Prototype condition proposal system, prototype condition proposal method

Abstract

The present invention provides a technology with which it is possible to accurately propose favorable trial production conditions for materials even if the parameter space to be searched is wide with respect to the computational resources used. This trial production condition proposal system 1 is a system for proposing material trial production conditions to material developers, and is provided with a regression model construction processing unit 112 and a trial production condition proposal processing unit 113. The regression model construction processing unit 112 executes a regression model construction process on property measurement data representing actual measurement results of material properties. The trial production condition proposal processing unit 113 performs an optimization process to search for optimal trial production conditions for a material using the constructed regression model, and executes a trial production condition proposal process on the basis of the result of the optimization process.

Description

Technical Field

[0001] The present invention relates to a system and method for proposing prototype production conditions for materials to material developers.

Background Art

[0002] In the field of materials science where research and development of materials are conducted, a method called Materials Informatics (MI), which efficiently predicts physical properties, structures, etc. of materials by using information technologies such as statistical analysis and machine learning, is widely used today. Regarding the research and development of materials using this Materials Informatics, for example, the technique of Patent Document 1 is known. Patent Document 1 discloses a system for estimating production conditions of a substance having optimal physical properties and structure from a data set including production conditions of each of a plurality of substances used as samples and substance information representing physical properties and structures of each substance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the field of material development using Materials Informatics, usually, first, the characteristics of the material to be developed are actually measured by various evaluation tests, and data representing the actual measurement results (hereinafter referred to as "characteristic actual measurement data") is obtained. Next, this characteristic actual measurement data is input into a computer to construct various trained machine learning models. Then, using this trained machine learning model, conditions (hereinafter referred to as "prototype production conditions") for prototyping the material are estimated.

[0005] Incidentally, the aforementioned measured characteristic data usually contains a very large number of explanatory variables. When the number of explanatory variables is extremely large, the number of combinations of explanatory variables also becomes enormous. Therefore, when trying to estimate good prototype conditions for a material based on materials informatics, it is usually necessary to explore a very broad parameter space. However, there are generally limits to the computational resources that can be spent when having a computer perform such processing. For this reason, when trying to estimate good prototype conditions for a material with realistic computational resources and within a realistic computation time, the parameter space to be explored may be too broad for the available computational resources, and there is a risk that good prototype conditions cannot be estimated within a realistic computation time.

[0006] Furthermore, the aforementioned measured characteristic data often consists of a small number of samples, and the distribution of explanatory variables included in each measured characteristic data is often biased. Therefore, when exploring a parameter space far from the distribution region of explanatory variables included in the measured characteristic data input into the computer for building a machine learning model (hereinafter also referred to as "training data"), the estimation accuracy decreases compared to exploring the vicinity of the distribution region of those explanatory variables. Consequently, the region of the parameter space to be explored that can be explored with high accuracy is limited to the vicinity of the distribution region of the explanatory variables included in the training data. Therefore, even if the entire parameter space to be explored is thoroughly searched, it may not be possible to explore the entire parameter space with uniform accuracy, and thus good prototype conditions for the material may not be estimated.

[0007] In view of the above problems, the present invention aims to provide a technology that can accurately propose good prototype conditions for materials, even when the parameter space to be explored is extensive in terms of the computational resources used. [Means for solving the problem]

[0008] The prototype condition proposal system according to the present invention proposes prototype conditions for materials to material developers and comprises a regression model construction processing unit and a prototype condition proposal processing unit. The regression model construction processing unit performs a regression model construction process on property measurement data representing the measured results of material properties. The prototype condition proposal processing unit performs an optimization process to search for the optimal prototype conditions for the material using the constructed regression model and performs a prototype condition proposal process based on the results of the optimization process. Furthermore, the prototype condition proposal method according to the present invention is a method of proposing prototype conditions for a material to a material developer using a computer. This prototype condition proposal method involves having a computer perform a regression model construction process and a prototype condition proposal process. The regression model construction process represents the process of constructing a regression model for measured characteristic data that represents the measured results of the material's properties. The prototype condition proposal process represents the process of performing an optimization process to search for the optimal prototype conditions for the material using the constructed regression model, and proposing prototype conditions for the material based on the results of the optimization process. In the prototype condition proposal system and prototype condition proposal method according to the present invention, the optimization process is either (A) or (B) below. (A) The optimization process includes a prototype level candidate creation process that creates a specified number of prototype level candidates, a calculation process that calculates predicted values ​​or acquisition functions for all created prototype level candidates, and an extraction process that extracts the prototype conditions that have the best predicted values ​​or acquisition functions. (B) The optimization process includes a selection process for selecting an initial prototype level from a list of prototype level candidates whose characteristics have been measured; a penalty setting process for setting a penalty for deviations from predetermined constraints for the selected prototype level; and a process for optimizing the prototype conditions so that the predicted value or acquisition function obtained by adding or multiplying the set penalty is the best. Furthermore, in the prototype condition proposal system and prototype condition proposal method according to the present invention, if the optimization process is as described in (A) above, the prototype level candidate creation process includes a selection process for selecting a standard prototype level from prototype level candidates whose characteristics have been measured, a variation process for applying random number variations to the selected prototype level within the range in which the material can be prototyped, and a saving process for saving the prototype level candidate to which the variation process has been applied, which are repeated a specified number of times by the computer.

[0009] Furthermore, the problems disclosed in this application and their solutions will be made clear from the description in the section on embodiments for carrying out the invention and from the drawings. [Effects of the Invention]

[0010] According to the present invention, even if the parameter space to be explored is extensive in terms of the computational resources used, good prototype conditions for materials can be accurately proposed. [Brief explanation of the drawing]

[0011] [Figure 1] A schematic diagram showing an overview of a prototype condition suggestion system according to one embodiment of the present invention. [Figure 2] A diagram showing the functional blocks of a prototype condition suggestion system according to one embodiment of the present invention. [Figure 3]A flowchart showing the overall processing flow of a prototype condition proposal system according to one embodiment of the present invention. [Figure 4] A flowchart showing the details of preprocessing of measured characteristic data. [Figure 5] A flowchart illustrating the details of the regression model construction process. [Figure 6] A flowchart showing the details of the prototype condition proposal process. [Figure 7] A diagram illustrating the procedure for focused search. [Modes for carrying out the invention]

[0012] The present embodiment will be described in detail below. Figure 1 is a schematic diagram showing an overview of a prototype condition proposal system according to one embodiment of the present invention. The prototype condition proposal system 1 shown in Figure 1 optimizes various prototype conditions that should be considered when prototyping a material, such as material composition and firing conditions, and proposes the results to the material developer, who is the user of this system. This optimization of prototype conditions is performed using various machine learning algorithms based on measured property data that represents the measured properties of the material. In this case, various regression models such as Gaussian Process Regression (GPR), Linear Regression, Regression Tree (including the case of ensemble method), Neural Network Regression, Support Vector Regression (SVR), Logistic Regression, and LASSO Regression (Least Absolute Shrinkage and Selection Operator Regression) are used as prediction models.

[0013] As shown in FIG. 1, when a user of the prototype condition proposal system 1 performs a prototype of a material under the prototype conditions proposed by the prototype condition proposal system 1 and actually measures the characteristics of the material, which is the resulting product, characteristic measurement data representing the evaluation value of the characteristics of this resulting product is newly generated. When this characteristic measurement data is used as new learning data to make the prototype condition proposal system 1 learn, the prototype condition proposal system 1 proposes more optimized prototype conditions to the user. The prototype condition proposal system 1 of the present embodiment can propose prototype conditions with more preferable predicted values of characteristics each time the user repeats this cycle. Note that the prototype condition proposal system 1 may include a function for prototyping a material or a function for actually measuring the characteristics of the material to be prototyped, or may be integrally configured with these functions.

[0014] As shown in FIG. 1, the prototype condition proposal system 1 of the present embodiment is realized by a single general-purpose computer device. The following description is made on the assumption that the prototype condition proposal system 1 is realized by a single general-purpose computer device including one or more processor devices, one or more storage devices, one or more input / output devices, and a wired or wireless communication line (none of which are shown in the drawings) connecting them.

[0015] This computer device is installed as a terminal, for example, inside a laboratory, and is connected via a communication network such as the Internet 400 or a dedicated line to various other terminals installed inside and outside the laboratory, various terminals such as laptop PCs, tablets, and smartphones (hereinafter referred to as "user terminals") owned by each user, and other devices such as a server device. Note that the computer device and the Internet 400 are connected by wire via well-known communication devices (not shown), but may also be connected wirelessly.

[0016] Next, various functions of the prototype condition proposal system 1 will be described with reference to FIG. 2. FIG. 2 is a diagram showing the functional blocks of the prototype condition proposal system 1 according to an embodiment of the present invention. Note that each block described below represents a functional unit block, not a hardware unit configuration. As shown in FIG. 2, the prototype condition proposal system 1 of the present embodiment includes a control unit 11, a storage unit 12, a user interface unit 13, and a communication unit 14.

[0017] The control unit 11 executes various data processes based on the user's operation input detected by the user interface unit 13, the data acquired by the communication unit 14, and the programs and data stored in the storage unit 12. The control unit 11 also functions as an interface for the user interface unit 13, the communication unit 14, and the storage unit 12.

[0018] The control unit 11 has functional blocks of a characteristic measurement data preprocessing unit 111, a regression model construction processing unit 112, and a prototype condition proposal processing unit 113. The control unit 11 is configured using a processor device such as a CPU (Central Processing Unit) and various coprocessors (hereinafter, also simply referred to as "processor"), and can realize these functional blocks by executing a predetermined program. Note that instead of the processor, the control unit 11 may be configured using a logic circuit such as an FPGA (Field Programmable Gate Array). Further, the control unit 11 may be configured by a combination of a processor and a logic circuit.

[0019] The program executed by the control unit 11 may be installed from a program source. The program source may be, for example, a program distribution computer or a computer-readable recording medium. Furthermore, the program executed by the control unit 11 may consist of a device driver, an operating system, various application programs located at a higher layer, and libraries that provide common functions to these programs. In addition, two or more programs may be implemented as a single program, or one program may be implemented as two or more programs.

[0020] The characteristic measurement data preprocessing unit 111 performs preprocessing on the characteristic measurement data in its raw state immediately after recording. This processing performed by the characteristic measurement data preprocessing unit 111 is called characteristic measurement data preprocessing.

[0021] The regression model construction processing unit 112 executes a process to construct a regression model for the pre-processed measured characteristic data. This process performed by the regression model construction processing unit 112 is called the regression model construction process.

[0022] The prototype condition proposal processing unit 113 performs an optimization process to search for the optimal prototype conditions for the material using the regression model constructed by the regression model construction processing unit 112 on the pre-processed characteristic measurement data, and then executes a process to propose prototype conditions for the material to the user based on the results of the optimization process. This process performed by the prototype condition proposal processing unit 113 is called the prototype condition proposal process.

[0023] The specific details of these processes will be described later.

[0024] The memory unit 12 is configured using a storage device such as RAM or flash memory, and stores programs that supply various processing instructions to the control unit 11, as well as data representing various information used in the processing executed by the control unit 11. For example, the memory unit 12 stores characteristic measurement data that has been preprocessed by the characteristic measurement data preprocessing unit 111 (hereinafter referred to as "preprocessed data"), and data representing the regression model constructed by the regression model construction processing unit 112. The control unit 11 can realize the aforementioned functional blocks of the characteristic measurement data preprocessing unit 111, the regression model construction processing unit 112, and the prototype condition proposal processing unit 113 by reading and writing this information to the memory unit 12.

[0025] The user interface unit 13 accepts input operations from the user and is responsible for processing related to the user interface, such as displaying images and outputting sound. The user interface unit 13 has functional blocks for input unit 131 and output unit 132. The input unit 131 detects various operations from the user. The input unit 131 is configured using, for example, a keyboard, pointing device, or touch panel. The output unit 132 performs actions such as displaying images on the screen or outputting sound to the user. The output unit 132 is configured using, for example, a liquid crystal display or touchscreen.

[0026] The communications unit 14 is responsible for processing communication between user terminals owned by each user and other devices such as server equipment, which takes place via the Internet 400. The communications unit 14 is configured using, for example, a NIC (Network Interface Card) or an HBA (Host Bus Adapter).

[0027] In this embodiment, each function of the prototype condition suggestion system 1 has been described as being integrated and implemented by a single computer device. However, these functions may be implemented by multiple interconnected computer devices or server devices. Furthermore, the prototype condition suggestion system 1 may consist of a general-purpose computer device such as a laptop PC and a web browser installed thereon, or it may consist of a web server and various portable devices.

[0028] Furthermore, the descriptions of each function are merely examples, and multiple functions may be combined into one function, or one function may be divided into multiple functions.

[0029] Next, the overall processing flow of the prototype condition suggestion system 1 will be explained with reference to Figure 3. Figure 3 is a flowchart showing the overall processing flow of the prototype condition suggestion system 1 according to one embodiment of the present invention. In the following explanation, the processing may be described using each of the aforementioned functions or programs as the subject, but the processing described using a function or program as the subject may also be processing performed by a processor or a device having such a processor.

[0030] In step S310, the control unit 11 performs preprocessing of the measured characteristics data using the measured characteristics data preprocessing unit 111. This preprocesses the measured characteristics data, making it preprocessed data, which allows subsequent processes to be executed normally. Details of the measured characteristics data preprocessing performed in step S310 will be explained later with reference to the flowchart in Figure 4. Once the measured characteristics data preprocessing is complete, the control unit 11 proceeds to step S320.

[0031] In step S320, the control unit 11 executes the regression model construction process using the regression model construction processing unit 112. This constructs a regression model for the pre-processed data. Details of the regression model construction process performed in step S320 will be explained later with reference to the flowchart in Figure 5. Once the regression model construction process is complete, the control unit 11 proceeds to step S330.

[0032] In step S330, the control unit 11 executes a regression model evaluation process. In this regression model evaluation process, the generalization performance, which is an indicator representing the prediction accuracy of the regression model, is evaluated for each of the multiple regression models constructed as a result of the processes up to step S320. This evaluation is performed, for example, by performing cross-validation with other regression models. The evaluation results are visualized using graphs such as scatter plots and box plots. This allows the user to receive suggestions for prototype conditions based on regression models with good generalization performance. Once the regression model evaluation process is complete, the control unit 11 proceeds to step S340.

[0033] In step S340, the control unit 11 executes a prototype condition proposal process using the prototype condition proposal processing unit 113. In this prototype condition proposal process, the user of the prototype condition proposal system 1 can modify the prototype conditions for the material proposed by the prototype condition proposal system 1 as needed to make them even more favorable. The control unit 11 obtains predicted values ​​for the material properties when prototyped using the modified prototype conditions by the user by applying them to a selected regression model, and presents them to the user. In other words, the user can interactively modify the prototype conditions while confirming the predicted values. Thus, the prototype condition proposal system 1 is a system that can incorporate the knowledge of the user, who is the developer of the material, into the prototype conditions for the material proposed to the user. Details of the prototype condition proposal process performed in step S340 will be explained later with reference to the flowchart in Figure 6. Once the prototype condition proposal process is completed, the control unit 11 terminates the process shown in the flowchart in Figure 3.

[0034] The prototype condition suggestion system 1 of this embodiment performs the processes in steps S310 to S340 of Figure 3 to suggest good prototype conditions to the user. Specifically, in step S310 of the prototype condition suggestion system 1 of this embodiment, the necessary preprocessing is automatically applied to the measured characteristic data in its raw state. Therefore, the processes in steps S320 to S340 can be executed without the need for manual, complex preprocessing of the measured characteristic data. Furthermore, in the prototype condition suggestion system 1 of this embodiment, the user can select a regression model with good generalization performance. Therefore, the prototype condition suggestion system 1 can suggest prototype conditions to the user that result in good predicted characteristic values.

[0035] As mentioned above in relation to Figure 1, the user of the prototype condition proposal system 1 can perform a prototype of a material using the prototype conditions proposed by the prototype condition proposal system 1, measure the properties of the resulting product, and then train the prototype condition proposal system 1 with the data representing the measurement results as new property measurement data. After that, the user can have the prototype condition proposal system 1 perform each of the steps S310 to S340 in Figure 3 again. In this case, the prototype condition proposal system 1 can propose more optimized prototype conditions to the user. That is, the prototype condition proposal system 1 of this embodiment can propose prototype conditions with better predicted property values ​​each time the steps S310 to S340 in Figure 3 are repeated for the same prototype target.

[0036] Figure 4 is a flowchart showing the details of the preprocessing of measured characteristic data.

[0037] In step S410, the control unit 11 receives the input of the measured characteristics data from the user via the input unit 131 or the communication unit 14 using the measured characteristics data preprocessing unit 111. The measured characteristics data to be input to the prototype condition proposal system 1 may be, for example, categorical data, continuous data, or discrete data. The specific data format of the measured characteristics data to be input to the prototype condition proposal system 1 can be determined as appropriate. Once the processing in step S410 is complete, the control unit 11 proceeds to step S420.

[0038] In step S420, the control unit 11 sets the type of variable for the measured characteristic data input from the user in step S410 using the characteristic measurement data preprocessing unit 111. Here, either an explanatory variable or a target variable is set. An explanatory variable is a variable that forms the basis for determining the predicted value of the characteristic. In this embodiment, the composition of the material constituting the prototype conditions and the firing conditions are examples of explanatory variables. A target variable is a variable that represents the characteristic value of the prototype material that is the subject of prediction. As an example of the specific processing in step S420, the explanatory variable may be set by default, and the control unit 11 may accept a setting operation from the user who wishes to change it to the target variable. Once the processing in step S420 is complete, the control unit 11 proceeds to step S430.

[0039] In step S430, the control unit 11 uses the characteristic measurement data preprocessing unit 111 to determine whether there are any outliers in the characteristic measurement data for which one of the explanatory variables and the objective variable was set in step S420. This determination of the presence or absence of outliers is performed, for example, by first determining whether there are outliers outside the range of mean ± 2σ by representing the characteristic measurement data as a histogram, and then determining whether there were any data input errors or malfunctions in the evaluation test machine when generating the characteristic measurement data for which outliers were determined to be present. If the control unit 11 determines that the characteristic measurement data contains outliers, it deletes the outliers and makes them missing values, and proceeds to step S440. Furthermore, if the type of outlier included in the characteristic measurement data is one of the objective variables in a case where the explanatory variables completely overlap but there are levels with different characteristics, the control unit 11 deletes the sample related to this outlier and proceeds to step S440. However, when treating levels with different characteristics where all explanatory variables overlap as outliers, it is necessary to delete the level itself, unlike when only one explanatory variable is outlier due to an input error or the like. On the other hand, if the control unit 11 determines that the measured characteristic data does not contain outliers, it proceeds to step S440.

[0040] Furthermore, in cases where all the explanatory variables overlap and there are levels with different characteristics, it may be desirable to retain both explanatory variables, assuming that the overlap is meaningful. In such cases, the prototype condition proposal system 1 of this embodiment can omit the processing in step S430.

[0041] In step S440, the control unit 11 determines whether or not there are missing values ​​in the measured characteristic data using the pre-processing unit 111. This is because the measured characteristic data may already contain missing values. If the control unit 11 determines that the pre-processed data contains missing values, it imputes the missing values ​​and proceeds to step S450. The imputation of missing values ​​is performed, for example, by using the mean, median, minimum, or maximum value of the measured characteristic data (excluding outliers) as the value to impute the missing values. Alternatively, the imputation of missing values ​​may be performed by linear interpolation. The pre-processing unit 111 may, for example, display the imputed value in red when it imputes missing values ​​in step S440, to make it easier to identify. Alternatively, the pre-processing unit 111 may, for example, delete the level itself without imputing the missing value in step S440. Furthermore, in the prototype condition proposal system 1 of this embodiment, the characteristic measurement data preprocessing unit 111 can also delete the explanatory variables themselves if there is a large percentage of missing data in the explanatory variables, for example, if more than 50% of the data is missing. On the other hand, if it is determined that the characteristic measurement data does not contain any missing values, the control unit 11 proceeds to step S450.

[0042] In step S450, the control unit 11, using the characteristic measurement data preprocessing unit 111, performs encoding processing on the explanatory variable if it is a categorical value rather than a continuous value, converting it into numerical data. The characteristic measurement data preprocessing unit 111 performs this encoding processing by, for example, referring to a record in a table stored in the storage unit 12 that represents the correspondence between categorical data and numerical data. Once the processing in step S450 is complete, the control unit 11 proceeds to step S460.

[0043] In step S460, the control unit 11 determines, using the characteristic measurement data preprocessing unit 111, whether or not the characteristic measurement data contains redundant explanatory variables. This determination is based on whether or not it is possible to extract combinations of explanatory variables whose correlation coefficient is greater than or equal to a predetermined number, for example, 0.8 or more. If it is determined that the characteristic measurement data contains redundant explanatory variables, the control unit 11 deletes one of the redundant explanatory variables and proceeds to step S470. In this embodiment of the prototype condition proposal system 1, combinations of explanatory variables whose correlation coefficient is 0.8 or more are visualized to the user through the output unit 132, and the user can select the explanatory variable to delete through the input unit 131. On the other hand, if it is determined that the characteristic measurement data does not contain redundant explanatory variables, the control unit 11 proceeds directly to step S470.

[0044] In step S470, the control unit 11 uses the characteristic measurement data preprocessing unit 111 to perform standardization processing on the characteristic measurement data as necessary. This standardization processing transforms the scale of the characteristic measurement data so that the mean = 0 and the standard deviation (variance) = 1. Once the processing in step S470 is complete, the control unit 11 stores the preprocessed characteristic measurement data, which has been preprocessed in steps S410 to S470 of Figure 4, into the storage unit 12, and terminates the characteristic measurement data preprocessing shown in the flowchart of Figure 4. The control unit 11 may also use the characteristic measurement data preprocessing unit 111 to perform normalization processing on the characteristic measurement data as necessary. In such cases, the standardization processing in S470 may be omitted, and the subsequent processes may be executed.

[0045] Figure 5 is a flowchart showing the details of the regression model construction process.

[0046] In step S510, the control unit 11 selects the cross-validation conditions for evaluating the regression model to be constructed on the pre-processed data, using the regression model construction processing unit 112. In this embodiment, the prototype condition proposal system 1 performs evaluation for each regression model using K-fold cross-validation. The default value for this is set to K=10. In this case, the prototype condition proposal system 1 evaluates the regression model using 10-fold cross-validation. In this embodiment, the prototype condition proposal system 1 also allows the user to select the cross-validation conditions. That is, the regression model construction processing unit 112 can receive cross-validation conditions from the user via the input unit 131 or the communication unit 14. Once the processing in step S510 is complete, the control unit 11 proceeds to step S520.

[0047] In step S520, the control unit 11 uses the regression model construction processing unit 112 to select candidate regression models to be used as predictive models for exploring prototype conditions. At this time, the regression model construction processing unit 112 selects regression models as candidates that have been selected by the user via the input unit 131 or the communication unit 14. In the prototype condition proposal system 1 of this embodiment, the user can select multiple regression models as candidates from Gaussian process regression and various regression models such as linear regression, regression trees (including cases using ensemble methods), neural network regression, support vector regression, logistic regression, and LASSO regression. Once the processing in step S520 is complete, the control unit 11 proceeds to step S530.

[0048] In step S530, the control unit 11 uses the regression model construction processing unit 112 to calculate the weighting criteria, which are the basis for weighting the measured characteristic data. The weighting criteria are calculated based on the difference between the objective variable included in the measured characteristic data and the target characteristic, which represents the target value of the material's properties, or on a statistic that represents the rarity of the explanatory variables included in the measured characteristic data. A specific example of a statistic that represents the rarity of an explanatory variable is the probability of an explanatory variable that satisfies predetermined conditions. Once the processing in step S530 is complete, the control unit 11 proceeds to step S540.

[0049] In step S540, the control unit 11 executes a process in which the regression model construction processing unit 112 weights the measured characteristic data based on the weight criteria calculated in step S530. In addition, in the prototype condition proposal system 1 of this embodiment, the regression model construction processing unit 112 can also directly weight the regression model based on the weight criteria calculated in step S530. Specifically, the process that the regression model construction processing unit 112 executes in step S540 based on the results of step S530 is one of the following: setting a loss function, which is a function that serves as an indicator for learning; oversampling, which amplifies rare or highly important measured characteristic data and adds it as training data; or undersampling, which removes redundant or less important measured characteristic data from the training data. By executing these processes, the weights of the training data are appropriately adjusted even when the number of training data used to construct the machine learning model is small or when there is a bias in the distribution of the training data. As a result, estimation accuracy is improved, and prototype conditions for good materials can be proposed with accuracy. When the processing in step S540 is completed, the control unit 11 proceeds to step S550.

[0050] In step S550, the control unit 11 uses the regression model construction processing unit 112 to search for and set the optimal hyperparameters for each regression model of the method selected as a candidate in step S520. In the prototype condition proposal system 1 of this embodiment, for each regression model, the regression model construction processing unit 112 automatically searches for all parameters and automatically sets the ones that will best improve the generalization performance of the regression model when set as hyperparameters during the construction of the regression model. Once the processing in step S550 is complete, the control unit 11 proceeds to step S560.

[0051] In step S560, the control unit 11 performs the process of creating regression models for each method with optimal hyperparameters set by the regression model construction processing unit 112. After creating regression models for each method, the regression model construction processing unit 112 selects the regression model with the highest generalization performance from all the created regression models to determine the final regression model. Once the processing in step S560 is complete, the control unit 11 terminates the regression model construction process shown in the flowchart of Figure 5.

[0052] Figure 6 is a flowchart showing the details of the prototype condition proposal process.

[0053] In step S610, the control unit 11 uses the prototype condition proposal processing unit 113 to perform a search for prototype conditions based on the regression model constructed in step S560 of Figure 5. The prototype condition proposal processing unit 113 performs this process using optimization (details will be described later). The prototype condition proposal system 1 of this embodiment is configured to use various optimization methods such as mathematical optimization (MO), Bayesian optimization (BO), genetic algorithm (GA), Newton's method (NM), and simplex method (SM). As a result, each explanatory variable that yields the best predicted value of the target variable (characteristic) is proposed to the user as a provisional prototype condition representing the result of the search process. At this time, the prototype condition proposal processing unit 113 also performs a sensitivity analysis on the provisional prototype condition to evaluate the importance of each explanatory variable constituting the provisional prototype condition and presents the evaluation results. In addition, in the prototype condition suggestion system 1 of this embodiment, if the regression model used is a Gaussian process regression, it is also possible to select the prototype conditions that maximize the acquisition function. When the processing in step S610 is completed, the control unit 11 proceeds to step S620.

[0054] In step S620, the control unit 11 receives a modification request from the user to the provisional prototype conditions proposed to the user in step S610 via the prototype condition proposal processing unit 113. The prototype condition proposal processing unit 113 receives an input operation from the user via the input unit 131 or the communication unit 14 regarding the modification of the values ​​of each explanatory variable constituting the provisional prototype conditions, and modifies the provisional prototype conditions according to the modification. At this time, the control unit 11 uses the prototype condition proposal processing unit 113 to obtain predicted values ​​for the material properties when prototypes are made with the modified provisional prototype conditions using a regression model, and presents the calculation results to the user. Also at this time, similar to step S610, the prototype condition proposal processing unit 113 performs a sensitivity analysis on the modified provisional prototype conditions to evaluate the importance of the explanatory variables constituting the modified provisional prototype conditions, and presents the evaluation results as well. This evaluation result is updated each time the user modifies the provisional prototype conditions, so that the latest evaluation results are always presented to the user. When the processing in step S620 is completed, the control unit 11 proceeds to step S630.

[0055] In step S630, the control unit 11, using the prototype condition proposal processing unit 113, determines whether the predicted values ​​of the characteristics obtained in step S620 are insufficient as characteristic values ​​for the material to be prototyped for the modified provisional prototype conditions. This determination is made, for example, if the regression model used is a Gaussian process regression, by obtaining an acquisition function for each prototype condition that represents the expected value by which the material's characteristics will be improved when prototyped under that condition, and determining whether the difference between the value of the acquisition function and the maximum value of the acquisition function is within a predetermined range. The acquisition function is calculated based on the predicted value μ of the material's characteristics when prototyped under any given prototype condition and the standard deviation σ that represents the variability of the predicted value. If it is determined that the predicted characteristic values ​​are insufficient, the process returns to step S620 to receive instructions from the user to modify the prototype conditions again. If it is determined that the predicted characteristic values ​​are not insufficient, the predicted values ​​of the material properties when prototyping with the modified provisional prototype conditions are considered sufficient, and the provisional prototype conditions are finalized and proposed to the user as finalized prototype conditions. In other words, this determination process is repeated until it is determined that the predicted values ​​of the material properties related to the provisional prototype conditions are not insufficient. Once the processing in step S630 is completed, the control unit 11 terminates the prototype condition proposal process shown in the flowchart of Figure 6.

[0056] The prototype condition suggestion processing unit 113 may perform optimization processing using a sampling method or perform optimization processing continuously in step S610. The sampling method is a method of generating multiple prototype level candidates and selecting the prototype level candidate with the best characteristics. In the prototype condition suggestion system 1 of this embodiment, the user can choose between these two execution methods when executing the optimization processing in step S610. Of these, the specific details of the processing performed in step S610 when the prototype condition suggestion processing unit 113 performs optimization processing using a sampling method are shown below as steps S611 to S616.

[0057] In step S611, the control unit 11 receives an input operation from the user via the input unit 131 to specify the number of prototype level candidates to be created. The prototype condition proposal processing unit 113 then performs a process to create the specified number of prototype level candidates. This process is called the prototype level candidate creation process. The prototype level candidate creation process includes a process that causes the computer to repeatedly execute each of the processes in steps S614 to S616, which will be described later, a specified number of times. As a result of the prototype level candidate creation process, the number of prototype level candidates specified by the user is created. In the prototype condition proposal system 1 of this embodiment, 10,000 prototype level candidates are created according to the user's specifications. When the process in step S611 is completed, the control unit 11 proceeds to step S612.

[0058] In step S612, the control unit 11 performs a process to calculate predicted values ​​or acquisition functions for all prototype level candidates created in step S611 using the prototype condition proposal processing unit 113. This process is called the calculation process. As a result of the calculation process, predicted values ​​or acquisition functions are calculated for all prototype level candidates created in step S611. Once the processing in step S612 is complete, the control unit 11 proceeds to step S613.

[0059] In step S613, the control unit 11, using the prototype condition proposal processing unit 113, extracts the prototype condition with the best predicted value or acquisition function from all the prototype level candidates created in step S611, based on the predicted value or acquisition function calculated in step S612. This process is called the extraction process. The prototype condition proposal processing unit 113 performs the extraction process in step S613 using the various optimization processing methods described above in relation to step S610. As a result of the extraction process, the prototype level candidate with the best predicted value or acquisition function is extracted as the prototype condition from all the prototype level candidates created in step S611. When the processing in step S613 is completed, the control unit 11 terminates the optimization processing to be performed in step S610.

[0060] Furthermore, the prototype candidate creation process performed in step S611 includes, as described above, the process of having the computer repeatedly execute each of the processes in steps S614 to S616, which will be described later, a specified number of times.

[0061] In step S614, the control unit 11, using the prototype condition proposal processing unit 113, performs a process to select a reference prototype level from prototype level candidates whose characteristics have been measured. This process is called the selection process. In the selection process, measured characteristic data is used as prototype level candidates whose characteristics have been measured. That is, in the selection process, first, measured characteristic data with good characteristics are extracted from multiple measured characteristic data. Next, a selection probability is calculated so that the extracted measured characteristic data with good characteristics is selected preferentially as prototype conditions. Furthermore, measured characteristic data is randomly selected according to the calculated selection probability. The combination of raw materials in this selected measured characteristic data is set as a prototype level candidate. In this way, in the selection process, the reference prototype level is randomly selected according to a selection probability calculated so that those with good characteristics are selected preferentially as prototype conditions from among the measured characteristic data that are considered prototype level candidates. This makes it easier for prototype level candidates with good characteristics to be selected as prototype conditions. Furthermore, the selection process may include a process for weighting the selection probability of the standard prototype level. In this case, weighting makes it easier to select a prototype level that is preferable as a standard. As a result of the selection process, the combination of raw materials in the candidate standard prototype level is determined. When the process in step S614 is completed, the control unit 11 proceeds to step S615.

[0062] In step S615, the control unit 11, using the prototype condition proposal processing unit 113, performs a process to randomly vary the prototype level selected in step S614 within the range in which the material can be prototyped. This process is called the variation process. In this variation process, first, the composition ratio of the raw materials in the candidate prototype level is randomly set while adhering to the constraint of the total value. Next, the average particle size and the maximum firing temperature in the candidate prototype level are calculated based on the raw material combination determined in step S614 and the composition ratio of the raw materials determined in step S615. Subsequently, other explanatory variables are set according to the distribution of the explanatory variables included in the measured data of each characteristic. As a result of the variation process, the values ​​of various explanatory variables, including the composition ratio of the material, in the candidate prototype level to be used as a reference are determined. Once the processing in step S615 is complete, the control unit 11 proceeds to step S616.

[0063] In step S616, the control unit 11 performs a process to save the prototype level candidates that have undergone variation processing in step S615, using the prototype condition proposal processing unit 113. This process is called the saving process. As a result of the saving process, the prototype level candidates are saved by being written to the storage unit 12. Once the processing in step S616 is completed, the control unit 11 returns to step S614 and executes each of the processes in steps S614 to S616 described above again. Each of the processes in steps S614 to S616 is executed repeatedly a specified number of times. In the prototype condition proposal system 1 of this embodiment, 10,000 times is specified as the number of times each of the processes in steps S614 to S616 is executed repeatedly. Therefore, each of the processes in steps S614 to S616 is executed repeatedly 10,000 times. As a result, in the prototype level candidate creation process performed in step S611, the necessary and sufficient prototype level candidates are created in order to accurately extract prototype conditions with good characteristics. When the control unit 11 has repeatedly executed each of the processes in steps S614 to S616 10,000 times, it terminates the prototype level candidate creation process in step S611 and proceeds to step S612.

[0064] Furthermore, the prototype level candidate creation process executed in step S611 may further include a determination process to determine whether or not a standard prototype level exists, and a standard generation process to generate a standard using random numbers if the determination process determines that there is no standard. In this case, even if a standard does not exist, a prototype level candidate can be created in the same way as if a standard existed by generating a standard using random numbers.

[0065] On the other hand, when the prototype condition proposal processing unit 113 performs optimization processing continuously, the specific details of the processing performed in step S610 are shown below as steps S617 to S619.

[0066] In step S617, the control unit 11, using the prototype condition proposal processing unit 113, performs a process to select an initial prototype level from among candidate prototype levels whose characteristics have been measured. This process is called the selection process. In the selection process, measured characteristic data is used as candidate prototype levels whose characteristics have been measured. That is, in the selection process, the measured characteristic data to be used as the initial value of the prototype level is selected from multiple measured characteristic data. In the selection process, the prototype level to be the initial value is selected randomly. This makes it less likely that the region in the parameter space being searched will be biased when searching for prototype conditions in step S610. The selection process may also include a process to weight the selection probability of the initial value prototype level. In this case, by weighting in this way, it becomes easier to select a prototype level that is preferable as the initial value. When the process in step S617 is completed, the control unit 11 proceeds to step S618.

[0067] In step S618, the control unit 11 performs a process to set a penalty for the selected prototype level if it deviates from predetermined constraints, using the prototype condition proposal processing unit 113. This process is called the penalty setting process. As a method of setting the penalty, if the penalty is added in step S619, a very large negative number is assigned as the evaluation value related to the penalty. Also, if the penalty is multiplied in step S619, 0 is assigned as the evaluation value related to the penalty. In the prototype condition proposal system 1 of this embodiment, if the selected prototype level deviates from predetermined constraints, in the initial setting, 0 is assigned as the evaluation value related to the penalty in step S618. Then, by multiplying by 0 in step S619, the prototype level is ignored. In addition, in the prototype condition proposal system 1 of this embodiment, the constraint condition that "the material can be prototyped," or in other words, "only the range and combination of parameters that can prototype the material are allowed," is set in advance. As a result, constraints based on materials science are imposed, so the prototype condition proposal system 1 can propose only prototype conditions that can actually be used to prototype the material. Other specific examples of constraints include "focusing the search on existing measured property data that is close to the target physical properties" and "focusing the search on prototype conditions that are considered effective from a materials science perspective." Note that there only needs to be one or more pre-set constraints. That is, one constraint may be set alone, or a combination of multiple constraints may be set. The prototype condition proposal processing unit 113 may also automatically adjust the pre-set constraints and their internal parameters (for example, weights for performing focused searches) so that the obtained predicted values ​​and acquisition functions are best. When the processing in step S618 is completed, the control unit 11 proceeds to step S619.

[0068] In step S619, the control unit 11 performs a process to optimize the prototype conditions using the prototype condition proposal processing unit 113 so that the predicted value or acquisition function obtained by adding or multiplying the penalty set in step S618 is the best. The prototype condition proposal processing unit 113 performs this process in step S619 using the various optimization processing methods described above in relation to step S610. The prototype condition proposal processing unit 113 also repeats this process in step S619 a number of times specified by the user. In the prototype condition proposal system 1 of this embodiment, 10,000 times is specified as the number of times the process in step S619 is repeated. Therefore, the optimization process is repeated 10,000 times in step S619. When the process in step S619 is completed, the control unit 11 terminates the optimization process to be performed in step S610.

[0069] Furthermore, similar to the sampling method, the optimization process performed in step S610 may further include a determination process to determine whether or not a standard prototype level exists, and a standard generation process to generate a standard using random numbers if the determination process determines that there is no standard. In this case, even if a standard does not exist, a candidate prototype level can be created in the same way as if a standard existed by generating a standard using random numbers.

[0070] Thus, the optimization process performed in step S610 is carried out either by a sampling method or continuously. When the optimization process is carried out by a sampling method, the prototype level candidate with the best characteristics is selected from multiple prototype level candidates, so the prototype condition proposal system 1 can estimate the best prototype conditions for the material and propose them as provisional prototype conditions. Also, when the optimization process is carried out continuously, the prototype levels are refined compared to when the optimization process is carried out in a single step, so the prototype condition proposal system 1 can estimate the best prototype conditions for the material and propose them as provisional prototype conditions. Figure 7 shows how the prototype condition proposal processing unit 113 searches for provisional prototype conditions to be proposed by performing the optimization process in step S610 in this manner. Figure 7 also shows, as a comparative example, how provisional prototype conditions to be proposed are searched using conventional technology. Figure 7 shows predicted data for the coefficient of thermal expansion (CTE) and bending strength, which are characteristics of a ceramic composite material made from several raw materials, on a scatter plot. As mentioned above, in the case of conventional technology, since the entire broad parameter space is explored evenly, the search around the target region within that parameter space becomes sparse. In contrast, the prototype condition proposal system 1 of this embodiment can perform a focused search around the target region within the parameter space by performing optimization processing using a sampling method as shown in steps S611 to S616, or by performing continuous optimization processing as shown in steps S617 to S619. In other words, the prototype condition proposal system 1 of this embodiment increases the influence of measured characteristic data with good characteristics as candidate prototype levels, and as shown in Figure 7, it can focus on searching for prototype conditions that are close to the characteristics of the measured characteristic data, and efficiently find many prototype conditions that can satisfy the desired characteristics.

[0071] As mentioned above, in this embodiment, when a user prototypes a material based on the prototype conditions proposed in step S630 of Figure 6, and data representing the measured properties of the resulting material is input as new measured property data, the prototype condition proposal system 1 of this embodiment proposes more optimized prototype conditions to the user based on the newly input measured property data. In this case, the control unit 11 of the prototype condition proposal system 1 determines whether or not the newly input measured property data contains missing values. If it is determined that there are missing values, the system performs preprocessing of the newly input measured property data to fill in these missing values, starting from step S440 of Figure 4. On the other hand, if it is determined that there are no missing values, preprocessing of the newly input measured property data is not necessary. In such cases, the control unit 11 determines whether or not it is necessary to update the regression model. If it is determined that the regression model needs to be updated, the control unit 11 starts processing from step S510 in Figure 5 to construct a new regression model and executes the regression model construction process for the newly input characteristic measurement data. On the other hand, if it is determined that the regression model does not need to be updated, the control unit 11 omits the regression model construction process and the regression model evaluation process, and uses the previously constructed regression model to perform the prototype condition suggestion process for the newly input characteristic measurement data, starting processing from step S610 in Figure 6. In addition, if the newly input characteristic measurement data contains missing values ​​and the regression model does not need to be updated, the control unit 11 similarly omits the regression model construction process and the regression model evaluation process.

[0072] According to the embodiments of the present invention described above, the following effects and advantages are achieved.

[0073] (1) The prototype condition proposal system 1 is a system that proposes prototype conditions for a material to a material developer, and comprises a regression model construction processing unit 112 and a prototype condition proposal processing unit 113. The regression model construction processing unit 112 performs a regression model construction process (Figure 5) on the measured characteristic data representing the measured results of the material's properties (step S320). The prototype condition proposal processing unit 113 performs an optimization process (step S610) to search for the optimal prototype conditions for the material using the constructed regression model, and performs a prototype condition proposal process (Figure 6) (step S340) based on the results of the optimization process (step S610). In this way, even if the parameter space to be searched is extensive in terms of the computational resources used, good prototype conditions for the material can be proposed with high accuracy.

[0074] (2) The optimization process (step S610) includes a prototype level candidate creation process (step S611) which creates a specified number of prototype level candidates, a calculation process (step S612) which calculates predicted values ​​or acquisition functions for all created prototype level candidates, and an extraction process (step S613) which extracts the prototype conditions with the best predicted values ​​or acquisition functions. In this way, the prototype level candidate with the best predicted values ​​or acquisition functions is extracted as a prototype condition from all created prototype level candidates. As a result, the prototype condition proposal system 1 can estimate the best prototype conditions for the material.

[0075] (3) The prototype level candidate creation process (step S611) includes a selection process (step S614) in which a standard prototype level is selected from prototype level candidates whose characteristics have been measured, a variation process (step S615) in which random numbers are used to vary the selected prototype level within the range in which the material can be prototyped, and a saving process (step S616) in which the prototype level candidate to which the variation process (step S615) has been applied is saved, and the computer is made to repeat these processes a specified number of times. In this way, the prototype level candidate creation process (step S611) creates a necessary and sufficient number of prototype level candidates in order to accurately extract prototype conditions with good characteristics.

[0076] (4) The prototype level to be used as a standard is randomly selected in the selection process (step S614). This makes it easier for candidate prototype levels with good characteristics to be selected as prototype conditions.

[0077] (5) The selection process (step S614) may include a process (not shown) for weighting the selection probability of the standard prototype level. In this case, by weighting in this way, a prototype level that is preferable as a standard becomes more likely to be selected.

[0078] (6) The prototype level candidate creation process (step S611) may further include a determination process (not shown) for determining whether or not a standard prototype level exists, and a standard generation process (not shown) for generating a standard using random numbers if the determination process (not shown) determines that there is no standard. In this case, even if a standard does not exist, a prototype level candidate can be created in the same way as if a standard existed by generating a standard using random numbers.

[0079] (7) The optimization process (step S610) includes a selection process (step S617) in which an initial prototype level is selected from prototype level candidates whose characteristics have been measured, a penalty setting process (step S618) in which a penalty is set for the selected prototype level if it deviates from predetermined constraints, and a process (step S619) in which the prototype conditions are optimized so that the predicted value or acquisition function obtained by adding or multiplying the set penalty is the best. In this way, the prototype levels are refined compared to when the optimization process is performed in a single step. As a result, the prototype condition proposal system 1 can estimate the best prototype conditions for the material.

[0080] (8) The initial prototype level is randomly selected in the selection process (step S617). This makes it less likely that the region within the parameter space being searched will be biased when searching for prototype conditions (step S610).

[0081] (9) The selection process (step S617) may include a process (not shown) that weights the probability of selecting an initial prototype level. In this case, by weighting in this way, a preferred prototype level is more likely to be selected as the initial value.

[0082] (10) The optimization process (step S610) may further include a determination process (not shown) for determining whether or not there is a standard prototype level, and a standard generation process (not shown) for generating a standard using random numbers if the determination process (not shown) determines that there is no standard. In this case, even if a standard does not exist, a candidate prototype level can be created in the same way as if a standard existed by generating a standard using random numbers.

[0083] (11) The constraint is that the material can be prototyped. In this way, a constraint from the materials science is imposed, so the prototype condition proposal system 1 can propose only prototype conditions that can actually be used to prototype the material.

[0084] It should be noted that the present invention is not limited to the embodiments described above, and can be implemented using any components without departing from the spirit of the invention.

[0085] The embodiments and modifications described above are merely examples, and the present invention is not limited to these, as long as the features of the invention are not impaired. Furthermore, although various embodiments and modifications have been described above, the present invention is not limited to these. Other embodiments conceivable within the scope of the technical idea of ​​the present invention are also included within the scope of the present invention. [Explanation of Symbols]

[0086] 1: Prototype Condition Proposal System 11: Control Unit 12: Storage part 13: User Interface Section 14: Communications Department 111: Preprocessing unit for measured characteristic data 112: Regression Model Construction Processing Unit 113: Prototype Condition Proposal Processing Unit 131: Input section 132: Output section 400: Internet

Claims

1. A prototype condition proposal system that proposes prototype conditions for materials to material developers, A regression model construction processing unit that performs a regression model construction process on the measured characteristic data representing the measured characteristics of the aforementioned material, An optimization process is performed to search for the optimal prototype conditions for the material using the constructed regression model, and a prototype condition proposal processing is performed based on the results of the optimization process. Equipped with, The aforementioned optimization process, A prototype level candidate creation process that creates a specified number of prototype level candidates, A calculation process to calculate predicted values ​​or acquisition functions for all the prototype level candidates created above, An extraction process that extracts the prototype conditions that yield the best predicted values ​​or acquisition function. Includes, The aforementioned process for creating candidate prototypes is as follows: A selection process involves choosing a standard prototype level from a list of candidate prototype levels whose characteristics have already been measured, and Within the range where the material can be prototyped, a variation process is performed to randomly vary the selected prototype level, A storage process for saving the prototype level candidate that has undergone the aforementioned variation processing. A prototype condition suggestion system that includes a process that causes a computer to repeatedly execute a specified number of times.

2. In the prototype condition proposal system described in claim 1, The prototype level used as the criterion is a prototype condition proposal system that is randomly selected in the selection process.

3. In the prototype condition proposal system described in claim 2, A prototype condition proposal system in which the selection process includes a process for weighting the selection probability of the prototype level to be used as the criterion.

4. In the prototype condition proposal system described in claim 1, The aforementioned process for creating candidate prototypes is as follows: A determination process to determine whether or not a prototype meets the required standard, If the aforementioned determination process determines that there is no criterion, a criterion generation process is performed to generate a criterion using random numbers. A prototype condition suggestion system, which further includes this.

5. A prototype condition proposal system that proposes prototype conditions for a material to a material developer, A regression model construction processing unit that performs a regression model construction process on the measured characteristic data representing the measured characteristics of the aforementioned material, An optimization process is performed to search for the optimal prototype conditions for the material using the constructed regression model, and a prototype condition proposal processing is performed based on the results of the optimization process. Equipped with, The aforementioned optimization process, A selection process to select an initial prototype level from candidate prototype levels whose characteristics have already been measured, A penalty setting process is performed to set a penalty for deviations from predetermined constraints regarding the selected prototype level. A process to optimize the prototype conditions so that the predicted value or acquisition function obtained by adding or multiplying the set penalty is the best. A prototype condition suggestion system, including the above.

6. A prototype condition proposal system according to claim 5, wherein the initial prototype level is randomly selected in the selection process.

7. In the prototype condition proposal system described in claim 6, A prototype condition proposal system, wherein the selection process includes a process for weighting the selection probability of the prototype level that becomes the initial value.

8. In the prototype condition proposal system described in claim 5, The aforementioned optimization process is A determination process to determine whether or not a prototype meets the required standard, If the aforementioned determination process determines that there is no criterion, a criterion generation process is performed to generate a criterion using random numbers. A prototype condition suggestion system, which further includes this.

9. A prototype condition proposal system according to claim 5, wherein the constraint condition is that the material can be prototyped.

10. A method for proposing prototype conditions, which uses a computer to propose prototype conditions for materials to material developers, A regression model construction process is performed to construct a regression model for the measured property data representing the measured properties of the aforementioned material, An optimization process is performed to search for the optimal prototype conditions for the material using the constructed regression model, and a prototype condition proposal process is performed to propose prototype conditions for the material based on the results of the optimization process. The computer is made to execute the above, The aforementioned optimization process, A prototype level candidate creation process that creates a specified number of prototype level candidates, A calculation process to calculate predicted values ​​or acquisition functions for all the prototype level candidates created above, An extraction process that extracts the prototype conditions that yield the best predicted values ​​or acquisition function. Includes, The aforementioned process for creating candidate prototypes is as follows: A selection process involves choosing a standard prototype level from a list of candidate prototype levels whose characteristics have already been measured, and Within the range where the material can be prototyped, a variation process is performed to randomly vary the selected prototype level, A storage process for saving the prototype level candidate that has undergone the aforementioned variation processing. A method for proposing prototype conditions, which includes a process that causes the computer to repeatedly execute the above a specified number of times.

11. A method for proposing prototype conditions for a material to a material developer using a computer, A regression model construction process is performed to construct a regression model for the measured property data representing the measured properties of the aforementioned material, An optimization process is performed to search for the optimal prototype conditions for the material using the constructed regression model, and a prototype condition proposal process is performed to propose prototype conditions for the material based on the results of the optimization process. The computer is made to execute the above, The aforementioned optimization process, A selection process to select an initial prototype level from candidate prototype levels whose characteristics have already been measured, A penalty setting process is performed to set a penalty for deviations from predetermined constraints regarding the selected prototype level. A process to optimize the prototype conditions so that the predicted value or acquisition function obtained by adding or multiplying the set penalty is the best. A method for proposing prototype conditions, including the above.

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