flow of processing by the characteristic display device according to the fourth embodiment. [Figure 28] FIG. 11 is a block diagram showing a functional configuration of a display system according to a fifth embodiment. [Figure 29] FIG. 13 is a diagram showing an example of a first image having a prediction model display button according to the fifth embodiment. [Figure 30] FIG. 13 is a diagram showing an example of transition of a display screen according to the fifth embodiment. [Figure 31] 13 is a flowchart showing an example of an overall flow of processing by the characteristic display device according to the fifth embodiment. [Figure 32] FIG. 13 is a block diagram showing a functional configuration of a display system according to a sixth embodiment. [Figure 33] FIG. 20 is a diagram showing an example of a compound graph data image according to the sixth embodiment. [Figure 34] FIG. 20 is a diagram showing another example of a compound graph data image according to the sixth embodiment. [Figure 35] FIG. 20 is a diagram showing an example of transition of a display screen according to the sixth embodiment. [Figure 36] 13 is a flowchart showing an example of an overall flow of processing by the characteristic display device according to the sixth embodiment. DETAILED DESCRIPTION OF THE INVENTION

[0015] (Findings that led to this disclosure) In recent years, in fields such as image recognition and natural language processing, recognition and identification methods using machine learning have made significant progress, and are beginning to be applied to predicting material properties, such as materials informatics (MI). For example, Non-Patent Document 1 discloses a method for optimizing material properties and creating phase diagrams using Bayesian optimization. As a demonstration example, the results of a low-melting-point composition search or phase diagram search in the NaF-KF-LiF system are displayed on a triangular phase diagram, with experimental values ​​superimposed on predicted values. However, the system does not have a function to display compound information data linked to experimental values.

[0016] Patent Document 1 handles a huge number of compounding ratios and outputs compounding ratios that can achieve the target physicochemical properties. However, it does not disclose a method for tracking experimental compound composition data or graph data by displaying them together with the experimental values.

[0017] Therefore, the present inventors have found that centralized management is possible by using an experimental database in which compound identification information is assigned to experimental data and stored, or by using an experimental database in which compound identification information is automatically assigned to experimental data, to track information on experimental data corresponding to experimental values ​​or predicted values ​​and display compound information data, and have arrived at the present disclosure.

[0018] a fourth step of generating a map showing the predicted property values ​​of the compound corresponding to the determined compound structure at positions corresponding to the determined compound structure, and superimposing the acquired experimental property values ​​on the map at positions corresponding to the compound structure corresponding to the acquired experimental property values, thereby generating a first image and outputting the first image to a display unit; a fifth step of acquiring compound identification information corresponding to the experimental property value selected on the map; a sixth step of acquiring compound information data associated with the compound identification information and related to the selected experimental property value; and a seventh step of generating a second image including a compound data image showing the compound information data and the first image, and outputting the second image to a display unit.

[0019] Here, the configuration of the compound is determined for each combination of option data obtained by selecting one option data from multiple option data for each of multiple variables. That is, a predicted property value of the compound is obtained for each combination of option data. An experimental property value of the compound is obtained corresponding to at least one combination of option data.

[0020] This generates and displays a first image in which the experimental property values ​​are superimposed on a map showing predicted property values ​​for each combination of option data, i.e., predicted property values ​​for each configuration of the compound. Therefore, for example, if a computer user inputs many variables and many option data to be substituted for each of the many variables into the computer, a map showing a huge amount of information can be displayed. In other words, the map shows the predicted property values ​​for each of the many possible configurations (i.e., compositional formulas or chemical formulas) of the compound, corresponding to the configuration, and also shows the experimental property values ​​for at least one configuration, corresponding to the configuration. Therefore, by looking at the map, the user can easily grasp the predicted property values ​​and experimental property values ​​for all configurations of the compound.

[0021] Furthermore, in a characteristic display method according to one embodiment of the present disclosure, when a characteristic experimental value superimposed on the map is selected by a user's input operation, a second image is generated and displayed. That is, a compound data image showing compound information data related to the selected characteristic experimental value is displayed on one screen together with the first image. As a result, by performing a simple input operation, the user can display a compound data image showing compound information data related to the characteristic experimental value of interest. The user can easily visually recognize and understand the compound information data shown in the compound data image, the predicted characteristic values ​​of each component shown in the first image, and the selected characteristic experimental value without switching screens.

[0022] On the other hand, in Patent Document 1 and Non-Patent Document 1, experimental or predicted values ​​of material properties are displayed for a composition formula or a search range of process conditions, but compound information data linked to the experimental values ​​cannot be displayed. Therefore, a property display method according to one embodiment of the present disclosure can display the compound information data. In other words, the compound information data can be tracked and displayed as information on the above-mentioned experimental data, thereby enabling the unified management of experimental property values, predicted property values, and compound information data. As a result, it is possible to appropriately support users in material development.

[0023] The characteristic display method may further include a step of generating a reduced first image by reducing the first image before generating the second image in the seventh step, and in the seventh step, the second image including the compound data image and the reduced first image may be generated and output to a display unit.

[0024] Therefore, before the second image is generated, the first image is reduced in size to generate the second image, and therefore in the fourth step, the first image can be displayed enlarged on the screen, thereby improving the visibility of the first image.

[0025] The screen on which the first image and the second image are displayed in the fourth step and the seventh step may comprise a first region and a second region, and in the fourth step, the first image arranged in the first region may be output to a display unit, and in the seventh step, the first image may be arranged in the first region and the compound data image may be arranged in the second region to generate the second image and output it to the display unit.

[0026] As a result, the layout and size of the first image displayed in the fourth step are the same as those in the seventh step, which reduces the processing load on the first image and improves the processing speed. Furthermore, even when the displayed image is switched from the first image to the second image, the layout and size of the first image do not change, which reduces the sense of discomfort felt by the user when the image is switched.

[0027] The compound information data may include compound configuration data and compound graph data related to the characteristic experimental values.

[0028] For example, the compound constitution data is data showing information about a compound in a table format, and the compound graph data is data showing information about a compound in a graph format. Therefore, an image showing these data is displayed as a compound data image, so that the user can easily grasp various information about the compound related to the selected experimental characteristic value from multiple perspectives. When the compound constitution data and the compound graph data are data used to derive the experimental characteristic value, the user can easily grasp the raw data of the experimental characteristic value.

[0029] In the fifth step, a plurality of the characteristic experimental values ​​superimposed on the map may be selected, and the compound identification information corresponding to each of the plurality of the characteristic experimental values ​​may be acquired; in the sixth step, the compound information data corresponding to each of the plurality of the compound identification information may be acquired; and in the seventh step, the compound data image may be generated by superimposing the compound graph data of each of the acquired plurality of the compound information data.

[0030] This allows a plurality of compound graph data relating to a plurality of characteristic experimental values ​​to be displayed in an overlapping manner, allowing the user to easily grasp the differences between the compound graph data.

[0031] The compound data image in the seventh step may include a graph superimposed image showing the compound graph data of each of the acquired plurality of compound information data superimposed thereon, and a list image showing a list of the compound identification information associated with each of the acquired plurality of compound information data.

[0032] This allows the list image to be displayed, allowing the user to easily grasp the compound identification information of each of the multiple compound graph data that are superimposed.

[0033] The compound data image in the seventh step may include a graph superimposed image showing the compound graph data of each of the acquired plurality of compound information data superimposed thereon, and a tab switching image having a plurality of tabs for switching and displaying the compound configuration data corresponding to each of the plurality of compound graph data.

[0034] This allows the user to easily visually recognize and understand the compound constitution data related to those experimental property values ​​by switching between tabs, even when multiple experimental property values ​​are selected.

[0035] After the seventh step, at least one of the plurality of superimposed compound graph data displayed by the graph superimposed image may be edited, and a third image may be generated based on the editing and output to a display unit. For example, the editing may include switching between a displayed state and a hidden state a state of a selected compound graph data from the plurality of superimposed compound graph data and the compound identification information associated with the compound graph data. Alternatively, the editing may include selecting a partial area of ​​the graph superimposed image and enlarging and displaying the selected area.

[0036] This makes it possible to further improve the visibility of the multiple compound graph data that are superimposed.

[0037] After the seventh step, a partial area of ​​an image showing the compound graph data, which is included in the compound data image, may be selected, and the selected area may be enlarged and displayed.

[0038] This makes it possible to further improve the visibility of one compound graph data even when the compound data image shows the compound graph data.

[0039] After at least one of the first image and the second image is output to a display unit, the output image may be converted into a file of a predetermined format and saved. In this case, the saved file may be acquired, and a fourth image may be generated by reconstructing the acquired file into an image, and the fourth image may be output to a display unit.

[0040] This allows multiple computers to save files and reconstruct them by reading them out via the server, even if the files are stored on a cloud server. As a result, users of those computers can view the same images even if they are far away from each other, allowing for lively discussions about material properties. In other words, images can be shared.

[0041] In the seventh step, if information included in the compound information data shown in the second image is corrected after the second image is output, the corrected information may be output to a compound information database that stores the compound information data before correction, and a fifth image may be generated by correcting the second image based on the corrected information and output to a display unit.

[0042] For example, the experimental characteristic value superimposed on the first image in the second image may be calculated based on information contained in the compound information data. In such a case, if the information contained in the compound information data contains an error, the experimental characteristic value superimposed on the first image will deviate from the predicted characteristic value. Therefore, in a characteristic display method according to one embodiment of the present disclosure, the second image is corrected by correcting the information contained in the compound information data, so that, for example, the experimental characteristic value of the second image can be correctly corrected. Therefore, even if there is an error in the compound information data, the second image can be easily corrected appropriately.

[0043] In the fourth step, after outputting the first image, a prediction model image based on the plurality of predicted characteristic values ​​and at least one experimental characteristic value shown in the first image may be generated, a sixth image including the first image and the prediction model image may be generated and output to a display unit, and when the experimental characteristic value shown in the prediction model image is selected, the compound identification information corresponding to the selected experimental characteristic value may be obtained, the compound information data associated with the compound identification information may be obtained, and a seventh image including an image showing the compound information data and the sixth image may be generated and output to a display unit.

[0044] This allows not only the experimental characteristic value shown in the first image, but also the compound information data related to the experimental characteristic value when the experimental characteristic value shown in the prediction model image in the sixth image is selected. Therefore, the user can easily select, for example, an experimental characteristic value that significantly differs from the predicted characteristic value from the prediction model image showing the relationship between the predicted characteristic value and the experimental characteristic value, and refer to the compound information data. The seventh image contains the first image, the prediction model image, and an image showing the compound information data on a single screen, allowing the user to check these images at a glance without switching screens.

[0045] In the sixth step, the compound information data associated with the compound identification information may be acquired, and characteristic prediction data corresponding to the compound identification information may be calculated. An eighth image may be generated based on the compound information data and the characteristic prediction data, and a ninth image including the eighth image and the first image may be generated and output to a display unit.

[0046] This allows, for example, the generation of an eighth image showing the difference between the compound information data and the property prediction data, and the eighth image can be displayed together with the first image on one screen, thereby enabling more appropriate support for material development.

[0047] Of the plurality of variables, the number of first variables into which the option data indicating element species is substituted may be four or more.

[0048] This allows maps to be generated for compounds composed of four or more elements, and by changing the combinations, maps showing information for many composition formulas (or chemical formulas) can be generated. In other words, maps showing predicted property values ​​for each compound expressed by many composition formulas (or chemical formulas) can be generated. This allows for more effective support of materials development.

[0049] Of the plurality of variables, the number of second variables into which the option data indicating a value is substituted may be four or more.

[0050] This allows maps to be generated for compounds that are composed of a combination of four or more values ​​(i.e., element coefficients), and by changing the combinations, it is possible to generate maps that show information for many composition formulas (or chemical formulas). In other words, it is possible to generate maps that show predicted property values ​​for each of compounds expressed by many composition formulas (or chemical formulas). This allows for more effective support of materials development.

[0051] The map includes an image map configured by a first axis and a second axis corresponding to two of the four or more second variables, and the image map includes a plurality of image element maps arranged in a matrix along each of the first axis and the second axis, and each of the plurality of image element maps is configured by a third axis and a fourth axis corresponding to the other two of the four or more second variables, and at a position on the image element map corresponding to the determined compound configuration, a predicted property value of the compound corresponding to the determined compound configuration may be indicated by a color or a shade of color.

[0052] This makes it possible to generate a map in which the predicted property values ​​of each combination, that is, the compounds expressed by each composition formula (or each chemical formula), are easy to view, even if the number of second variables is four or more.

[0053] In the second step, the determined compound structure is input into a machine learning model using a predetermined calculation algorithm to obtain a predicted property value of the compound corresponding to the compound structure, and the machine learning model may be a model trained by machine learning so as to output the predicted compound property value corresponding to the compound structure in response to the input of the determined compound structure.

[0054] For example, by performing machine learning on a machine learning model using many combinations of option data and experimental characteristic values ​​for those combinations as training data, a machine learning model with high prediction accuracy can be generated. Therefore, by using the machine learning model, it is possible to obtain highly accurate characteristic prediction values.

[0055] A characteristic display method according to one embodiment of the present disclosure includes a first step of acquiring a map showing positions corresponding to each structure of a compound; a second step of acquiring experimental characteristic values ​​for each of at least one structure of the compound; a third step of generating a first image by superimposing the acquired experimental characteristic values ​​on positions on the map corresponding to the structure corresponding to the experimental characteristic values, and outputting the first image to a display unit; a fourth step of acquiring compound information data corresponding to the experimental characteristic value selected on the map; and a fifth step of generating a second image including an image showing the compound information data and the first image, and outputting the second image to a display unit.

[0056] As a result, the characteristic experimental value is displayed at a position on the map corresponding to the composition of the compound corresponding to the characteristic experimental value, and therefore, by looking at the map, the user can easily understand the characteristic experimental value and the composition of the compound corresponding to the characteristic experimental value.

[0057] Furthermore, in a characteristic display method according to one embodiment of the present disclosure, when a characteristic experimental value superimposed on the map is selected by a user's input operation, a second image is generated and displayed. That is, a compound data image showing compound information data related to the selected characteristic experimental value is displayed on one screen together with the first image. As a result, by performing a simple input operation, the user can display an image showing compound information data related to the characteristic experimental value of interest. The user can easily visually recognize and understand the compound information data shown in the second image and the selected characteristic experimental value shown in the first image without switching screens.

[0058] On the other hand, in Patent Document 1 and Non-Patent Document 1, experimental or predicted values ​​of material properties are displayed for a composition formula or a search range of process conditions, but compound information data linked to the experimental values ​​cannot be displayed. Therefore, a property display method according to one embodiment of the present disclosure can display the compound information data. In other words, the compound information data can be tracked and displayed as information on the above-mentioned experimental data, allowing for unified management of the property experimental values ​​and compound information data. As a result, it is possible to appropriately support users in material development.

[0059] The map obtained in the first step indicates the predicted property values ​​of each of the structures of the compound at positions corresponding to the structures, and the property display method may further include a sixth step of obtaining compound identification information corresponding to the experimental property value selected on the map, wherein the fourth step obtains the compound information data that is associated with the compound identification information and related to the selected experimental property value, and the fifth step may generate the second image including an image showing the compound information data and the first image, and output the second image to a display unit.

[0060] As a result, the predicted property values ​​of each compound component are also displayed at the position on the map corresponding to that component. Since the experimental property values ​​are superimposed on the map, the user can easily compare the predicted property values ​​with the experimental property values ​​for the same compound component.

[0061] The characteristic display method may further include a seventh step of acquiring predicted property values ​​for each component of the compound, and in the first step, the map may be acquired by generating the map using the acquired predicted property values.

[0062] As a result, even if a map showing the predicted characteristic values ​​cannot be obtained, the computer itself can obtain the predicted characteristic values ​​and generate a map showing the predicted characteristic values, thereby eliminating the need to prepare a map showing the predicted characteristic values ​​in advance.

[0063] Hereinafter, embodiments of the present disclosure will be described with reference to the drawings. Each of the embodiments described below represents a specific example of the present disclosure. Therefore, the numerical values, shapes, materials, components, arrangement positions and connection forms of the components shown in the following embodiments are merely examples and are not intended to limit the present disclosure. Therefore, among the components in the following embodiments, components that are not described in the independent claims that represent the highest concept will be described as optional components.

[0064] It should be noted that the drawings are schematic diagrams and are not necessarily strict illustrations. In the drawings, the same reference numerals are used to designate substantially the same components, and redundant explanations will be omitted or simplified.

[0065] (Embodiment 1-1) A characteristic display method according to embodiment 1-1 will be described with reference to Figures 1 to 11. Note that embodiment 1 includes embodiment 1-1 and embodiment 1-2.

[0066] The configuration of embodiment 1-1 will be described below. A display system 1000 according to embodiment 1 of the present disclosure will be described in detail below with reference to the drawings.

[0067] 1 is a block diagram showing the configuration of a display system 1000 according to embodiment 1-1 of the present disclosure. The display system 1000 shown in FIG. 1 includes a characteristic display device 100, an input unit 1, a predictor database (DB) 2, an experiment database (DB) 3, a selection receiving unit 4, a compound information database (DB) 5, and a display unit 6.

[0068] The property display device 100 includes a property value acquisition unit 101, an image generation unit 102, a reduced image generation unit 103, a data acquisition unit 104, a data image generation unit 105, and an image layout determination unit 106. The property display device 100 may be configured with a processor such as a central processing unit (CPU) and a memory. In this case, the processor functions as the property display device 100 by executing a computer program stored in the memory, for example. In FIG. 1, the predictor database 2, the experiment database 3, and the compound information database 5 are configured with, for example, non-volatile memory.

[0069] Each component shown in FIG. 1 will be described in detail below.

[0070] [Input section 1] The input unit 1 acquires a search range to be displayed based on a user input, and outputs the acquired range to the characteristic value acquisition unit 101. The input unit 1 may be configured as, for example, a keyboard, a touch sensor, a touch pad, or a mouse.

[0071] FIG. 2 is a diagram showing an example of a search range to be displayed, input by a user. Data points included in the search range are expressed by a pre-specified value (for example, elements Li and O, and 3, which is the coefficient of element O) and range variables, and when values ​​or elements are specified for each of all range variables, one composition formula (or chemical formula) is shown. In other words, when values ​​or elements are specified for each of all range variables, the data expression displayed using those range variables shows one composition of a compound, i.e., one composition formula (or chemical formula). The data expression is, for example, Li shown in FIG. 2-3a-4b (M3 1-x M3' x ) a (M4 1-y M4' y ) 1+b O3. Note that range variables are also simply called variables. Compounds are synonymous with materials.

[0072] Here, range variables include categorical variables, discrete variables, continuous variables, etc. For example, categorical variables include element variable names M3, M3', M4, M4', element options La, Al, Ga, In selected for each of the variable names M3 and M3', and six combinations (4C2=6) of four elements that can be selected for the variable names M3 and M3', and element options Ti, Zr, Hf selected for each of the variable names M4 and M4', and three combinations (3C2=3) of three elements that can be selected for the variable names M4 and M4'.

[0073] For discrete variables, there are variable names a and b that set the coefficients of each element, coefficients 0.0, 0.05, 0.1, 0.15, 0.2 selected for variable name a, five combinations (5C1=5) of five options that can be selected for variable name a, coefficients 0.0, 0.1, 0.2, 0.3 selected for variable name b, and four combinations (4C1=4) of four options that can be selected for variable name b.

[0074] In continuous variables, there are variable names x and y that set the coefficients of each element. When variable names x and y vary from a minimum value of 0.0 to a maximum value of 1.0 with a step width of 0.1, there are 11 possible combinations ( 11 C1 = 11), where continuous variables may be expressed in the form of a list of all possible choices when varying from a minimum value of 0.0 to a maximum value of 1.0 with a step size of 0.1.

[0075] By changing the values ​​or elements specified for these range variables and generating data, various data are generated, which represent the search range of the display target. Note that the variables may also include process conditions (such as firing temperature and time, type of synthesis method, etc.).

[0076] That is, the multiple variables in this embodiment are categorical variables M3, M3', M4, and M4', discrete variables a and b, and continuous variables x and y. For the categorical variables M3 and M3', La, Al, Ga, and In are option data indicating elements that can be taken by M3 and M3', respectively. For the categorical variables M4 and M4', Ti, Zr, and Hf are option data indicating elements that can be taken by M4 and M4', respectively. For the discrete variable a, 0.0, 0.05, 0.1, 0.15, and 0.2 are option data indicating values ​​that a can take. For the discrete variable b, 0.0, 0.1, 0.2, and 0.3 are option data indicating values ​​that b can take. For a continuous variable x, 0.0, 0.1, 0.2, 0.3, . . ., 0.9, and 1.0 are option data indicating the possible values ​​for x. For a continuous variable y, 0.0, 0.1, 0.2, 0.3, . . ., 0.9, and 1.0 are option data indicating the possible values ​​for y. In other words, the variables M3, M3', M4, M4', a, b, x, and y are expressed as follows using option data:

[0077] M3∈{La,Al,Ga,In} M3'∈{La,Al,Ga,In} M4∈{Ti,Zr,Hf} M4'∈{Ti,Zr,Hf} a∈{0.0, 0.05, 0.1, 0.15, 0.2} b∈{0.0, 0.1, 0.2, 0.3} x∈{0.0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0} y∈{0.0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0}

[0078] Then, by selecting one option data from multiple option data corresponding to each of M3, M3', M4, M4', a, b, x, and y, a combination of option data is obtained. The combinations are (M3, M3', M4, M4', a, b, x, y) = (La, Al, Ti, Zr, 0.1, 0.1, 0.1, 0.1), (La, Al, Ti, Zr, 0.1, 0.1, 0.1, 0.2), (La, Al, Ti, Zr, 0.1, 0.1, 0.1, 0.3), etc. In other words, there are many combinations. The compositional formula (or chemical formula) of the compound is uniquely expressed by such combinations of option data. The above-mentioned search range includes all of these combinations, and the above-mentioned data points can be said to represent one combination or compound structure.

[0079] [Predictor Database (DB) 2] The predictor database 2 stores and manages the composition formulas (or chemical formulas) of multiple materials and at least one predictor for predicting the material's property values. The predictor database 2 is composed of, for example, a non-volatile memory. The predictor is a program based on a predetermined calculation algorithm, including machine learning, and outputs predicted property values ​​(hereinafter referred to as "property predicted values") for materials having each configuration within a search range determined by input variables and option data. The predictor database 2 may be configured to output various property predicted values, such as electrochemical property values, thermochemical property values, and X-ray diffraction patterns, using a single predictor. Alternatively, the predictor database 2 may store and manage multiple predictors, and different predictors may be used depending on the property to be predicted. For example, predictor A predicts electrochemical property values, predictor B predicts thermochemical property values, and predictor C predicts X-ray diffraction patterns. This reduces the complexity of the predictor's calculation algorithm and improves the prediction accuracy and processing speed for each property. Note that this is just one example, and the predictor may be of any format as long as it can output the desired property predicted value for the material configuration corresponding to the input variables and option data.

[0080] The predictor may be a computing device that performs calculations according to a computational model that mimics a biological neural network. The computing device may have an input layer, multiple hidden layers, and an output layer. The input layer may include multiple units Ui1, Ui2, ..., each of the multiple hidden layers may include multiple units, and the output layer may include multiple units Uo1, Uo2, ....

[0081] When input data X = [x1, x2,...] is input to the input layer, each unit in the intermediate layer and output layer performs calculations using multiple weight values, and output data Y = [y1, y2,...] is output from the output layer.

[0082] The multiple units Ui1, Ui2,... included in the input layer have a one-to-one correspondence with the input data x1, x2,... The multiple units Uo1, Uo2,... included in the output layer have a one-to-one correspondence with the output data y1, y2,... The number of units included in the output layer can be just one.

[0083] The input data X = [x1, x2, ] to the predictor may be [element symbol substituted for M3, element symbol substituted for M3', element symbol substituted for M4, element symbol substituted for M4', numerical value substituted for a, numerical value substituted for b, numerical value substituted for x, numerical value substituted for y], and the output data Y = [y1, y2, ] of the predictor may be [a value of a predetermined property of a compound having a chemical formula specified by the input data]. The value of the predetermined property may be a band gap value. The input data may further include one or more pieces of information other than the element symbol substituted for M3, element symbol substituted for M3', element symbol substituted for M4, element symbol substituted for M4', numerical value substituted for a, numerical value substituted for b, numerical value substituted for x, and numerical value substituted for y. The one or more pieces of information further included in the input data are, for example, descriptors generated by quantifying the atomic radius and electronegativity of the constituent elements of the material or the crystal structure.

[0084] The predictor may use backpropagation to optimize each weight value.

[0085] The training data used in the backpropagation method may be generated using element symbols and multiple numerical values ​​based on the chemical formula of the compound to be generated, and values ​​of predetermined properties of the generated compound.

[0086] The chemical formula of the compound to be produced (which can also be called the target composition) is Li 1.7 Al 0.04 Ga 0.06 Ti 0.2 Zr 0.8 O3, if the measured band gap of the produced compound is eV, the training data is [X01,Y01]=[x1,x2,···,y1,y2,···]=[3,13,22,40, 0.1, 0.0, 0.6, 0.8,e] 3 is the atomic number of Li, 13 is the atomic number of Al, 22 is the atomic number of Ti, and 40 is the atomic number of Zr.

[0087] That is, the predictor in the predictor database 2 predicts the property values ​​corresponding to the structure of the compound represented by each of the above combinations. Such prediction of property values ​​can also be considered as searching for the property values ​​of the material.

[0088] [Experimental Database 3] The experimental database 3 stores and manages the composition formula (or chemical formula) of the material, compound identification information, and experimental values ​​of the properties to be searched for (hereinafter referred to as "experimental property values"). If the search target includes process conditions, the experimental database 3 also stores in advance experimental process information indicating the process conditions used in the experiment.

[0089] FIG. 3 shows an example of experimental data stored in the experimental database 3. The compound identification information is a so-called ID, and may be any information that can identify a compound, such as a name, a symbol, or a number. In FIG. 3, as an example, the chemical formula: Li 1.45 La 0.045 Ti 1.1 Al 0.005The material represented by O3 is registered as ID: 000001-00001-001, and the experimental characteristic value (exp.data) is shown to be 2.349. This chemical formula is defined as (M3, M3', M4, M4', a, b, x, y) = (La, Al, Ti, Zr, 0.05, 0.1, 0.1, 0). In Figure 3, ID: 000001-00001-001 is composed of three levels of numbers (000001, 00001, 001). For example, the IDs are registered so that the first level numbers "000001" to "000005" are assigned to five chemical formulas with different composition ranges for the elements Zr and Ti contained in the material. Assigning IDs that categorize the composition formulas (or chemical formulas) of materials in this way facilitates management of the experimental database 3 by multiple users. The type of experimental characteristic value in Figure 3 is selected arbitrarily depending on the material. For example, for battery materials, the experimental characteristic value is conductivity, and for thermoelectric conversion materials, the experimental characteristic value is a thermoelectric conversion figure of merit.

[0090] [Characteristic value acquisition unit 101] The characteristic value acquisition unit 101 acquires characteristic predicted values ​​corresponding to the search range acquired from the input unit 1 from the predictor database 2, acquires characteristic experimental values ​​from the experimental database 3, and outputs these characteristic predicted values ​​and characteristic experimental values ​​to the image generation unit 102.

[0091] [Image generation unit 102] The image generation unit 102 generates a first image by superimposing the experimental characteristic value on the predicted characteristic value acquired from the characteristic value acquisition unit 101, and outputs the first image to the image layout determination unit 106. When the first image is reduced, the image generation unit 102 also outputs the first image to the reduced image generation unit 103.

[0092] 4A, 4B, 5A, and 5B are examples of first images to be generated. Note that FIG. 4C shows legends for the first images in FIGS. 4B and 5B. That is, [A] to [R] in FIGS. 4B and 5B are chemical formulas associated with A to R shown in FIG. 4C. The legends for each image map shown in FIGS. 4B and 5B, which will be described later, are also as shown in FIG. 4C.

[0093] The chemical formula corresponding to A shown in Fig. 4C may be written in the position A in Fig. 4B, the position A in Fig. 5B, ..., and the chemical formula corresponding to R shown in Fig. 4C may be written in the position R in Fig. 4B and the position R in Fig. 5B. If chemical formulas were written in the position A in Fig. 4B, the position A in Fig. 5B, ..., the position R in Fig. 4B, and the position R in Fig. 5B, the chemical formulas would be too small to be recognized, so they are written as shown in Fig. 4B, Fig. 5B, and Fig. 4C.

[0094] In Figures 4A and 4B, an image map is shown for each combination of choice data for each of the four categorical variables (M3, M3', M4, M4'). Hereinafter, this combination is also referred to as a combination of categorical variables. A collection of image maps corresponding to each combination of all categorical variables is also referred to as a map. An image map is made up of an array (or matrix) of multiple image element maps. Continuous variables x and y are assigned to two axes of the image element map, and discrete variables a and b are assigned to two axes of the array of image element maps (i.e., the image map). The variable assignment method is not limited to this. The characteristic prediction value may be displayed directly for all the obtained combinations of choice data, or may be interpolated and displayed for combinations that have not been obtained (i.e., data points that have not been obtained). Such characteristic prediction values ​​are indicated by color or a shade of color.

[0095] Here, FIG. 4A shows image maps corresponding to two combinations of categorical variables, and FIG. 4B shows image maps corresponding to 18 combinations of categorical variables. The number of image element maps included in the map of the present disclosure is determined by the number of combinations of choice data for each of the two variables assigned to the two axes of the image element map array and the number of combinations of choice data for each of the four categorical variables (i.e., elements). In other words, the wider the composition range of the materials stored and managed in the predictor database 2 and the more element combinations in the material composition, the greater the number of image element maps. Therefore, it is important for users to devise a display that is visually easy to understand.

[0096] As an example of the display, if the user selects the chemical formula Li in Figure 2, 2-3a-4b (M3 1ーx M3' x ) a (M4 1ーy M4' y ) 1+b A search for predicted property values ​​for a composition represented by O3 will be described with reference to FIG. 4A. First, the image generator 102 assigns continuous variables x and y to two axes of an image element map. The continuous variables x and y are variables with option data that vary from a minimum value of 0.0 to a maximum value of 1.0 in steps of 0.1, with 11 possible combinations of x and y. Therefore, although the image element map of the present disclosure is square, the shape of the image element map depends on how the axes of the image element map are assigned, and may be triangular or other shapes. At positions indicated by the values ​​of the variables x and y on such an image element map, the predicted property values ​​of the compound defined by those values ​​are indicated by color or color shading. An example of a predicted property value in the present disclosure is band gap, but is not limited to this.

[0097] The image generation unit 102 then assigns discrete variables a and b to two axes of the array of the image element map. The discrete variable a has five option data values: 0.0, 0.05, 0.1, 0.15, and 0.2, and the discrete variable b has four option data values: 0.0, 0.1, 0.2, and 0.3. Therefore, there are 5 × 4 = 20 image element maps for each combination of categorical variables in FIG. 4A. In the above example, to facilitate visual understanding for the user, when comparing the continuous variables x and y with the discrete variables a and b, the continuous variables x and y, which have a larger number of combinations, are assigned to the axes of the image element map, and the discrete variables a and b, which have a smaller number of combinations, are assigned to the axes of the array of the image element map. The assignment may be arbitrary by the user, or may be automatically assigned by calculating the number of combinations.

[0098] Furthermore, for categorical variables, there are six combinations of choice data for elements La, Al, Ga, and In selected for variable names M3 and M3', respectively, and three combinations of choice data for elements Ti, Zr, and Hf selected for variable names M4 and M4', respectively. As a result, in Figure 4B, image maps consisting of 20 image element maps corresponding to the combinations of categorical variables are displayed in six rows and three columns, for a total of 18 combinations (20 image element maps x 18 combinations = 360 in total). The above-mentioned map is composed of these 18 image maps. Note that Figure 4A displays two of the 18 image maps.

[0099] As shown in FIGS. 4A and 4B , the image generating unit 102 displays the experimental characteristic values ​​as marks such as circles. For example, the color or color shading of the marks such as circles indicates the experimental characteristic values. That is, the image generating unit 102 superimposes a mark having a color or color shading corresponding to the experimental characteristic value of the compound at a position on the map corresponding to the combination described above that indicates the composition of the compound generated through the experiment. Note that the superimposition of the mark is also referred to as superimposition of the experimental characteristic value. An example of the experimental characteristic value in the present disclosure is the band gap of the compound obtained through the experiment, but this is not limited thereto. The experimental characteristic value may also be data described in a document or book. In the map of this embodiment, the relationship between the value and the color or color shading may be consistent for both the experimental characteristic value and the predicted characteristic value. This makes it possible to highlight the difference between the color or color shading of the mark for the experimental characteristic value and the surrounding color or color shading when the experimental characteristic value deviates from the predicted characteristic value. As a result, it is easy to visually understand that the experimental characteristic value deviates from the predicted characteristic value. In other words, if the color or color shading of the mark of the characteristic experimental value is visually the same as the color or color shading of the characteristic predicted value, it can be visually and immediately understood that the characteristic experimental value and the characteristic predicted value are approximately the same.

[0100] The display method of the characteristic experimental values ​​may also be devised to make them visually easy for the user to understand. For example, as shown in FIGS. 5A and 5B, the image generating unit 102 may highlight newly added characteristic experimental values, characteristic experimental values ​​whose characteristics meet a predetermined standard, characteristic experimental values ​​selected by the user, etc. As an example, the image generating unit 102 may highlight the characteristic experimental values ​​by outlining the dots, decorating the dots with a lustrous hue, using diamond marks, stars, etc. In the case where the experimental database 3 is shared, it is expected that tens to hundreds of new experimental data will be added per day. Therefore, highlighting the newly added characteristic experimental values ​​allows the user to grasp the daily progress of the experiment.

[0101] The characteristic experimental value where the characteristic satisfies a predetermined standard, the characteristic experimental value selected by the user, etc. may be specified by the user or may be specified in advance.

[0102] These highlighting methods make it easier for users to visually understand, especially when there are more than 100 image element maps as in Figure 5B, by highlighting the characteristic experimental values ​​(i.e., marks indicating characteristic experimental values) scattered on the image element maps based on a certain rule. As further improvements to the visually easier to understand display method, an image displaying a data representation above the image map, an image displaying a color bar, an image displaying the values ​​of a range variable, etc. may be adopted.

[0103] In this way, the first image in the present disclosure is a map made up of the above-mentioned multiple image maps, and is a map on which marks such as dots, stars, or circles indicating characteristic experimental values ​​are superimposed.

[0104] [Reduced image generation unit 103] The reduced image generating unit 103 generates a reduced image of the first image generated by the image generating unit 102 (hereinafter referred to as a reduced first image), and outputs it to the image layout determining unit 106.

[0105] [Image layout determination unit 106] The image layout determination unit 106 acquires a first image from the image generation unit 102, determines the layout, and outputs it to the display unit 6. When a compound data image is acquired from the data image generation unit 105 described below, the image layout determination unit 106 determines the layout to fit the acquired first image and the compound data image on one screen, generates a second image, and outputs it to the display unit 6. When the first image is reduced, the image layout determination unit 106 acquires a reduced first image from the reduced image generation unit 103, determines the layout to fit the acquired reduced first image and the compound data image on one screen, generates a second image, and outputs it to the display unit 6.

[0106] [Display section 6] The display unit 6 is, for example, but not limited to, a liquid crystal display, a plasma display, an organic EL (Electro-Luminescence) display, etc. The display unit 6 displays the results obtained from the image layout determination unit 106 (i.e., the first image or the second image, etc.).

[0107] The display unit 6 is, for example, a display unit provided in an information terminal, an information terminal equipped with an electronic experiment notebook, etc. It may also be a display unit provided in a synthesis apparatus used to synthesize raw materials or compounds, a reaction apparatus used to react raw materials or compounds, an analysis apparatus used to analyze compounds, an evaluation apparatus used to evaluate compounds, etc.

[0108] Hereinafter, details of each component used in the operation after the first image is displayed on the display unit 6 and the characteristic experimental value included in the first image is selected by the user will be described.

[0109] [Selection Reception Section 4] When a user selects a characteristic experimental value included in the first image displayed on the screen of the display unit 6, the selection receiving unit 4 receives the user's input and outputs it to the data acquiring unit 104. The user's input may be performed by clicking on an experimental point, which is a mark indicating the characteristic experimental value, or by dragging the map to select it.

[0110] That is, the selection receiving unit 4 selects, for example, one characteristic experimental value from one or more characteristic experimental values ​​included in the first image in response to an input operation by the user, and outputs the selected characteristic experimental value to the data acquiring unit 104. Note that the selection receiving unit 4 may be configured as, for example, a keyboard, a touch sensor, a touch pad, or a mouse.

[0111] [Data Acquisition Unit 104] The data acquisition unit 104 acquires, from the experiment database 3, compound identification information assigned to the characteristic experimental value selected by the user in the selection receiving unit 4. Furthermore, the data acquisition unit 104 acquires, from the compound information database 5, compound information data including at least one of compound configuration data and compound graph data corresponding to the compound identification information, and outputs the data to the data image generation unit 105. The expression "at least one of A and B" in the specification, claims, abstract, and drawings of the present application may also mean "A, B, or both A and B."

[0112] [Compound Information Database 5] The compound information database 5 stores and manages compound information data corresponding to the characteristic experimental values ​​managed in the experiment database 3.

[0113] FIG. 6 shows an example of compound information data. The compound information data shown in FIG. 6 includes compound composition data and compound graph data. The compound composition data includes compound identification information (also called sample ID or compound ID) and compound-related information. The related information is so-called metadata, and includes, for example, the name of the researcher / experimenter, the date of data registration, composition information, process conditions, and characteristic value information. The composition information indicates the target composition. The target composition is also called the target composition. The process conditions include the actual composition, raw materials, experimental procedures, firing conditions, the shape and size of the crucible, the type of equipment, and the serial number of the equipment. The characteristic value information includes impedance, oxidation potential, crystalline phase, and the like. In other words, the compound composition data indicates that a compound with the actual composition was produced as a result of producing the compound according to the process conditions to produce a compound with the target composition, and indicates characteristic value information obtained through experiments on the produced compound. The actual composition is evaluated using, for example, energy dispersive X-ray spectroscopy, X-ray photoelectron spectroscopy, inductively coupled plasma optical emission spectroscopy, or the like. The experimental and predicted characteristic values ​​shown on the maps in FIGS. 4A, 4B, 5A, and 5B are associated with the target compositions, but may also be associated with the actual compositions.

[0114] The experimental characteristic values ​​shown in the experimental data in the experimental database 3 are, for example, values ​​derived or calculated based on the characteristic value information of the compound information data. As a specific example, the experimental characteristic value is a band gap derived using the impedance, oxidation potential, etc. of the characteristic value information.

[0115] The compound composition data shown in Figure 6 is an example, and this compound composition data only needs to include at least compound identification information (sample ID) and target composition. Furthermore, if the compound composition data includes the name of the researcher / experimenter and the registration date, the reliability of the experimental data will be improved. If the compound composition data includes process conditions and property value information, it is expected that more specific material properties will be discovered and the value of the experimental data will be improved.

[0116] The compound graph data includes compound identification information and compound measurement data. The measurement data may include, for example, impedance obtained by electrochemical impedance spectroscopy (PEIS), a voltammogram obtained by cyclic voltammetry (CV), or a diffraction pattern obtained by X-ray diffraction (XRD). X-ray diffraction (XRD) will be described later (see the descriptions of Figures 33 and 34). The measurement data may indicate multiple characteristics, not just one type of characteristic. In this disclosure, PEIS, CV, and XRD are referred to as Characteristic 1, Characteristic 2, and Characteristic 3, respectively. The compound composition data may also include experimental characteristic values, such as band gaps, derived from impedance, etc.

[0117] [Data image generation unit 105] The data image generating unit 105 generates a compound data image based on the compound information data acquired from the data acquiring unit 104, and outputs the generated image to the image layout determining unit 106. The compound data image shows the compound information data as an image.

[0118] 7 shows an example of a generated compound data image, which includes a compound structure data image and a compound graph data image corresponding to the characteristic experimental values ​​selected by the user.

[0119] The impedance, oxidation potential, and crystalline phase included in the compound configuration data may be information manually read from the compound graph data. For example, an experimenter may read the value on the horizontal axis corresponding to the value 0 on the vertical axis from the graph of characteristic 1 (i.e., PEIS) in the compound graph data as the impedance and register the impedance in the compound configuration data. An experimenter may read the potential on the horizontal axis where the current value on the vertical axis rises from 0 from the graph of characteristic 2 (i.e., CV) in the compound graph data as the oxidation potential and register the oxidation potential in the compound configuration data. An experimenter may read the crystalline phase of the compound from the pattern of the graph of characteristic 3 (i.e., XRD) in the compound graph data and register the crystalline phase in the compound configuration data.

[0120] 8 and 9 are examples of the display results of the second image. In FIGS. 8 and 9, the first image (or reduced first image) is arranged on top and the compound data image is arranged on the bottom, but the arrangement is arbitrary as long as it fits on one screen. As shown in FIG. 9, the selected experimental characteristic value may be highlighted on the first image or reduced first image. As an example of highlighting, in FIG. 9, the experimental characteristic value is displayed as a star. By highlighting, it becomes easier for an unspecified number of users to visually understand the correspondence between the displayed compound information data and the selected experimental characteristic value. In other words, even when a user other than the user who selected the experimental characteristic value views the displayed second image, they can understand at a glance which experimental characteristic value the compound information data displayed in the second image is based on.

[0121] 10A, 10B, 10C, and 11 are examples of images showing the transition of the display results on the display unit 6. FIG.

[0122] In Figures 10A, 10B, and 10C, a first image is first displayed, and after the user selects a characteristic experimental value, a second image is displayed, which combines a reduced first image obtained by reducing the first image with a compound data image on a single screen. In this way, when the characteristic experimental value of the first image is selected, a second image including the compound data image is dynamically displayed. As shown in Figures 10A and 10B, reducing the first image after the user's selection is expected to improve user operability when selecting a characteristic experimental value. In particular, when the first image is the example shown in Figure 4B, i.e., when the image element map exceeds 100, it becomes easier to discuss the selection of a characteristic experimental value while checking the surrounding image maps, as shown in Figure 10B.

[0123] On the other hand, if the first image is like the example shown in Figure 4B, it is expected that the reduced first image obtained by reducing the entire first image will be difficult for the user to see. Therefore, as shown in Figure 10C, the periphery of the image map of the categorical variable containing the characteristic experimental value selected by the user may be selected and reduced. In other words, a portion of the map is selected and reduced. In this case, the second image is easier to visually understand when displayed compared to Figure 10B.

[0124] In FIG. 11, a first image is initially displayed in the upper portion of the screen displayed on the display unit 6 (i.e., the first region), and after the user selects a characteristic experimental value, a compound data image is displayed in the lower portion of the screen (i.e., the second region), thereby displaying a second image. In other words, if the first image does not occupy the entire screen of the display unit 6 from the beginning, the first image may be displayed as is without being reduced. The display size of the first image may be determined based on the screen size of the display unit 6 or the number of image maps of the first image. In FIG. 11, the process of reducing the first image can be omitted, which is expected to improve processing speed. It is also expected that the change in the display result on the display unit 6 between the user selecting a characteristic experimental value and the displaying of the second image is minimized, making it easier for the user to dynamically understand.

[0125] <Summary of Embodiment 1-1> As described above, the characteristic display method according to one aspect of embodiment 1-1 includes the following processes of steps 1 to 7. In step 1, the first acquisition unit acquires a plurality of variables that determine the structure of a compound and, for each of the plurality of variables, a plurality of option data items that indicate possible values ​​or elements for the variable. The first acquisition unit is, for example, a component included in the characteristic value acquisition unit 101 of the characteristic display device 100, and acquires the above-mentioned plurality of variables and the plurality of option data items from the input unit 1 as a search range.

[0126] In the second step, the second acquisition unit determines the compound configuration by selecting one option data from the plurality of option data for each of the plurality of variables, and acquires the predicted property value of the compound corresponding to the determined compound configuration. The second acquisition unit is, for example, a component included in the characteristic value acquisition unit 101 of the characteristic display device 100, and inputs the combination into the predictor database 2 and acquires the output result from the predictor database 2 to acquire the predicted property value.

[0127] In the third step, the third acquisition unit acquires experimental characteristic values ​​of the compound corresponding to the determined compound configuration. The third acquisition unit acquires, for example, experimental characteristic values ​​of components included in the characteristic value acquisition unit 101 of the characteristic display device 100, which are stored in association with the combination of the components, from the experimental database 3.

[0128] In a fourth step, the first image processing unit generates a map showing predicted property values ​​of the compound corresponding to the determined compound structure at a position corresponding to the compound structure, and generates a first image by superimposing the acquired experimental property values ​​on the map at a position corresponding to the compound structure corresponding to the experimental property values, and outputs the first image to the display unit 6. The first image processing unit is, for example, a component including the image generation unit 102 and the image layout determination unit 106 of the property display device 100.

[0129] In the fifth step, a fourth acquisition unit acquires compound identifying information corresponding to the experimental characteristic value selected on the map. The fourth acquisition unit is, for example, a component included in the data acquisition unit 104 of the characteristic display device 100, and acquires compound identifying information stored in association with the experimental characteristic value from the experimental database 3.

[0130] In the sixth step, the fifth acquisition unit acquires compound information data associated with the compound identification information and related to the selected characteristic experimental value. The fifth acquisition unit is a component included in the data acquisition unit 104 of the characteristic display device 100, and acquires the compound information data stored in association with the compound identification information from the compound information database 5.

[0131] In a seventh step, the second image processing unit generates a second image including a compound data image showing the compound information data and the first image, and outputs the second image to the display unit 6. The second image processing unit is a component including, for example, the data image generating unit 105 and the image layout determining unit 106 of the characteristic display device 100.

[0132] This generates and displays a first image in which the experimental property values ​​are superimposed on a map showing predicted property values ​​for each combination of option data, i.e., predicted property values ​​for each configuration of the compound. Therefore, for example, if a user of the property display device 100 inputs many variables and many option data items to be assigned to each of the many variables into the property display device 100 using the input unit 1, a map showing a vast amount of information can be displayed. That is, the map shows the predicted property values ​​for each of the many possible configurations (i.e., compositional formulas or chemical formulas) of the compound, in association with the configuration, and also shows the experimental property values ​​for at least one configuration, in association with the configuration. Therefore, by viewing the map, the user can easily grasp the predicted property values ​​and experimental property values ​​for all configurations of the compound.

[0133] Furthermore, in the property display method according to one aspect of the present embodiment, when an experimental property value superimposed on the map is selected by a user's input operation, a second image is generated and displayed. That is, a compound data image showing compound information data related to the selected experimental property value is displayed together with the first image on a single screen. As a result, the user can display a compound data image showing compound information data related to the experimental property value of interest by performing a simple input operation. The user can easily visually recognize and understand the compound information data shown in the compound data image, the predicted property values ​​of each component shown in the first image, and the selected experimental property value without switching screens. Therefore, the property display method according to one aspect of the present embodiment can track and display the compound information data, thereby enabling unified management of the experimental property values, predicted property values, and compound information data. As a result, the user's material development can be appropriately supported.

[0134] The characteristic display method according to one aspect of the present embodiment may further include a step in which the reduced image generation unit 103 generates a reduced first image by reducing the first image before generating the second image in the seventh step. In this case, in the seventh step, the image layout determination unit 106 generates a second image including the compound data image and the reduced first image, and outputs the second image to the display unit 6.

[0135] Therefore, before the second image is generated, the first image is reduced in size to generate the second image, and therefore in the fourth step, the first image can be displayed enlarged on the screen, thereby improving the visibility of the first image.

[0136] The screen on which the first image and the second image are displayed in the fourth step and the seventh step may consist of a first area and a second area, and the image layout determination unit 106 may output the first image laid out in the first area to the display unit in the fourth step, and may generate the second image by laying out the first image in the first area and laying out the compound data image in the second area in the seventh step, and output it to the display unit 6.

[0137] As a result, the layout and size of the first image displayed in the fourth step are the same as those in the seventh step, which reduces the processing load on the first image and improves the processing speed. Furthermore, even when the displayed image is switched from the first image to the second image, the layout and size of the first image do not change, which reduces the sense of discomfort felt by the user when the image is switched.

[0138] The compound information data includes compound configuration data and compound graph data related to experimental property values.

[0139] Therefore, since images showing these data are displayed as compound data images, the user can easily grasp various information about the compound related to the selected experimental characteristic value from multiple perspectives. When the compound constitution data and compound graph data are data used to derive the experimental characteristic value, the user can easily grasp the raw data of the experimental characteristic value.

[0140] A characteristic display method according to another aspect of embodiment 1-1 includes step 1 of acquiring a map showing positions corresponding to each structure of a compound, step 2 of acquiring experimental characteristic values ​​for at least one structure of the compound, step 3 of generating a first image by superimposing the acquired experimental characteristic values ​​on positions on the map corresponding to the structure, and outputting the first image to a display unit, step 4 of acquiring compound information data corresponding to a selected experimental characteristic value on the map, and step 5 of generating a second image including an image showing the compound information data and the first image and outputting the second image to a display unit. In other words, the characteristic display method does not require the acquisition of predicted characteristic values ​​and the generation of a map showing the predicted characteristic values. It is sufficient if a so-called empty map is acquired.

[0141] As a result, the characteristic experimental value is displayed at a position on the map corresponding to the composition of the compound corresponding to the characteristic experimental value, and therefore, by looking at the map, the user can easily understand the characteristic experimental value and the composition of the compound corresponding to the characteristic experimental value.

[0142] Furthermore, in the property display method according to another aspect of the present embodiment, when a property experimental value superimposed on the map is selected by a user's input operation, a second image is generated and displayed. That is, a compound data image showing compound information data related to the selected property experimental value is displayed together with the first image on a single screen. As a result, the user can display an image showing compound information data related to the property experimental value of interest by performing a simple input operation. The user can easily visually recognize and understand the compound information data shown in the image and the selected property experimental value shown in the first image without switching screens. Furthermore, since the compound information data can be tracked and displayed, the property experimental value and compound information data can be managed in a unified manner. As a result, the user's material development can be appropriately supported.

[0143] The map acquired in step 1 may show the predicted property value of each component of the compound at a position corresponding to that component. In this case, the property display method according to another aspect of the present embodiment further includes step 6 of acquiring compound identifying information corresponding to an experimental property value selected on the map. Then, in step 4, compound information data associated with the compound identifying information and related to the selected experimental property value is acquired, and in step 5, a second image including an image showing the compound information data and the first image is generated and output to the display unit. In other words, the property display method does not need to acquire predicted property values, as long as it acquires a map showing the predicted property values.

[0144] As a result, the predicted property values ​​of each compound component are also displayed at the position on the map corresponding to that component. Since the experimental property values ​​are superimposed on the map, the user can easily compare the predicted property values ​​with the experimental property values ​​for the same compound component.

[0145] The characteristic display method according to another aspect of the present embodiment may further include step 7 of acquiring predicted property values ​​for each constituent of the compound. In this case, in step 1, the map is acquired by generating the map using the acquired predicted property values. In other words, the characteristic display method does not require the use of a calculation algorithm to calculate the predicted property values.

[0146] As a result, even if a map showing predicted characteristic values ​​cannot be obtained, the characteristic display device 100 can obtain the predicted characteristic values ​​and generate a map showing the predicted characteristic values, eliminating the need to prepare a map showing predicted characteristic values ​​in advance.Since no calculation algorithm is used, the processing load can be reduced.

[0147] (Embodiment 1-2) Embodiment 1-2 is for the case where multiple characteristic experimental values ​​are selected. The display system in this embodiment has the same configuration as the display system 1000 in embodiment 1-1, but performs processing different from that in embodiment 1-1 when multiple characteristic experimental values ​​are selected. The data image generation unit 105 will be described in detail below.

[0148] [Data image generation unit 105] The data image generating unit 105 generates an image based on the compound information data acquired and selected from the data acquiring unit 104. In this embodiment, since a plurality of compound information data are selected, the data image generating unit 105 generates an image in which the plurality of compound information data are superimposed.

[0149] 12 and 13 are examples of a compound data image in which multiple experimental characteristic values ​​are selected. As shown in FIG. 12, instead of a compound structure data image, the compound data image includes a list image showing compound identifying information corresponding to each selected experimental characteristic value. When a user selects compound identifying information in the list image, the compound structure data image corresponding to the selected compound identifying information is displayed in place of the list image. Such a list image, i.e., a list-format display of compound identifying information, is expected to be easier to visually understand the greater the number of selected experimental characteristic values.

[0150] The generation and display of the list image, as well as the switching between the displayed list image and the compound structure data image, are performed, for example, by the data image generation unit 105. When multiple experimental characteristic values ​​are selected, the data image generation unit 105 generates a graph superimposed image as a compound graph data image, which shows superimposed compound graph data corresponding to each of the selected experimental characteristic values, as shown in FIG. 12 . In other words, the graph superimposed image is a compound graph data image showing multiple compound graph data. When a user selects one compound identification information from multiple compound identification information included in the list image, the data image generation unit 105 may switch the displayed graph superimposed image to a compound graph data image showing the compound graph data corresponding to the selected compound identification information. In this way, when compound identification information is selected, the compound graph data corresponding to the selected compound identification information is displayed. Alternatively, when compound identification information is selected, the data image generation unit 105 may change the color of the compound graph data corresponding to the selected compound identification information among the multiple compound graph data shown in the graph superimposed image to a color different from that of the other compound graph data.

[0151] On the other hand, in FIG. 13 , the compound structure data is displayed in a switchable tab format. That is, the data image generation unit 105 generates a tab switching image instead of a compound structure data image. The tab switching image has multiple tabs for switching between and displaying multiple compound structure data. These multiple tabs are associated with multiple compound identification information, respectively, and the multiple compound identification information corresponds to multiple characteristic experimental values ​​selected by the user. Therefore, when a user selects one tab from the multiple tabs included in the tab switching image, the data image generation unit 105 switches the displayed tab switching image to a compound structure data image showing the compound structure data corresponding to the selected tab. Furthermore, similar to the example of FIG. 12 , the data image generation unit 105 may switch the graph superimposed image to a compound graph data image showing the compound graph data corresponding to the selected tab. Alternatively, similar to the example of FIG. 12 , when a tab is selected, the data image generation unit 105 may change the color of the compound graph data corresponding to the selected tab among the multiple compound graph data displayed in the graph superimposed image to a color different from that of the other compound graph data. Such a tab format is expected to be easier to visually understand the fewer the number of selected characteristic experimental values, so for example, the display format may be switched depending on the number of selected characteristic experimental values, such as switching to the tab format of Fig. 13 when five or fewer characteristic experimental values ​​are selected, and switching to the list format of Fig. 12 when six or more characteristic experimental values ​​are selected.

[0152] The method of displaying the compound structure data is not limited to the formats shown in FIGS. 12 and 13, and any format may be used as long as it is capable of displaying a plurality of pieces of information together.

[0153] 14 and 15 are examples of images showing the transition of the output result of the display unit 6 when multiple experimental property values ​​are selected. FIG. 14 is an example of an image showing the transition of the display result of the display unit 6 when the first image similar to FIGS. 10A to 10C is reduced and the compound data image of FIG. 12 is used. FIG. 15 is an example of an image showing the transition of the output result of the display unit 6 when multiple experimental property values ​​are selected without reducing the first image similar to FIG. 11. As shown in FIGS. 14 and 15, the reduced first image or the first image displayed in the second image highlights the selected experimental property values ​​with stars. The user can cancel the multiple selection by selecting the stars in the second image, for example, by clicking. For example, multiple experimental property values ​​are selected, and multiple compound information data corresponding to the selected experimental property values ​​are displayed superimposed. In such a case, the selection of an unnecessary experimental property value can be canceled by clicking, for example, as described above. This makes it possible to select and discard experimental property values ​​and organize compound information data after the second image is displayed.

[0154] <Summary of Embodiment 1-2> As described above, in the characteristic display method of embodiment 1-2, in the fifth step, a plurality of characteristic experimental values ​​superimposed on the map are selected, and the data acquisition unit 104 acquires compound identification information corresponding to each of the plurality of characteristic experimental values. Then, in the sixth step, the data acquisition unit 104 acquires compound information data corresponding to each of the plurality of compound identification information. In the seventh step, the data image generation unit 105 generates a compound data image by superimposing compound graph data for each of the plurality of acquired compound information data.

[0155] This allows a plurality of compound graph data relating to a plurality of characteristic experimental values ​​to be displayed in an overlapping manner, allowing the user to easily grasp the differences between the compound graph data.

[0156] The compound data image in the seventh step includes a graph superimposed image showing compound graph data of each of the acquired plurality of compound information data superimposed thereon, and a list image showing a list of compound identification information associated with each of the acquired plurality of compound information data.

[0157] This allows the user to easily grasp the compound identification information for each of the multiple compound graph data that are superimposed, since a list image is displayed. More specifically, when a large number of characteristic experimental values ​​are selected, the multiple compound identification information is displayed in a list, making it easy to view the compound identification information on one screen and allowing the user to select any of the compound identification information.

[0158] Alternatively, the compound data image in the seventh step includes a graph superimposition image showing compound graph data of each of the acquired plurality of compound information data superimposed thereon, and a tab switching image having a plurality of tabs for switching and displaying compound configuration data corresponding to each of the plurality of compound graph data.

[0159] This allows the user to easily view and understand the compound constitution data related to the selected experimental property values ​​by switching between tabs, even when multiple experimental property values ​​are selected. More specifically, when only a small number of experimental property values ​​are selected, multiple compound identification information can be easily viewed on one screen by switching between tabs, and furthermore, can be arbitrarily selected.

[0160] (Embodiment 1: Description of Operation) Next, the operation of the characteristic display device 100 will be described.

[0161] (flowchart) FIG. 16 is a flowchart showing the overall flow of processing by the characteristic display device 100 according to the embodiments 1-1 and 1-2 of the present disclosure.

[0162] (Step S101) The characteristic value acquisition unit 101 acquires predicted characteristic values ​​and experimental characteristic values ​​corresponding to the search range acquired from the input unit 1 from the predictor database 2 and the experimental database 3, respectively, and outputs them to the image generation unit .

[0163] (Step S102) The image generating unit 102 generates a first image by superimposing the experimental characteristic value on the predicted characteristic value acquired from the characteristic value acquiring unit 101, and outputs the first image to the image layout determining unit 106.

[0164] (Step S103) The image layout determination unit 106 acquires the first image from the image generation unit 102, determines the layout of the first image, and outputs it to the display unit 6. As a result, the first image is displayed on the display unit 6.

[0165] (Step S104) When a characteristic experimental value included in the first image displayed on the screen of the display unit 6 is selected by the user, the selection receiving unit 4 receives the user's input and outputs it to the data acquiring unit 104. In other words, the selection receiving unit 4 outputs the characteristic experimental value selected by the user to the data acquiring unit 104.

[0166] (Step S105) The data acquiring unit 104 acquires compound identifying information corresponding to the characteristic experimental value output from the selection receiving unit 4 from the experiment database 3, and further acquires compound information data corresponding to the compound identifying information from the compound information database 5. Then, the data acquiring unit 104 outputs the compound information data to the data image generating unit 105.

[0167] (Step S106) The data image generating unit 105 generates a compound data image based on the compound information data acquired from the data acquiring unit 104 and outputs it to the image layout determining unit 106 .

[0168] (Step S107) The image layout determination unit 106 determines, for example, based on the compound data image output from the data image generation unit 105, whether or not to reduce the size of the first image displayed on the display unit 6 in step S103.

[0169] Here, if the image layout determination unit 106 determines that the displayed first image should be reduced (YES in step S107), it causes the reduced image generation unit 103 to reduce the first image. At this time, the reduced image generation unit 103 acquires the first image from the image generation unit 102. On the other hand, if the image layout determination unit 106 determines that the first image should not be reduced (NO in step S107), it uses the first image displayed in step S103.

[0170] For example, the image layout determination unit 106 determines to reduce the first image when the first image is displayed full screen in step S103, or when the amount of compound information data is large and the compound data image is large even if the first image is not displayed full screen. That is, the image layout determination unit 106 makes its determination taking into account the respective sizes of the first image and the compound data image. The image layout determination unit 106 determines not to reduce the first image when the first image is not displayed full screen in step S103, and when the amount of compound information data is small and the compound data image is small.

[0171] (Step S108) The reduced image generating unit 103 reduces the first image acquired from the image generating unit 102 to generate a reduced first image, and outputs the reduced first image to the image layout determining unit 106 .

[0172] (Step S109) When the first image is not reduced, the image layout determination unit 106 determines a layout in which the unreduced first image and the compound data image acquired from the data image generation unit 105 are placed on one screen, generates a second image, and outputs it to the display unit 6. When the first image is reduced, the image layout determination unit 106 acquires a reduced first image from the reduced image generation unit 103, determines a layout in which the acquired reduced first image and the compound data image are placed on one screen, generates a second image, and outputs it to the display unit 6.

[0173] (Step S110) The display unit 6 displays the second image acquired from the image layout determination unit 106.

[0174] The first to third steps described above in embodiments 1-1 and 1-2 correspond to step S101 in Fig. 16. The fourth step corresponds to steps S102 and S103, the fifth and sixth steps correspond to steps S104 and S105, and the seventh step corresponds to steps S106 to S110.

[0175] <Modification of the First Embodiment> In this modification, numerical information regarding data representation is input to the predictor, and the predictor outputs a characteristic value. This modification may be combined with the contents disclosed in other embodiments to modify this modification so that numerical information regarding data representation and element symbols are input to the predictor, and the predictor outputs a characteristic value.

[0176] First, the characteristic display device 100 displays a first image including a first plurality of graphs on the display unit 6 (see FIG. 5A). The first plurality of graphs are the data representation Li shown in FIG. 2-3a-4b (Al 1-x Ga x ) a (Ti 1-y Zr y ) 1+b There may be 20 graphs arranged in 4 rows and 5 columns relating to O3. The display unit may be called a display.

[0177] Next, the user selects first data included in a first graph included in the first plurality of graphs (see FIG. 10A). That is, when the property display device 100 detects the selection of the first data, the property display device 100 causes the display unit 6 to display a second image including a reduced first image, which is an image obtained by reducing the first screen, and a compound data image, which is an image including information related to the first data (see FIG. 10A).

[0178] FIG. 10A shows an example of how the first data is selected. The first graph is the data representation Li shown in FIG. 2-3a-4b (Al 1-x Ga x ) a (Ti 1-y Zr y ) 1+b The graphs are arranged in the first row and third column of 20 graphs arranged in four rows and five columns for O3. The first graph is for the chemical formula Li 1.7 (Al 1-x Ga x ) 0.1 (Ti 1-y Zr y )1O3, the band gap value when (x, y) = (0.0, 0.0), ~, and the band gap value when (x, y) = (1.0, 1.0) are shown (see Figure 4A).

[0179] The first plurality of graphs is a graph 11 ,~,Graph pq ,~,Graph rs (1≦p≦r, 1≦q≦s, where p is a natural number, q is a natural number, r is a natural number of 2 or more, and s is a natural number of 2 or more).

[0180] Multiple values ​​e pq(1,1) ~e pq(n,m) is displayed on graph pq (where n is a natural number greater than or equal to 2, m is a natural number greater than or equal to 2, i is a natural number, and j is a natural number).

[0181] As an example of a first plurality of graphs, the data representation Li 2-3a-4b (Al 1-x Ga x ) a (Ti1-y Zr y ) 1+b 20 graphs arranged in 4 rows and 5 columns on O3 11 ~Graph 45 These 20 graphs are shown in Figure 10A and Figure 5A (r=4, s=5). 11 ~Graph 45 The relationship is shown below.

[0182] [Table 1]

[0183] graph 11 is multiple values ​​e 11(1,1) ~e 11(n,m) Display, ~, graph pq is multiple values ​​e pq(1,1) ~e pq(n,m) Display, ~, graph rs multiple values ​​e rs(1,1) ~e rs(n,m) The graph below shows 13 Multiple values ​​displayed by e 13(1,1) ~e 13(n,m) Explain.

[0184] graph 13 is the data representation Li shown in FIG. 10A. 2-3a-4b (Al 1-x Ga x ) a (Ti 1-y Zr y ) 1+b This graph is located in the 1st row and 3rd column of the 20 graphs arranged in 4 rows and 5 columns for O3.

[0185] graph 13 is the data representation Li 2-3a-4b (Al 1-x Ga x ) a (Ti 1-y Zr y ) 1+bIn O3, the band gap values ​​of 121 compounds represented by 121 chemical formulas are shown, obtained by varying x and y with a = 0.1 and b = 0.0. Any two of the 121 chemical formulas, (x, y) in Chemical Formula 1 and (x, y) in Chemical Formula 2, are different.

[0186] In other words, graph 13 shows the chemical formula Li 1.7 (Al1Ga0) 0.1 The band gap value e of the material specified as (Ti1Zr0)1O3 13(1,1) (=e 13((x,y)=(0.1,0.0)) ), ~, chemical formula Li 1.7 (Al1Ga0) 0.1 The band gap value e of the material specified by (Ti0Zr1)1O3 13(1,11) (=e 13((x,y)=(0.0,1.0)) ), ~, chemical formula Li 1.7 (Al 0.5 Ga 0.5 ) 0.1 The band gap value e of the material specified as (Ti1Zr0)1O3 13(6,1) (=e 13((x,y)=(0.5,0.0) ), ~, chemical formula Li 1.7 (Al 0.5 Ga 0.5 ) 0.1 The band gap value e of the material specified by (Ti0Zr1)1O3 13(6,11) (=e 13((x,y)=(0.5,1.0) ), ~, chemical formula Li 1.7 (Al0Ga1) 0.1 The band gap value e of the material specified as (Ti1Zr0)1O3 13(11,1) (=e 13((x,y)=(1.0,0.0) ), ~, chemical formula Li 1.7 (Al0Ga1) 0.1 The band gap value e of the material specified by (Ti0Zr1)1O3 13(11,11) (=e 13((x,y)=(1.0,1.0) ) where x has a minimum value of 0.0 and a maximum value of 1.0 and changes in increments of 0.1, and y has a minimum value of 0.0 and a maximum value of 1.0 and changes in increments of 0.1.

[0187] For example, e 13(5,6) (=e 13((x,y)=(0.4,0.5))) is expressed as (b,a)=0.0,0.1) (based on e13, see the table above), and (x,y)=(0.4,0.5) is expressed as data representation Li 2-3a-4b (Al 1-x Ga x ) a (Ti 1-y Zr y ) 1+b The chemical formula obtained by substituting O3 is Li 2-3×0.1-4×0.0 (Al 1-0.4 Ga 0.4 ) 0.1 (Ti 1-0.5 Zr 0.5 ) 1+0.0 O3(=chemical formula Li 1.7 (Al 0.6 Ga 0.4 ) 0.1 (Ti 0.5 Zr 0.5 ) 1.0 O3) may also be interpreted as meaning the band gap value of the material specified.

[0188] The first number is a = 0.1, the second number is 1.0 (= 1 + b = 1 + 0.0) based on b = 0.0, the third number is x = 0.4, and the fourth number is y = 0.5. The fact that the first to fourth numbers are these values ​​is due to the chemical formula Li 2-3×0.1-4×0.0 (Al 1-0.4 Ga 0.4 ) 0.1 (Ti 1-0.5 Zr 0.5 ) 1+0.0 O3 and Data Representation Li 2-3a-4b (Al 1-x Ga x ) a (Ti 1-y Zr y ) 1+b This can be understood by comparing it with O3.

[0189] Chemical formula Li 2-3×0.1-4×0.0 (Al 1-0.4 Ga 0.4 ) 0.1 (Ti 1-0.5 Zr 0.5 ) 1+0.0 O3(=chemical formula Li 1.7 (Al 0.6 Ga 0.4 )0.1 (Ti 0.5 Zr 0.5 ) 1.0 O3) is a compound with the first chemical formula (AlO3) containing the first element symbol Ga. 0.6 Ga 0.4 ) and a second chemical formula containing the second element symbol Zr (Ti 0.5 Zr 0.5 ) is included.

[0190] The first number, 0.1, is the first chemical formula (Al 0.6 Ga 0.4 ) and the second number, 1.0, is the second chemical formula (Ti 0.5 Zr 0.5 ) is a subscript number attached to the symbol Ga, the third number 0.4 is a subscript number attached to the symbol Ga, and the fourth number 0.5 is a subscript number attached to the symbol Zr.

[0191] The predictor is tuned using a plurality of training data, including the first training data.

[0192] The first training data may include a fifth value, a sixth value, a seventh value, an eighth value, and a predetermined property of the compound to be produced, such as a band gap, based on the chemical formula of the compound to be produced. An example of this first training data is shown below.

[0193] The chemical formula is Li 1.7 (Al 0.4 Ga 0.6 ) 0.1 (Ti 0.2 Zr 0.8 ) 1.0 In order to produce compound A, which is Li2O, La2O3, Al2O3, and TiO2, the raw materials are treated under specified conditions, and the actual compound B obtained has the chemical formula Li 1.706 Al 0.041 Ga 0.059 Ti 0.2 Zr 0.8 O 3.003 Let us assume that:

[0194] The chemical formula of the target compound (here, Li1.7 Al 0.04 Ga 0.06 Ti 0.2 Zr 0.8 O3(=Li 1.7 (Al 0.4 Ga 0.6 ) 0.1 (Ti 0.2 Zr 0.8 ) 1+0.0 O3)) may be called the target composition, target composition formula, or target chemical formula.

[0195] The chemical formula of the compound actually obtained (here, Li 1.706 Al 0.041 Ga 0.059 Ti 0.2 Zr 0.8 O 3.003 ) may also be called the actual composition, actual formula, or actual chemical formula.

[0196] The band gap value obtained by measuring compound B is e 00 Let's say.

[0197] Compound A has the chemical formula Li 1.7 (Al 0.4 Ga 0.6 ) 0.1 (Ti 0.2 Zr 0.8 ) 1+0.0 O3 and Data Representation Li 2-3a-4b (Al 1-x Ga x ) a (Ti 1-y Zr y ) 1+b Comparing O3, we can see that the fifth digit in the chemical formula of compound A is a = 0.1, the sixth digit in the chemical formula of compound A is 1 + b = 1.0, the seventh digit in the chemical formula of compound A is x = 0.6, and the eighth digit in the chemical formula of compound A is y = 0.8.

[0198] Therefore, the training data is (a, b, x, y, band gap value) = (0.1, 0.0, 0.6, 0.8, e 00) The training data may include information other than a, b, x, y, and band gap, such as information identifying Li, Al, Ga, Ti, Zr, and / or O.

[0199] The first data is e 00 may be.

[0200] The ninth number is a = 0.1, the tenth number is b = 0.0, the eleventh number is x = 0.6, and the twelfth number is y = 0.8. The numbers contained in the chemical formula can be calculated to determine the numbers to input into the predictor nodes. In this example, the sixth number (= 1 + b) is converted to the tenth number (= b).

[0201] The first and fifth digits indicate the value of a in the data representation, so the first and fifth digits correspond. The second and sixth digits indicate the value of 1+b in the data representation, so the second and sixth digits correspond. The third and seventh digits indicate the value of y in the data representation, so the third and seventh digits correspond. The fourth and eighth digits indicate the value of x in the data representation, so the fourth and eighth digits correspond.

[0202] The tuned predictor is input with information specifying the number corresponding to p, the number corresponding to q, the number corresponding to i, and the value for j, and then calculates the value e pq(i,j) Output.

[0203] For example, output e 13(5,6) (=e 13((x,y)=(0.4,0.5)) ) is the data representation Li 2-3a-4b (Al 1-x Ga x ) a (Ti 1-y Zr y ) 1+b Chemical formula Li for O3 2-3×0.1-4×0.0 (Al 1-0.4 Ga 0.4 ) 0.1 (Ti 1-0.5 Zr 0.5 ) 1+0.0 O3(=chemical formula Li 1.7 (Al0.6 Ga 0.4 ) 0.1 (Ti 0.5 Zr 0.5 ) 1.0 O3) is the band gap value of the material specified by e 13(5,6) is the value output from the predictor when (a, b, x, y) = (0.1, 0.0, 0.4, 0.5) is input to the predictor.

[0204] The information related to the first data is the graph of Characteristic 1 and the graph of Characteristic 2 shown in Figure 10A. The graph of Characteristic 1 shows an impedance value of 235 ohms, and the graph of Characteristic 2 shows an oxidation potential value of 3.5.

[0205] (Embodiment 2) In the second embodiment, after a second image is generated and displayed, the display content is edited based on user input and then displayed. In the second embodiment, the same components as those in the first embodiment are denoted by the same reference numerals, and the description thereof will be omitted.

[0206] A description will be given of the configuration of the second embodiment. A display system 2000 according to the second embodiment of the present disclosure will be described in detail below with reference to the drawings.

[0207] 17 is a block diagram showing the configuration of a display system 2000 according to the second embodiment of the present disclosure. The display system 2000 shown in FIG. 17 includes a characteristic display device 200, an input unit 1, a predictor database (DB) 2, an experiment database (DB) 3, a selection receiving unit 4, a compound information database (DB) 5, a display unit 6, and an editing information input unit 21.

[0208] The characteristic display device 200 includes a characteristic value acquisition unit 101, an image generation unit 102, a reduced image generation unit 103, a data acquisition unit 104, a data image generation unit 105, an image layout determination unit 106, and an edited image generation unit 201. The characteristic display device 200 may be configured with a processor such as a central processing unit (CPU) and a memory. In this case, the processor functions as the characteristic display device 200 by executing a computer program stored in the memory, for example. In FIG. 17, the predictor database 2, the experiment database 3, and the compound information database 5 are configured with, for example, non-volatile memory.

[0209] The following describes the details of each component shown in FIG.

[0210] Since each component other than the editing information input unit 21 and the edited image generation unit 201 is the same as in Figure 1, the explanation will be omitted and the operation after the second image in embodiments 1-1 and 1-2 is displayed on the display unit 6 will be explained.

[0211] [Editing Information Input Section 21] The editing information input unit 21 receives user input and outputs it to the edited image generation unit 201. The user input may be performed by clicking or dragging. The editing information input unit 21 may be configured as, for example, a keyboard, a touch sensor, a touchpad, or a mouse.

[0212] [Edited image generation unit 201] The edited image generation unit 201 generates an edited image based on the input acquired from the edit information input unit 21, and outputs the generated edited image to the image layout determination unit 106. The generated edited image is displayed on the display unit 6 by the image layout determination unit 106.

[0213] Fig. 18 shows an example of editing information input by the user after the second image is displayed on the display unit 6, and Fig. 19 shows an example of an edited image. The editing information is information specified by a user input received by the editing information input unit 21, and is, for example, compound identifying information specified by clicking or information on an area specified by dragging.

[0214] In FIG. 18 , as an example, four compound graph data corresponding to the four selected experimental characteristic values ​​are superimposed and displayed in a graph superimposed image included in the second image. In this case, as shown in FIG. 19 , the compound identification information displayed as a legend in the graph superimposed image is selected by clicking, or the compound graph data itself is selected by clicking. This causes the edited image generation unit 201 to switch between displaying and hiding the compound graph data and generate an edited image. That is, the edited image generation unit 201 acquires user input from the edited information input unit 21, i.e., the compound identification information or compound graph data selected by the user. The edited image generation unit 201 then switches between displaying and hiding the compound graph data associated with the selected compound identification information or the compound graph data directly selected. This switching between displaying and hiding generates an edited image. Note that in the example shown in FIG. 19 , the compound identification information is selected by clicking, and the compound graph data associated with the compound identification information is switched to hidden.

[0215] When a part of the compound graph data included in the compound graph data image is surrounded and selected by dragging (i.e., drawing range selection), the edited image generation unit 201 generates an edited image in which the selected drawing range is enlarged. That is, the edited image generation unit 201 acquires the user's input, i.e., the drawing range selected by the user, from the editing information input unit 21. Then, the edited image generation unit 201 generates an edited image by enlarging the selected drawing range.

[0216] For example, if the user of the display system 2000 or the characteristic display device 200 is a researcher, it is expected that the user will select multiple mutually similar experimental characteristic values ​​from the first image when conducting an experiment. In this case, it may be difficult to visually distinguish the differences in peak positions of the compound graph data corresponding to each experimental characteristic value, and it is often the case that these difficult-to-discern differences in peak positions are important in conducting the experiment.

[0217] Therefore, according to this embodiment, the user can superimpose multiple selected compound graph data to grasp the overall trend. Methods for comparing multiple compound graph data include displaying multiple compound graph data in an overlapping manner to check for small differences in peaks, displaying them in an overlapping manner with a shift to check for large differences in peaks, and displaying the difference between two compound graph data. Other methods include outputting waveform data from the predictor database 2 and displaying the degree of agreement between the compound graph data and the waveform data, or pre-saving a similarity calculation formula and highlighting compound graph data whose similarity does not satisfy a predetermined standard. That is, the method may involve the user visually checking, or may involve calculation using machine learning or a calculation formula and then comparing the results.

[0218] For more detailed analysis, you can change the display of compound graph data and expand the range where peaks are difficult to see due to complex or overlapping peaks. You can also check specific peak coincidences and differences (such as peak position shifts) and perform more detailed analysis.

[0219] (Embodiment 2: Description of Operation) Next, the operation of the characteristic display device 200 will be described.

[0220] (flowchart) 20 is a flowchart showing the overall flow of processing by the property display device 200 according to the second embodiment of the present disclosure. The processing from steps S201 to S210 is the same as the processing from steps S101 to S110 in Fig. 16, and therefore a description thereof will be omitted. That is, the operation after the second image in the first embodiment is generated and displayed will be described.

[0221] (Step S211) The editing information input unit 21 receives input from the user and outputs it to the edited image generation unit 201 .

[0222] (Step S212) The edited image generating unit 201 generates an edited image based on the input acquired from the editing information input unit 21 and outputs it to the image layout determining unit 106 .

[0223] (Step S213) The image layout determination unit 106 generates a third image in which the first image or the first reduced image and the edited image are displayed on one screen, and outputs the third image to the display unit 6.

[0224] (Step S214) The display unit 6 displays the third image acquired from the image layout determination unit 106.

[0225] <Summary of the second embodiment> As described above, in the characteristic display method of the second embodiment, after the seventh step, the edited image generation unit 201 and the image layout determination unit 106 edit at least one of the multiple superimposed compound graph data displayed by the graph superimposed image, generate a third image based on the edit, and output the third image to the display unit 6. The edit includes switching the states of selected compound graph data from the multiple superimposed compound graph data and the compound identification information associated with the compound graph data between a display state and a non-display state. Alternatively, the edit includes selecting a partial area of ​​the graph superimposed image and enlarging and displaying the selected area.

[0226] This makes it possible to improve the visibility of multiple compound graph data that are superimposed. Specifically, by displaying two compound graph data from three or more compound graph data and hiding the other compound graph data, it is possible to compare the two compound graph data one-to-one. By expanding the area, it is possible to compare the details of multiple compound graph data.

[0227] In the characteristic display method in the second embodiment, editing is performed on a graph superimposed image showing a plurality of compound graph data superimposed on each other, but the same editing as described above may also be performed on a compound graph data image showing one compound graph data. That is, after the seventh step, the edited image generation unit 201 and the image layout determination unit 106 select a partial area of ​​the compound graph data image included in the compound data image shown in Fig. 7, for example, and enlarge and display the selected area. This makes it possible to further improve the visibility of the compound graph data even when compound graph data corresponding to one characteristic experimental value or compound identification information is shown in the compound data image.

[0228] (Embodiment 3) In the third embodiment, a first image in which the experimental characteristic values ​​are superimposed on the predicted characteristic values, a second image in which the first image or the first reduced image and a compound data image are displayed on a single screen, or a third image in which the first image or the first reduced image and an edited image are displayed on a single screen are generated and displayed, and then the displayed content is saved based on a user input, and the saved data is read. The data is read by reconstructing the saved data into a fourth image and displaying it. In this embodiment, the same components as those in the first embodiment are denoted by the same reference numerals, and a description thereof will be omitted.

[0229] A description will be given of the configuration of embodiment 3. A display system 3000 according to embodiment 3 of the present disclosure will be described in detail below with reference to the drawings.

[0230] 21 is a block diagram showing the configuration of a display system 3000 according to the third embodiment of the present disclosure. The display system 3000 shown in FIG. 21 includes a characteristic display device 300, an input unit 1, a predictor database (DB) 2, an experiment database (DB) 3, a selection receiving unit 4, a compound information database (DB) 5, a display unit 6, a save input unit 31, a read input unit 32, and a save database (DB) 33.

[0231] The characteristic display device 300 includes a characteristic value acquisition unit 101, an image generation unit 102, a reduced image generation unit 103, a data acquisition unit 104, a data image generation unit 105, an image layout determination unit 106, a saved data generation unit 301, and a saved data reconstruction unit 302. The characteristic display device 300 may be configured with a processor such as a central processing unit (CPU) and a memory. In this case, the processor functions as the characteristic display device 300 by executing a computer program stored in the memory, for example. In FIG. 21 , the predictor database 2, the experiment database 3, the compound information database 5, and the saved database 33 are configured with, for example, non-volatile memory.

[0232] The following describes each component shown in FIG. 21 in detail.

[0233] 1, and therefore description thereof will be omitted. That is, the following describes operations after generating and displaying a first image in which the experimental characteristic value is superimposed on the predicted characteristic value, a second image in which the first image or the first reduced image and the compound data image are displayed on a single screen, or a third image in which the first image or the first reduced image and the edited image are displayed on a single screen. In this case, in the third embodiment, the first image, the reduced first image, the second image, and the third image displayed on the display unit 6 each have a data read button and a data save button.

[0234] FIG. 22 is an example of a first image having a data load button a1 and a data save button b1. The ability to save and load data allows a user who is a researcher to save and reconfirm information about properties that have been investigated during an experiment, which often takes several years. In collaborative research with multiple people, data can be shared by attaching files to emails or sharing files on the cloud while conducting experiments. In other words, even if collaborative users are located in different locations, data can be shared in real time, allowing for repeated analysis and discussions to conduct collaborative research.

[0235] [Save Input Section 31] The save input unit 31 receives user input and outputs it to the save data generation unit 301. The user may input by clicking the data save button b1 and then specifying a save file name. The save file name may be specified by the user at will, or the user may be allowed to specify a save file name that is automatically assigned based on the content of the data to be saved.

[0236] [Saved data generation unit 301] The saved data generation unit 301 outputs the first image, reduced first image, second image, or third image displayed on the display unit 6 when receiving user input from the saved input unit 31 to the saved database 33 with a saved file name designated by the user. For example, after at least one image of a plurality of images such as the above-mentioned first image and second image is displayed, the saved data generation unit 301 converts the displayed image into a file of a predetermined format and saves it.

[0237] [Saved Database 33] The saved database 33 stores the image data acquired from the saved data generation unit 301. The data saved in the saved database 33 is also called image data, a file, or saved data. The saved database 33 may be a cloud server connected to the characteristic display device 300 via a communication network such as the Internet.

[0238] [Read input section 32] The read input unit 32 receives user input and outputs the saved data selected by the user to the saved data reconstruction unit 302. The user input includes clicking the data read button a1, and then specifying the name of the saved file to be read.

[0239] [Stored data reconstruction unit 302] The saved data reconstruction unit 302 retrieves image data corresponding to the user-specified saved file name received from the read input unit 32 from the saved database 33, reconstructs the data, and outputs it to the image layout determination unit 106. In other words, a fourth image is output to the image layout determination unit 106. The image data saved in the saved database 33 is the first image, the reduced first image, the second image, or the third image displayed on the display unit 6. That is, the image data cannot be further manipulated, such as selecting or editing further characteristic experimental values. In other words, each image, such as the first image, is saved in a file format that does not allow further manipulation, such as selecting or editing further characteristic experimental values. Therefore, by reconstructing the image data, the state of the image data can be restored to its pre-save state, in which manipulation is possible. By enabling so-called data backup, analysis can be resumed immediately after loading when sharing data, for example, by attaching the file to an email or sharing the file on the cloud. This is expected to facilitate smooth discussions and more efficient research and development.

[0240] The save input unit 31 and the read input unit 32 in this embodiment may be configured as, for example, a keyboard, a touch sensor, a touch pad, or a mouse.

[0241] (Third embodiment: explanation of operation) Next, the operation of the characteristic display device 300 will be described.

[0242] (flowchart) 23 is a flowchart showing an overall flow of processing by the property display device 300 according to the third embodiment of the present disclosure. The processing from steps S301 to S303 and S310 to S316 is the same as the processing from steps S101 to S110 in FIG. 16, and therefore a description thereof will be omitted. That is, the following describes operations performed after generating and displaying on the display unit 6 a first image in which the experimental property value is superimposed on the predicted property value, a second image in which the first image or the first reduced image and the compound data image are combined on a single screen, or a third image in which the first image or the first reduced image and the edited image are combined on a single screen.

[0243] (Step S304) The save input unit 31 receives the input from the user (YES in step S304) and outputs it to the save data generation unit 301.

[0244] (Step S305) The saved data generation unit 301 outputs the display content displayed on the display unit 6 when the saved data generation unit 301 receives user input from the saved data input unit 31 to the saved data database 33 under a saved file name designated by the user. Note that if the saved data input unit 31 does not receive user input (NO in step S304), the processing in step S305 is skipped.

[0245] (Step S306) The read input unit 32 receives the input from the user (YES in step S306), and outputs the stored data selected by the user to the stored data reconstructing unit 302.

[0246] (Step S307) The saved data reconstruction unit 302 retrieves the data of the user-specified saved file name received from the read input unit 32 from the saved database 33, reconstructs it into a fourth image, and then outputs the fourth image to the image layout determination unit 106.

[0247] (Step S308) The image layout determination unit 106 determines the layout of the fourth image acquired from the saved data reconstruction unit 302 and outputs it to the display unit 6.

[0248] (Step S309) The display unit 6 displays the fourth image acquired from the image layout determination unit 106. If the read input unit 32 does not receive an input from the user (NO in step S306), the processes of steps S307 to S309 are skipped.

[0249] <Summary of the Third Embodiment> As described above, in the characteristic display method of the third embodiment, after at least one of the first image and the second image is output to the display unit, the saved data generation unit 301 converts the output image into a file of a predetermined format and saves it. Then, the saved data reconstruction unit 302 and the image layout determination unit 106 acquire the saved file and reconstruct the acquired file into an image to generate a fourth image, which is output to the display unit 6.

[0250] This allows multiple property display devices 300 to save files via the server, such as a cloud server, and reconstruct the files by reading them. As a result, users of the property display devices 300 can view the same images and actively discuss material properties, even if they are remote from each other. In other words, images can be shared.

[0251] (Fourth embodiment) In the fourth embodiment, a second image in which the first image or the first reduced image and the compound data image are displayed on one screen is corrected based on a user's input, and a fifth image in which the second image is corrected based on the correction result is generated and displayed. Note that in this embodiment, the same components as those in the first embodiment are denoted by the same reference numerals, and description thereof will be omitted.

[0252] A description will be given of the configuration of the fourth embodiment. A display system 4000 according to the fourth embodiment of the present disclosure will be described in detail below with reference to the drawings.

[0253] 24 is a block diagram showing the configuration of a display system 4000 according to the fourth embodiment of the present disclosure. The display system 4000 shown in FIG. 24 includes a characteristic display device 400, an input unit 1, a predictor database (DB) 2, an experiment database (DB) 3, a selection receiving unit 4, a compound information database (DB) 5, a display unit 6, a correction input unit 41, and a converter database (DB) 42.

[0254] The characteristic display device 400 includes a characteristic value acquisition unit 101, an image generation unit 102, a reduced image generation unit 103, a data acquisition unit 104, a data image generation unit 105, an image layout determination unit 106, a data correction unit 401, and a corrected image generation unit 402. The characteristic display device 400 may be configured with a processor such as a central processing unit (CPU) and a memory. In this case, the processor functions as the characteristic display device 400 by executing a computer program stored in the memory, for example. In FIG. 24, the predictor database 2, the experiment database 3, the compound information database 5, and the converter database (DB) 42 are configured with, for example, non-volatile memory.

[0255] The following describes the details of each component shown in FIG.

[0256] The components other than the correction input unit 41, converter database 42, data correction unit 401, and corrected image generation unit 402 are the same as those in Fig. 1, and therefore descriptions thereof will be omitted. That is, the operation after the second image, in which the first image or the first reduced image and the compound data image are displayed on one screen, will be described.

[0257] At this time, the second image in the fourth embodiment has a data correction button.

[0258] 25 is an example of the second image having a data correction button c. For example, in a research and development site, it is expected that (i) an incorrect input is made in the process of registering the characteristic experimental value in the experiment database 3, (ii) an incorrect experiment is performed and a correct characteristic experimental value is subsequently obtained, or (iii) the characteristic experimental value is updated to a more accurate value due to the acquisition of knowledge through experience, changes in the experimental method, changes over time, etc. By being able to correct the data, the characteristic experimental value can be corrected or updated, thereby improving the accuracy of the characteristic experimental value.

[0259] [Edit Input Section 41] The correction input unit 41 receives user input and outputs it to the data correction unit 401. User input includes inputting a correction value into the compound configuration data image, clicking the data correction button c, and then displaying a confirmation screen. The correction input unit 41 may be configured as, for example, a keyboard, a touch sensor, a touchpad, or a mouse. The correction value is input to change the value included in the compound configuration data shown by the compound configuration data image.

[0260] [Data Correction Section 401] The data correction unit 401 outputs the correction value acquired from the correction input unit 41 to the compound information database 5. The data correction unit 401 acquires a converter from the converter database 42. The data correction unit 401 then inputs the corrected compound structure data to the converter to acquire a corrected experimental characteristic value, and outputs the corrected experimental characteristic value to the experiment database 3. The corrected compound structure data includes the correction value. The data correction unit 401 outputs the correction value to the corrected image generation unit 402.

[0261] [Converter Database 42] The converter database 42 stores in advance converters that output experimental characteristic values ​​from compound structure data. One example of a converter is a formula that can calculate experimental characteristic values ​​from compound structure data. A converter may be prepared for each formula, or related formulas may be stored together in a single converter. The converter may be in any format that can receive compound structure data as input and output experimental characteristic values. For example, the converter may be a learning model generated by machine learning, specifically, a neural network generated by machine learning such as deep learning.

[0262] [Corrected image generation unit 402] The corrected image generation unit 402 generates a corrected image based on the correction value acquired from the correction input unit 41, and outputs the generated image to the image layout determination unit 106. This corrected image is an image in which the compound data image has been corrected, and the values ​​shown in the compound configuration data image included in the compound data image have been replaced with the correction values.

[0263] 26A and 26B are examples showing the transition from the second image to the fifth image due to correction. As shown in FIG. 26A, by selecting an experimental characteristic value displayed in the first image, the image layout determination unit 106 displays on the display unit 6 a second image in which a compound data image corresponding to the selected experimental characteristic value and a reduced first image are combined on a single screen. Here, as an example, an experimental characteristic value that appears to be the result of an input error, i.e., an experimental characteristic value that significantly differs from the predicted characteristic value, is selected. Next, as shown in FIG. 26B, the user inputs a corrected value for the compound configuration data displayed in the compound configuration data image and clicks the data correction button c. For example, the impedance value "235 Ω" is corrected to a corrected value of "23.5 Ω." The image layout determination unit 106 displays a confirmation screen D1 on the display unit 6. Then, based on the user's input, the image layout determination unit 106 confirms the corrected input information (i.e., the corrected value) on the displayed confirmation screen D1 and displays the fifth image.

[0264] The fifth image is an image in which the corrected first image or reduced first image and the corrected image, which is the corrected compound data image, are placed on a single screen. The first image or reduced first image is corrected and placed in the fifth image by outputting the corrected experimental characteristic values ​​from the data correction unit 401 to the experimental database 3. That is, the characteristic value acquisition unit 101 acquires the corrected experimental characteristic values ​​from the experimental database 3. Then, the image generation unit 102 or reduced image generation unit 103 replaces the mark indicating the experimental characteristic value before correction, which is included in the first image or reduced image, with a mark indicating the experimental characteristic value after correction. In this way, the first image or reduced first image is corrected. The corrected first image or reduced first image is placed in the fifth image.

[0265] In Fig. 26B, it can be seen that the experimental characteristic values ​​are closer to the predicted characteristic values ​​compared to the pre-correction values ​​in Fig. 26A. According to this embodiment, after inputting the correction values, it is possible to generate and display a fifth image without using any other device or program, and compare the pre-correction and post-correction values, thereby reducing the number of analysis steps.

[0266] (Fourth embodiment: explanation of operation) Next, the operation of the characteristic display device 400 will be described.

[0267] (flowchart) 27 is a flowchart showing the overall flow of processing by the property display device 400 according to the fourth embodiment of the present disclosure. The processing from steps S401 to S410 is the same as the processing from steps S101 to S110 in Fig. 16, and therefore a description thereof will be omitted. That is, the operation after the second image, in which the first image or the first reduced image and the compound data image are displayed on one screen, will be described.

[0268] (Step S411) The correction input unit 41 receives a correction value as a user input and outputs it to the data correction unit 401 .

[0269] (Step S412) The data correction unit 401 outputs the correction value acquired from the correction input unit 41 to the compound information database 5. As a result, the value of the compound structure data included in the compound information data stored in the compound information database 5 is replaced with the correction value. The data correction unit 401 acquires a converter from the converter database 42. Then, the data correction unit 401 inputs the corrected compound structure data into the converter to acquire a corrected characteristic experimental value, and outputs the corrected characteristic experimental value to the experiment database 3. Furthermore, the data correction unit 401 outputs the correction value to the corrected image generation unit 402.

[0270] (Step S413) The corrected image generating unit 402 generates a corrected image based on the correction value acquired from the data correcting unit 401 and outputs it to the image layout determining unit 106 .

[0271] (Step S414) The image layout determination unit 106 generates a fifth image in which the corrected first image or the first reduced image and the corrected image are displayed on one screen, and outputs the fifth image to the display unit 6.

[0272] (Step S415) The display unit 6 displays the fifth image acquired from the image layout determination unit 106.

[0273] <Summary of the Fourth Embodiment> Thus, in the characteristic display method of embodiment 4, in the seventh step, after the image layout determination unit 106 outputs the second image to the display unit 6, when the information contained in the compound information data shown in the second image is corrected, the data correction unit 401, the corrected image generation unit 402, and the image layout determination unit 106 output the corrected information to the compound information database 5 that stores the compound information data before correction, and generate a fifth image by correcting the second image based on the corrected information, and output it to the display unit 6.

[0274] For example, as described above, there are cases where the experimental characteristic values ​​superimposed on the first image in the second image are calculated based on information contained in the compound information data. In such cases, if there is an error in the information contained in the compound information data, the experimental characteristic values ​​superimposed on the first image will deviate from the predicted characteristic values. Therefore, in the characteristic display method of this embodiment, the second image is corrected by correcting the information contained in the compound information data, so that the experimental characteristic values ​​of the second image can be correctly corrected. Therefore, even if there is an error in the compound information data, the second image can be easily corrected appropriately.

[0275] (Embodiment 5) In the fifth embodiment, after displaying the first image and a prediction model display button, a prediction model image is generated based on user input, and a sixth image is generated and displayed, which contains at least one of the first image and the reduced first image and at least one of the prediction model image and the reduced prediction model image on a single screen. Then, a seventh image is generated and displayed, which contains at least one of the sixth image and the reduced sixth image and a compound data image corresponding to the characteristic experimental value shown in the prediction model image selected by the user on a single screen. In this embodiment, the same components as those in the first embodiment are designated by the same reference numerals, and their description will be omitted.

[0276] A description will be given of the configuration of embodiment 5. A display system 5000 according to embodiment 5 of the present disclosure will be described in detail below with reference to the drawings.

[0277] 28 is a block diagram showing the configuration of a display system 5000 according to embodiment 5 of the present disclosure. The display system 5000 shown in FIG. 28 includes a characteristic display device 500, an input unit 1, a predictor database (DB) 2, an experiment database (DB) 3, a selection receiving unit 4, a compound information database (DB) 5, a display unit 6, and a prediction model selection receiving unit 51.

[0278] The characteristic display device 500 includes a characteristic value acquisition unit 101, an image generation unit 102, a reduced image generation unit 103, a data acquisition unit 104, a data image generation unit 105, an image layout determination unit 106, and a prediction model image generation unit 501. The characteristic display device 500 may be configured with a processor such as a central processing unit (CPU) and a memory. In this case, the processor functions as the characteristic display device 500 by executing a computer program stored in the memory, for example. In FIG. 28, the predictor database 2, the experiment database 3, and the compound information database 5 are configured with, for example, non-volatile memory.

[0279] The following describes the details of each component shown in FIG.

[0280] The components other than the prediction model selection receiving unit 51 and the prediction model image generating unit 501 are the same as those in Fig. 1, and therefore description thereof will be omitted. That is, the description will begin with the operation after the first image is displayed.

[0281] At this time, the first image in the fifth embodiment has a prediction model display button.

[0282] FIG. 29 is an example of a first image having a prediction model display button d. Because the first image prioritizes improving visual comprehension, it is impossible to know the actual degree of difference between the experimental characteristic values ​​and the corresponding predicted characteristic values. When there are multiple experimental characteristic values ​​or when the first image is as shown in FIG. 4B , i.e., when there are 100 or more image element maps, examining the experimental characteristic values ​​point by point would require an enormous amount of time. Therefore, by displaying a prediction model that shows the correspondence between the experimental characteristic values ​​and the predicted characteristic values ​​using a linear graph or the like, it is possible to know the specific degree of difference between the experimental characteristic values ​​and the corresponding predicted characteristic values.

[0283] [Prediction model selection reception unit 51] The prediction model selection receiving unit 51 receives a user input and outputs it to the prediction model image generating unit 501. The user input may be performed by clicking a prediction model display button d. The prediction model selection receiving unit 51 may be configured as, for example, a keyboard, a touch sensor, a touchpad, or a mouse.

[0284] [Prediction model image generation unit 501] The prediction model image generation unit 501 acquires the experimental characteristic values ​​included in the first image displayed when the input from the prediction model selection reception unit 51 is received from the experiment database 3, and acquires the predicted characteristic values ​​from the predictor database 2. Thereafter, the prediction model image generation unit 501 generates a prediction model image based on the acquired experimental characteristic values ​​and predicted characteristic values, and outputs the generated prediction model image to the image layout determination unit 106.

[0285] FIG. 30 is an example of an image showing the transition of a display screen. After the prediction model display button d is selected by the user, the image layout determination unit 106 displays a sixth image on the display unit 6, in which the reduced first image and the prediction model image are combined on a single screen. As shown in FIG. 30, the prediction model image is an image in which a predicted property value and an experimental property value corresponding to the same compound identification information are represented by a single point. For example, the prediction model image is configured as a graph in which the values ​​on the vertical axis and the horizontal axis represent the predicted property value and the experimental property value. When the user selects a property value included in the prediction model image of the sixth image, the selection receiving unit 4 acquires the selected property value and outputs it to the data acquisition unit 104. The data image generation unit 105 generates a compound data image corresponding to the selected property value and outputs the compound data image to the image layout determination unit 106. The image layout determination unit 106 displays a seventh image on the display unit 6, in which the compound data image and at least one of the sixth image and the reduced sixth image are combined on a single screen. This allows data selection while viewing the prediction model image, improving analysis efficiency compared to selecting experimental property values ​​in the first image. For example, if there is an experimental property value that differs significantly from the predicted property value, the user selects that experimental property value and checks the corresponding compound information data. For example, the experimental property value is point B, which is slightly away from the dashed line in the prediction model image shown in FIG. 30 where the predicted property value and the experimental property value perfectly match. On the other hand, if there is an experimental property value that approximately matches the predicted property value, the user selects that experimental property value and checks the corresponding compound information data. For example, the experimental property value that approximately matches the predicted property value is point A, which partially overlaps the dashed line in the prediction model image shown in FIG. 30 where the predicted property value and the experimental property value perfectly match. The user can then compare the compound information data to identify parameters or process conditions that cause the difference. Depending on the contributing parameters or process conditions, the user can determine whether the difference is due to the accuracy of the predicted property value or the experimental property value, making it easier to efficiently consider next improvement measures.

[0286] (Fifth embodiment: explanation of operation) Next, the operation of the characteristic display device 500 will be described.

[0287] (flowchart) 31 is a flowchart showing the overall flow of processing by the characteristic display device 500 according to the fifth embodiment of the present disclosure. The processing from steps S501 to S503, steps S508 to S509, and steps S512 to S514 is the same as the processing from steps S101 to S108 in FIG. 16, and therefore description thereof will be omitted.

[0288] (Step S504) The prediction model selection receiving unit 51 receives an input from the user and outputs it to the prediction model image generating unit 501 .

[0289] (Step S505) The prediction model image generation unit 501 acquires the characteristic experimental values ​​included in the first image displayed when the input from the prediction model selection reception unit 51 is received from the experiment database 3, and acquires the characteristic predicted values ​​from the predictor database 2. Thereafter, the prediction model image generation unit 501 generates a prediction model image based on the acquired characteristic experimental values ​​and characteristic predicted values.

[0290] (Step S506) The image layout determination unit 106 determines whether or not to reduce the prediction model image. If the prediction model image generated in the prediction model image generation unit 501 is to be reduced (YES in step S506), the image layout determination unit 106 causes the reduced image generation unit 103 to reduce the prediction model image. At this time, the prediction model image generation unit 501 outputs the prediction model image to the reduced image generation unit 103. Then, the image layout determination unit 106 acquires the reduced prediction model image from the reduced image generation unit 103. On the other hand, if the prediction model image is not to be reduced (NO in step S506), the image layout determination unit 106 acquires the prediction model image from the prediction model image generation unit 501.

[0291] (Step S507) The reduced image generation unit 103 reduces the prediction model image acquired from the prediction model image generation unit 501 and outputs the reduced prediction model image to the image layout determination unit 106 .

[0292] (Step S510) The image layout determination unit 106 generates a sixth image in which at least one of the first image and the reduced first image and at least one of the prediction model image and the reduced prediction model image are displayed on one screen, and outputs the sixth image to the display unit 6.

[0293] (Step S511) The display unit 6 displays the sixth image acquired from the image layout determination unit 106.

[0294] (Step S515) The image layout determination unit 106 determines whether or not to reduce the sixth image.

[0295] If the sixth image is to be reduced (YES in step S515), the image layout determination unit 106 causes the reduced image generation unit 103 to reduce the sixth image. At this time, the image layout determination unit 106 outputs the sixth image to the reduced image generation unit 103. If the sixth image is not to be reduced (NO in step S515), the process proceeds directly to step S517.

[0296] (Step S516) The reduced image generation unit 103 reduces the sixth image acquired from the image layout determination unit 106 and outputs the reduced image to the image layout determination unit 106.

[0297] (Step S517) The image layout determination unit 106 generates a seventh image in which the compound data image and at least one of the sixth image and the reduced sixth image are placed on one screen, and outputs the seventh image to the display unit 6.

[0298] (Step S518) The display unit 6 displays the seventh image acquired from the image layout determination unit 106.

[0299] <Summary of the Fifth Embodiment> Thus, in the characteristic display method of embodiment 5, in the fourth step, after the image layout determination unit 106 outputs the first image to the display unit 6, the prediction model image generation unit 501 generates a prediction model image based on the multiple predicted characteristic values ​​and at least one experimental characteristic value shown in the first image. Then, the image layout determination unit 106 generates a sixth image including the first image and the prediction model image and outputs it to the display unit. Here, when the experimental characteristic value shown in the prediction model image is selected, the data acquisition unit 104 acquires compound identification information corresponding to the selected experimental characteristic value and acquires compound information data associated with the compound identification information. The image layout determination unit 106 generates a seventh image including an image showing the compound information data and the sixth image, and outputs it to the display unit 6.

[0300] This allows not only the experimental characteristic value shown in the first image, but also the compound information data related to the experimental characteristic value when the experimental characteristic value shown in the prediction model image in the sixth image is selected. Therefore, the user can easily select, for example, an experimental characteristic value that significantly differs from the predicted characteristic value from the prediction model image showing the relationship between the predicted characteristic value and the experimental characteristic value, and refer to the compound information data. The seventh image contains the first image, the prediction model image, and an image showing the compound information data on a single screen, allowing the user to check these images at a glance without switching screens.

[0301] (Sixth embodiment) In the sixth embodiment, after displaying a first image, compound identification information corresponding to a characteristic experimental value selected by the user is acquired, characteristic prediction data corresponding to the compound identification information is acquired, and a compound data image corresponding to the relationship with the characteristic prediction data is generated and displayed. The characteristic prediction data indicates, for example, the characteristics of compound graph data predicted for a compound having the above-described compound identification information. In a specific example, the characteristic prediction data indicates each peak included in the compound graph data as its characteristics. In other words, this embodiment is effective, for example, in cases where compound graph data that allows comparison of peak positions with the characteristic prediction data is stored in the compound information database 5. In this embodiment, the same components as those in the first embodiment are designated by the same reference numerals, and description thereof will be omitted.

[0302] A description will be given of the configuration of the sixth embodiment. A display system 6000 according to the sixth embodiment of the present disclosure will be described in detail below with reference to the drawings.

[0303] 32 is a block diagram showing the configuration of a display system 6000 according to the sixth embodiment of the present disclosure. The display system 6000 shown in FIG. 32 includes a characteristic display device 600, an input unit 1, a predictor database (DB) 2, an experiment database (DB) 3, a selection receiving unit 4, a compound information database (DB) 5, and a display unit 6.

[0304] The characteristic display device 600 includes a characteristic value acquisition unit 101, an image generation unit 102, a reduced image generation unit 103, a data acquisition unit 104, a data image generation unit 105, an image layout determination unit 106, and a predicted data acquisition unit 601. The characteristic display device 600 may be configured with a processor such as a central processing unit (CPU) and a memory. In this case, the processor functions as the characteristic display device 600 by executing a computer program stored in the memory, for example. In FIG. 32, the predictor database 2, the experiment database 3, and the compound information database 5 are configured with, for example, non-volatile memory.

[0305] The following describes the details of each component shown in FIG.

[0306] The components other than the predicted data acquisition unit 601 are the same as those in Fig. 1, and therefore description thereof will be omitted. That is, the description will begin with the operation after the first image is displayed.

[0307] [Prediction data acquisition unit 601] The predicted data acquisition unit 601 acquires, from the predictor database 2 , characteristic prediction data corresponding to the compound identification information acquired by the data acquisition unit 104 , and outputs the data to the data image generation unit 105 .

[0308] The predictor held in the predictor database 2 outputs property prediction data corresponding to a compound when the compound's structure, i.e., a combination of option data, is input. Therefore, the predicted data acquisition unit 601 inputs a combination of option data indicating the structure of a compound having compound identification information acquired by the data acquisition unit 104 to the predictor, thereby acquiring property prediction data corresponding to the compound. In this embodiment, the predictor may be in any format as long as it can output property prediction data for the input compound structure.

[0309] Alternatively, a predictor held in the predictor database 2 may output property prediction data corresponding to a compound in response to input of the compound's configuration, i.e., a combination of option data, and information contained in the compound information data of that compound. In this case, the prediction data acquisition unit 601 inputs to the predictor the combination of option data indicating the configuration of a compound having compound identification information acquired by the data acquisition unit 104, and the information contained in the compound information data of that compound acquired from the compound information database 5. In this way, the prediction data acquisition unit 601 acquires property prediction data corresponding to that compound.

[0310] 33 and 34 are examples of compound graph data images. Here, the compound graph data image shows, as an example, XRD (X-Ray Diffraction) data as compound graph data. Here, XRD is a type of analytical technique for investigating the type of substance. XRD data is data that shows the results obtained by the XRD, for example, as a graph. Note that the horizontal axis of the graph indicates the diffraction angle, and the vertical axis indicates the diffracted X-ray intensity. In the following explanation, the XRD data is data obtained by an experiment on a compound having compound identification information, and the characteristic prediction data indicates, for example, the peak positions and intensities of the XRD data predicted for the compound having the compound identification information.

[0311] It is well known that there is a close relationship between the type of substance (i.e., material or compound) and its properties. Therefore, identifying the type of synthesized substance is extremely important in material development. Figure 33 shows XRD data and predicted property data in which the peak positions are determined to roughly match. The upper part 611 of Figure 33 shows XRD data corresponding to the experimental property values. The lower part 612 shows a comparison of the peak positions of the predicted property data and the XRD data corresponding to the experimental property values. In the peak comparison between the predicted property data and the XRD data, dotted lines indicate the peak positions of the XRD data. When the peak positions of the XRD data and the predicted property data match, at least a portion of the dotted line is shown as a solid line. That is, the XRD data corresponding to the experimental property values ​​shown in the upper part 611 of Figure 33 contains seven peaks, including large and small ones, and the lower part 612 shows that the peak positions of six of these peaks match the peak positions of the predicted property data.

[0312] On the other hand, Figure 34 shows XRD data and predicted property data in which the peak positions are determined to be inconsistent. The XRD data corresponding to the experimental property values ​​shown in the upper part 613 of Figure 34 contains seven peaks, both large and small, but the lower part 614 shows that only two of these peaks match the peak positions of the predicted property data. This suggests, for example, that the process conditions may not be optimal, or that the type of material described above has not been identified.

[0313] Figure 35 shows an example of an image showing the transition of the display screen. This allows the user to easily analyze and consider the results by changing the display results according to the degree of agreement between the property prediction data and the XRD data, and allows the user to consider possible next experimental conditions. In other words, research efficiency is also improved.

[0314] (Sixth embodiment: explanation of operation) Next, the operation of the characteristic display device 600 will be described.

[0315] (flowchart) 36 is a flowchart showing the overall flow of processing by the characteristic display device 600 according to the sixth embodiment of the present disclosure. The processing from steps S601 to S605 and the processing from steps S608 to S609 are the same as the processing from steps S101 to S105 and the processing from steps S107 to S108 in FIG. 16, and therefore description thereof will be omitted.

[0316] (Step S606) The predicted data acquisition unit 601 acquires, from the predictor database 2 , characteristic prediction data corresponding to the compound identification information acquired by the data acquisition unit 104 , and outputs the data to the data image generation unit 105 .

[0317] (Step S607) The data image generation unit 105 generates an eighth image based on the compound information data acquired from the data acquisition unit 104 and the property prediction data acquired from the prediction data acquisition unit 601. The compound information data includes, for example, XRD data as compound graph data. The eighth image is, for example, a compound structure data image and a compound graph data image shown in FIG. 33 or 34.

[0318] (Step S610) The image layout determination unit 106 determines a layout in which the first image and / or the reduced first image and the eighth image are placed on one screen, generates a ninth image, and outputs it to the display unit 6.

[0319] (Step S611) The display unit 6 displays the ninth image acquired from the image layout determination unit 106.

[0320] <Summary of the Sixth Embodiment> As described above, in the sixth step of the characteristic display method of the sixth embodiment, the data acquisition unit 104 acquires compound information data associated with the compound identification information, and the prediction data acquisition unit 601 calculates characteristic prediction data corresponding to the compound identification information using the predictor database 2. Then, the data image generation unit 105 and the image layout determination unit 106 generate an eighth image based on the compound information data and the characteristic prediction data, and generate a ninth image including the eighth image and the first image, and output the ninth image to the display unit 6.

[0321] This allows, for example, the generation of an eighth image showing the difference between the compound information data and the property prediction data, and the eighth image can be displayed together with the first image on a single screen. For example, the difference can be a difference in peak position. This allows for more appropriate support of material development.

[0322] <Supplementary information> In each of the above embodiments, the number of categorical variables is four, but it may be more than 4. In other words, the number of categorical variables among the multiple variables, that is, the number of first variables into which option data indicating element species are substituted, may be four or more.

[0323] This allows maps to be generated for compounds composed of four or more elements, and by changing the combinations, maps showing information for many composition formulas (or chemical formulas) can be generated. In other words, maps showing predicted property values ​​for each compound expressed by many composition formulas (or chemical formulas) can be generated. This allows for more effective support of materials development.

[0324] In each of the above embodiments, the total number of discrete variables and continuous variables is four, but it may be more than 4. In other words, the number of discrete variables and continuous variables among the multiple variables, i.e., the number of second variables to which option data indicating values ​​are assigned, may be four or more.

[0325] This allows maps to be generated for compounds that are composed of a combination of four or more values ​​(i.e., element coefficients), and by changing the combinations, it is possible to generate maps that show information for many composition formulas (or chemical formulas). In other words, it is possible to generate maps that show predicted property values ​​for each of compounds expressed by many composition formulas (or chemical formulas). This allows for more effective support of materials development.

[0326] The map includes an image map configured with a first axis and a second axis corresponding to two second variables out of four or more second variables. The image map includes a plurality of image element maps arranged in a matrix along each of the first and second axes. Each of the plurality of image element maps is configured with a third axis and a fourth axis corresponding to the other two second variables out of the four or more second variables. Then, at a position on the image element map corresponding to the determined compound configuration, a predicted property value of the compound corresponding to the determined configuration is indicated by a color or a shade of color.

[0327] For example, the first and second axes corresponding to two second variables are the axis corresponding to the discrete variable a and the axis corresponding to the discrete variable b, as shown in Figure 4A. The third and fourth axes corresponding to the other two second variables are the axis corresponding to the continuous variable x and the axis corresponding to the continuous variable y, as shown in Figure 4A. In the example shown in Figure 4A, the predicted characteristic values ​​are indicated by the intensity of black and white.

[0328] This makes it possible to generate a map in which the predicted property values ​​of each combination, that is, the compounds expressed by each composition formula (or each chemical formula), are easy to view, even if the number of second variables is four or more.

[0329] The predictor in the predictor database 2 in each of the above embodiments may be a machine learning model. That is, in the second step described above, the characteristic value acquisition unit 101 inputs the determined compound structure into a machine learning model using a predetermined calculation algorithm, thereby acquiring a predicted property value of the compound corresponding to the compound structure. This machine learning model is a model trained by machine learning so as to output a predicted property value of a compound corresponding to the determined compound structure in response to the input of the compound structure.

[0330] For example, by performing machine learning on a machine learning model using many combinations of option data and experimental characteristic values ​​for those combinations as training data, a machine learning model with high prediction accuracy can be generated. Therefore, by using the machine learning model, it is possible to obtain highly accurate characteristic prediction values.

[0331] <Other aspects> While the display systems 1000 to 6000 according to the present disclosure have been described above based on the embodiments, the present disclosure is not limited to these embodiments. As long as they do not deviate from the spirit of the present disclosure, various modifications conceivable by those skilled in the art to the present embodiments, and configurations constructed by combining components of different embodiments, are also included within the scope of the present disclosure.

[0332] For example, the characteristic display device in each of the above embodiments is a part of a display system, but may include all of the components of the display system. For example, the characteristic display device may include an input unit 1, a predictor database 2, an experiment database 3, etc. The characteristic display device may include multiple processors. The characteristic display device may be configured as a single computer device, or may be composed of multiple computer devices connected to each other so that they can communicate with each other. In other words, the multiple components included in the characteristic display device in each of the above embodiments may not be provided in a single, identical device, but may be distributed and arranged in different devices.

[0333] In each of the above embodiments, each component may be configured with dedicated hardware, or may be realized by executing a software program appropriate for that component. Each component may be realized by a program execution unit such as a CPU or processor reading and executing a software program recorded on a recording medium such as a hard disk or semiconductor memory. Here, a program for realizing the characteristic display device of each of the above embodiments may cause a processor to execute each step included in at least one of the flowcharts shown in Figures 16, 20, 23, 27, 31, and 36, for example.

[0334] (Hardware configuration) Specifically, the above-mentioned display systems 1000 to 6000 may be configured as a computer system including a microprocessor, a ROM (Read Only Memory), a RAM (Random Access Memory), a hard disk drive, a display unit, a keyboard, a mouse, etc. A display program is stored in the RAM or the hard disk drive. The display systems 1000 to 6000 achieve their functions by the microprocessor operating in accordance with the display program. Here, the display program is configured by combining multiple instruction codes that indicate commands to the computer to achieve a predetermined function.

[0335] Furthermore, some or all of the components constituting the above-described display systems 1000 to 6000 may be configured as a single system LSI (Large Scale Integration). A system LSI is an ultra-multifunctional LSI manufactured by integrating multiple components on a single chip, and specifically, is a computer system configured including a microprocessor, ROM, RAM, etc. A computer program is stored in the RAM. The system LSI achieves its functions when the microprocessor operates in accordance with the computer program.

[0336] Some or all of the components constituting the above display systems 1000 to 6000 may be configured as an IC card or a standalone module that can be attached to a computer. The IC card or module is a computer system composed of a microprocessor, ROM, RAM, etc. The IC card or module may include the above-mentioned ultra-multifunctional LSI. The IC card or module achieves its functions when the microprocessor operates according to a computer program. This IC card or module may be tamper-resistant.

[0337] The present disclosure may also be a display method executed by the above-described display systems 1000 to 6000. This display method may be realized by a computer executing a display program, or may be realized by a digital signal comprising the display program.

[0338] Furthermore, the present disclosure may be configured such that the display program or the digital signal is stored in a computer-readable non-transitory recording medium. Examples of the recording medium include a flexible disk, a hard disk, a CD-ROM, an MO, a DVD, a DVD-ROM, a DVD-RAM, a Blu-ray (registered trademark) Disc (BD), and a semiconductor memory. The display program may be stored in a non-transitory recording medium.

[0339] The present disclosure may be configured by transmitting the display program or the digital signal via a telecommunications line, a wireless or wired communication line, a network such as the Internet, or data broadcasting.

[0340] The present disclosure may be a computer system including a microprocessor and a memory, the memory storing a display program, and the microprocessor operating in accordance with the display program.

[0341] The display program or the digital signal may be implemented by another independent computer system by recording it on the non-transitory recording medium and transferring it, or by transferring the display program or the digital signal via the network or the like.

[0342] The display system may be configured with a server and a terminal carried by a user that is connected to the server via a network. [Industrial Applicability]

[0343] The present disclosure is useful when displaying experimental values ​​or predicted values ​​of material properties in a search range, improving traceability, and utilizing predictions based on machine learning or the like in material development. [Explanation of symbols]

[0344] 1 Input section 2 Predictor Database (DB) 3 Experimental Database (DB) 4 Selection reception section 5. Compound Information Database (DB) 6 Display section 21 Editing information input section 31 Save Input Section 32 Reading input section 33 Storage Database (DB) 41 Correction input section 42 Converter database (DB) 51 Prediction model selection reception unit 100, 200, 300, 400, 500, 600 characteristic display device 101 Characteristic value acquisition unit 102 Image generation unit 103 Reduced image generation unit 104 Data Acquisition Unit 105 Data image generation unit 106 Image placement determination unit 201 Edited image generation unit 301 Saved Data Generation Unit 302 Stored Data Reconstruction Unit 401 Data Correction Department 402 Corrected image generation unit 501 Prediction model image generation unit 601 Prediction Data Acquisition Unit 1000, 2000, 3000, 4000, 5000, 6000 display systems

Claims

1. An information processing device comprising a memory and a processor, The processor uses the memory to: generating a list image that displays compound information associated with each of the plurality of experimental data in a list format; a step of accepting a selection of one of the compound information items displayed in the list image; acquiring the experimental data including process conditions and property information corresponding to the selected compound information; and displaying the acquired experimental data on a display unit. Information processing device.

2. The experimental data further includes one or more compound graph data; In the step of displaying the experimental data on a display unit, the processor displays the one or more compound graph data on the display unit. The information processing device according to claim 1 .

3. the processor further executes a step of displaying, on the display unit, a tab switching image that displays the plurality of pieces of experimental data corresponding to the plurality of pieces of compound information by switching tabs.

3. The information processing device according to claim 1.

4. The processor further executes a step of displaying prediction data indicating a predicted result of a property of each constituent of the compound, and the compound information corresponding to the plurality of experimental data is displayed together with the prediction data.

3. The information processing device according to claim 1.

5. the processor further executes a step of calculating difference information indicating a difference between the predicted data and the characteristic information included in the experimental data, and displaying the difference information. The information processing device according to claim 4 .

6. the processor further executes the step of saving the current display state as a file in a predetermined format.

3. The information processing device according to claim 1.

7. The processor further executes a step of acquiring the saved file, and reconstructing and displaying the display state based on the file. The information processing device according to claim 6 .

8. The process conditions include at least one of the actual composition of the compound information, raw materials, experimental procedures, firing conditions, shape and size of a crucible, type of equipment, and serial number of the equipment. The information processing device according to claim 1 .

9. The characteristic information includes at least one of impedance, oxidation potential, and crystalline phase of the compound information. The information processing device according to claim 1 .

10. When the experimental data includes graph data of a plurality of compounds, The processor, in displaying the one or more compound graph data, generating a graph superimposed image showing the plurality of compound graph data superimposed on one another and displaying the image on the display unit; The information processing device according to claim 2 .

11. 1. A computer-implemented information processing method, comprising: generating a list image that displays compound information associated with each of the plurality of experimental data in a list format; a step of accepting a selection of one of the compound information items displayed in the list image; acquiring the experimental data including process conditions and property information corresponding to the selected compound information; a step of displaying the acquired experimental data on a display unit; An information processing method including:

12. generating a list image that displays compound information associated with each of the plurality of experimental data in a list format; a step of accepting a selection of one of the compound information items displayed in the list image; acquiring the experimental data including process conditions and property information corresponding to the selected compound information; a step of displaying the acquired experimental data on a display unit; A program that causes a computer to execute the following.

Images