Information processing system, information processing method, and program

Abstract

To provide an information processing system configured to obtain a highly accurate analysis result even for significant overlap of spectra originating from components.SOLUTION: An acquisition step A6 acquires target measurement data indicating a result of measurement using an X ray for a sample to be analyzed, and at least one piece of reference data. A designation receiving step A7 receives a designation of the reference data. A setting step A9 sets an objective function for executing non-negative matrix factorization, including a linear combination of at least one variable basis and at least one fixed basis, based on the designation. Each fixed basis corresponds to the designated reference data. The components of the variable basis are set to be variable parameters in the non-negative matrix factorization. The components of the fixed basis are set to be fixed parameters in the non-negative matrix factorization.SELECTED DRAWING: Figure 4

Description

[Technical Field]

[0001] The present invention relates to an information processing system, an information processing method, and a program. [Background technology]

[0002] Patent Document 1 discloses a technique that can return decomposition results that are close to true basis spectra when decomposing spectral data that are expected to have high linear independence.

[0003] The non-negative matrix factorization unit according to the technology finds a plurality of basis spectral data and activation data by searching for a local minimum of an objective function that includes a regularization term that evaluates the degree of deviation between a set of observed spectral data and a set of estimated spectral data calculated from a plurality of basis spectral data and activation data, as well as the linear independence of the plurality of basis spectral data or the activation data. [Prior art documents] [Patent documents]

[0004] [Patent Document 1] Japanese Patent Application Publication No. 2019-087042 Summary of the Invention [Problem to be solved by the invention]

[0005] However, for example, when peaks contained in a spectrum overlap, if the X-ray measurement results are analyzed using non-negative matrix factorization based on the above technique, it may be difficult to obtain stable analysis results. [Means for solving the problem]

[0006] According to one aspect of the present invention, there is provided an information processing system. The information processing system includes at least one processor. The processor is configured to execute a program to perform the following steps: In the acquisition step, target measurement data indicating the results of measurement using X-rays on a sample to be analyzed and at least one reference data are acquired; In the specification acceptance step, specification of the reference data is accepted; In the setting step, based on the specification, an objective function for performing nonnegative matrix factorization is set, the objective function including a linear combination of at least one variable basis and at least one fixed basis, where each fixed basis corresponds to the specified reference data; Components of the variable basis are set to be variable parameters during nonnegative matrix factorization; Components of the fixed basis are set to be fixed parameters during nonnegative matrix factorization. [Brief explanation of the drawings]

[0007] [Figure 1] 1 is a configuration diagram illustrating an information processing system 1. FIG. [Figure 2] FIG. 2 is a diagram illustrating an example of the configuration of an X-ray measurement device 2. [Figure 3] FIG. 2 is a block diagram showing a hardware configuration of an information processing device 3. [Figure 4] 1 is an activity diagram showing an example of the flow of information processing executed in the information processing system 1. FIG. [Figure 5] FIG. 10 is a diagram showing the relationship between target measurement data, a variable basis, and a fixed basis included in an objective function. [Figure 6] FIG. 10 is a diagram showing an example of an analysis screen IM1. [Figure 7] FIG. 1 is a diagram showing the results of a component analysis of an SiO-based negative electrode material of a lithium secondary battery based on this information processing. [Figure 8] As a comparative example, the results of a component analysis of an SiO-based negative electrode material for a lithium secondary battery based on linear combination fitting (LCF) are shown. DETAILED DESCRIPTION OF THE INVENTION

[0008] DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS The present invention will be described below with reference to the accompanying drawings. Various features shown in the following embodiments can be combined with each other.

[0009] Incidentally, the program for realizing the software appearing in one embodiment may be provided as a non-transitory computer-readable medium, or may be provided so that it can be downloaded from an external server, or may be provided so that the program is started on an external computer and its functions are realized on a client terminal (so-called cloud computing).

[0010] Furthermore, various information processing according to an embodiment may realize input and output corresponding to the input. Here, the form of information referenced in such information processing (hereinafter referred to as reference information) is not limited as long as an output is obtained as a result of the input. The reference information may be, for example, rule-based information such as a database, a lookup table, or a predetermined function (including a decision formula such as a regression formula constructed using a statistical method), a trained model that has previously trained the correlation between input and output, or a large-scale language model that can output a desired result by inputting a prompt.

[0011] In one embodiment, a "unit" may include, for example, a combination of hardware resources implemented by a circuit in the broad sense and software information processing that can be specifically realized by these hardware resources. In one embodiment, various information is handled, and this information is represented, for example, by physical values ​​of signal values ​​representing voltage and current, high and low signal values ​​as a binary bit set consisting of 0 or 1, or quantum superposition (so-called quantum bits), and communication and calculations can be performed on a circuit in the broad sense.

[0012] Furthermore, a circuit in the broad sense is a circuit realized by at least an appropriate combination of a circuit, circuitry, processor, memory, etc. The processor may be a general-purpose processor or a dedicated circuit. That is, it includes an application specific integrated circuit (ASIC), a programmable logic device (e.g., a simple programmable logic device (SPLD), a complex programmable logic device (CPLD), and a field programmable gate array (FPGA)), etc.

[0013] 1. Hardware Configuration This section explains the hardware configuration.

[0014] <Information Processing System 1> FIG. 1 is a configuration diagram showing an information processing system 1. The information processing system 1 according to this embodiment is used to analyze the state of a sample to be analyzed, such as its structure, composition, valence, and chemical bonding state, using X-rays. The information processing system 1 includes, for example, a database DB, an X-ray measurement device 2, and an information processing device 3. The database DB, the X-ray measurement device 2, and the information processing device 3 are configured to be able to communicate with each other via a telecommunications line. In one embodiment, the information processing system 1 is made up of one or more devices or components. For example, if the information processing system 1 is made up of only the information processing device 3, the information processing system 1 can be the information processing device 3. These components will be described below.

[0015] <Database> The database DB stores reference data related to measurements using X-rays. The reference data may include open data or may be provided limitedly to some users. The reference data is data that may be obtained when performing measurements using X-rays on a substance in a certain state. The reference data may include, for example, the results of measurements using X-rays performed on a predetermined reference sample, or predicted data of the results of measurements using X-rays obtained by simulations related to the reference sample. The reference sample is, for example, a sample having a known structure, composition, valence, chemical bonding state, etc. The reference sample is preferably, for example, a single sample having a purity of not less than a predetermined value (for example, 99.99% or more). Also, the simulations related to the reference sample include, for example, any simulations such as first-principles calculations, band calculations, simulations based on the molecular orbital method, and molecular dynamics simulations when assuming a certain defined system.

[0016] <X-ray measuring apparatus 2> FIG. 2 is a diagram showing a configuration example of the X-ray measuring apparatus 2. The X-ray measuring apparatus 2 is configured to spectroscopically detect fluorescent X-rays generated by irradiating a sample S with X-rays and generate the detection results as target measurement data. In the present embodiment, as an example, the X-ray measuring apparatus 2 is configured to measure X-rays in a manner capable of analyzing information on microscopic chemical states such as the valence and spin of elements constituting the sample S based on X-ray emission spectroscopy (XES). Specifically, for example, the X-ray measuring apparatus 2 includes an X-ray irradiation unit 21, a sample stage 22, two spectroscopic crystals 23a and 23b, and an X-ray detection unit 24.

[0017] The X-ray irradiation unit 21 is configured to irradiate X-rays X1. The X-ray irradiation unit 2 may irradiate characteristic X-rays from the target by irradiating thermoelectrons from an electron gun onto a target such as Cu, Mo, W, etc. Hereinafter, for convenience of explanation, the X-rays X1 irradiated from the X-ray irradiation unit 21 are referred to as incident X-rays X1.

[0018] The sample stage 22 is configured so that a sample S to be analyzed can be placed thereon. The sample S placed on the sample stage 22 is positioned so that it is irradiated with incident X-rays X1. The sample S irradiated with the incident X-rays X1 outputs fluorescent X-rays X2 corresponding to the constituent elements of the sample S.

[0019] The two dispersing crystals 23a, 23b are arranged to form a double-crystal monochromator. For example, the first dispersing crystal 23a is configured to reflect the fluorescent X-rays X2 generated from the sample S. The second dispersing crystal 23b is configured to further reflect the fluorescent X-rays X2 reflected by the first dispersing crystal 23a. Each of the dispersing crystals 23a, 23b is configured to adjust the reflection angle of the fluorescent X-rays X2 by rotating. The two dispersing crystals 23a, 23b are, for example, crystals made of the same material, and can selectively reflect specific wavelengths in accordance with the incident angle of the fluorescent X-rays X2 by Bragg reflection. As a result, by appropriately adjusting the reflection angles of the fluorescent X-rays X2 by the first dispersing crystal 23a and the second dispersing crystal 23b, it is possible to separate the fluorescent X-rays X2 generated from the sample S into monochromatic light of a desired energy band. Note that the specific embodiment of the optical system for dispersing the fluorescent X-rays X2 is not limited to this and may be any.

[0020] The X-ray detection unit 24 is configured to detect X-rays that have passed through the sample S and thereby output the results of the X-ray measurement. The specific form of the X-ray detection unit 24 is arbitrary, but a proportional counter, a scintillation counter, a semiconductor detector, or the like may be used. In this embodiment, the X-ray detection unit 24 is configured to detect components that are dispersed by the analyzing crystals 23 a, 23 b out of the fluorescent X-rays X2 that are generated from the sample S due to the incident X-rays X1.

[0021] <Information processing device 3> 3 is a block diagram showing the hardware configuration of the information processing device 3. The information processing device 3 includes a communication bus 30, a communication unit 31, a storage unit 32, a processor 33, a display unit 34, and an input unit 35, and these components are electrically connected via the communication bus 30 inside the information processing device 3.

[0022] The communication unit 31 is preferably a wired communication means such as USB, IEEE1394, Thunderbolt (registered trademark), wired LAN network communication, etc., but may also include wireless LAN network communication, mobile communication such as 3G / LTE / 5G, BLUETOOTH (registered trademark) communication, etc. as needed. In other words, it is more preferable to implement it as a collection of multiple communication means. In other words, the information processing device 3 may communicate various information from the outside via the communication unit 31 and the network.

[0023] The storage unit 32 stores various pieces of information defined above. This can be implemented, for example, as a storage device such as a solid state drive (SSD) that stores various programs and the like related to the information processing device 3 executed by the processor 33, or as a memory such as a random access memory (RAM) that stores temporarily required information (arguments, arrays, etc.) related to the program operations. The storage unit 32 stores various programs, variables, etc. related to the information processing device 3 executed by the processor 33.

[0024] The processor 33 processes and controls the overall operations related to the information processing device 3. The processor 33 is, for example, a central processing unit (CPU) not shown. The processor 33 realizes various functions related to the information processing device 3 by reading out predetermined programs stored in the storage unit 32. In other words, information processing by software stored in the storage unit 32 is specifically realized by the processor 33, which is an example of hardware, and can be executed as each functional unit included in the processor 33. These will be described in more detail in the next section. Note that the processor 33 is not limited to being single, and multiple processors 33 may be provided for each function. A combination of these may also be used.

[0025] The processor 33 is configured to function as an acquisition unit and acquire information from the X-ray measurement apparatus 2 or other devices. The processor 33 is configured to be able to acquire various pieces of information by reading out various pieces of information stored in a storage area that is at least a part of the memory unit 32 and writing the read information into a working area that is at least a part of the memory unit 32. The storage area is, for example, an area of ​​the memory unit 32 that is implemented as a storage device such as an SSD. The working area is, for example, an area that is implemented as a memory such as a RAM. Note that acquisition by the processor 33 includes acquiring output results from each functional unit included in the processor 33.

[0026] The processor 33 is configured to display various types of information as a display processing unit. The information can be presented to a user via a display unit 34 (described later) or another device. In such a case, for example, the processor 33 controls the display unit 34 of the information processing device 3 to display visual information such as a screen, an image including a still image or a video, an icon, or a message. The processor 33 may generate only rendering information for displaying the visual information on the information processing device 3. Note that the processor 33 may present the output information to a user without going through the user of the information processing device 3 or another device.

[0027] The display unit 34 may be included in the housing of the information processing device 3 or may be externally attached. The display unit 34 displays a graphical user interface (GUI) screen that can be operated by the user. This is preferably implemented by selectively using display devices such as a CRT display, a liquid crystal display, an organic EL display, and a plasma display depending on the type of information processing device 3.

[0028] The input unit 35 is configured to be able to accept input from a user. The input unit 35 may be included in the housing of the information processing device 3 or may be externally attached. For example, the input unit 35 may be implemented as a touch panel integrated with the display unit 34. The touch panel allows the user to input tapping, swiping, and the like. Of course, instead of a touch panel, a switch button, a mouse, a QWERTY keyboard, a voice recognition device, a gesture detection device, a gaze detection device, a biosignal detection device, an imaging device, and the like may be used. That is, the input unit 35 accepts an operation input made by the user. In response, the input unit 35 transfers a signal corresponding to the operation input to the processor 33 via the communication bus 30. The processor 33 can execute predetermined control and calculation as necessary.

[0029] 2. Information Processing In this section, the information processing executed in the information processing system 1 described above will be described.

[0030] 2.1. Information processing flow 4 is an activity diagram showing an example of the flow of information processing executed in the information processing system 1. Note that the information processing may include any exception processing not shown. Exception processing includes interruption of the information processing or omission of each process. Selection or input performed in the information processing may be based on a user operation or may be performed automatically without relying on a user operation.

[0031] [Activity A1] First, in activity A1, the processor 33 transmits a measurement-related command to the X-ray measurement device 2. The measurement-related command may include any information related to the incident X-rays X1 irradiated onto the sample S, such as the irradiation intensity, irradiation time, and irradiation position of the incident X-rays X1. The measurement-related command may also include information related to the rotation speed of the analyzing crystals 23a and 23b (in other words, the integration time and step width of the fluorescent X-rays X2 in a certain energy band). In other words, the measurement-related command may include a measurement sequence. The measurement-related command may also include a command related to the start of measurement.

[0032] [Activity A2] Next, in activity A2, the X-ray measurement device 2 performs a measurement on the sample S based on a command related to the measurement sent from the processor 33. As a result, the X-ray measurement device 2 generates the results detected by the X-ray detection unit 24 as target measurement data. The target measurement data indicates the results of a measurement using X-rays on the sample S to be analyzed. The results of the measurement using X-rays indicate, for example, the results of a spectrum measurement using X-rays. In this embodiment, the X-ray measurement device 2 is configured to perform a measurement by XES, and the target measurement data is spectrum data obtained as a result of the measurement by XES, including the energy (in other words, wavelength) of X-rays detected by the X-ray detection unit 24 corresponding to the angle between the dispersing crystals 23a and 23b and the number of X-ray photons (count) detected by the X-ray detection unit 24 for each energy.

[0033] [Activity A3] Thereafter, in activity A3, the processor 33 displays the analysis screen IM1 (see FIG. 6) on the display unit 34. The analysis screen IM1 is configured to accept various operations from the user.

[0034] [Activity A4] Next, in activity A4, the processor 33 sends a command to the X-ray measurement device 2 to import the target measurement data.

[0035] [Activity A5] Next, in activity A5, the X-ray measurement device 2 transmits the object measurement data generated by the X-ray detection unit 24 to the information processing device 3 based on the transmitted data acquisition command.

[0036] [Activity A6] Next, in activity A6, the processor 33 acquires the target measurement data transmitted from the X-ray measurement device 2. Note that the information source for the target measurement data is not limited to the information processing device 3, and the processor 33 may use other devices, such as the storage unit 32 that stores the target measurement data, as the information source.

[0037] [Activity A7] Next, in activity A7, the processor 33 accepts the designation of reference data. For example, the processor 33 accepts an input by the user to the UI displayed on the analysis screen IM1 as the designation of reference data.

[0038] [Activity A8] Next, in activity A8, the processor 33 acquires at least one reference data. The processor 33 may acquire the specified reference data by, for example, reading it from the database DB, or may acquire information pre-stored in the storage unit 32 or the like as the reference data. The reference data is configured to be comparable with the target measurement data. For example, the reference data is configured to indicate the relationship between the energy of X-rays and the number of X-ray photons at that energy.

[0039] [Activity A9] Next, in activity A9, processor 33 sets an objective function for performing nonnegative matrix factorization based on the specified reference data. The objective function is an index to be maximized (or minimized) during optimization. The objective function includes a linear combination of at least one variable basis and at least one fixed basis. Each fixed basis corresponds to the specified reference data. The components of the fixed basis are set to be fixed parameters during nonnegative matrix factorization. The components of the variable basis are set to be variable parameters during nonnegative matrix factorization.

[0040] An example of how the objective function is set will now be described. FIG. 5 is a diagram showing the relationship between the target measurement data, variable basis, and fixed basis included in the objective function. Here, the target measurement data is spectral data showing the results of measuring the number of photons of fluorescent X-rays X2 in M ​​energy bands. The processor 33 processes a set of target measurement data from measurements performed under N different measurement conditions as an N×M matrix. Here, the set of target measurement data is represented as matrix X. "n" in FIG. 5 represents the nth target measurement data, and V in matrix X represents the number of photons per energy in the nth target measurement data. Note that if there is only one target measurement data, the processor 33 can simply treat a 1×m matrix as matrix X representing the set of target measurement data.

[0041] Next, the processor 33 sets a fixed basis based on the reference data acquired from a database DB or the like based on the designation. First, the processor 33 acquires the photon counts of fluorescent X-rays X2 in M ​​energy bands included in the target measurement data so that the reference data can be compared with the target measurement data. The processor 33 acquires, for example, data points included in the spectrum included in the reference data or estimates calculated from the data points using spline interpolation, trapezoidal approximation, or the like. Note that the photon counts at this time are relative values ​​within the entire spectrum and may be normalized, for example, so that the maximum value is a predetermined value. For example, if reference data related to S different reference samples is designated, the processor 33 treats the set of R reference data as an S × M matrix. In this way, the processor 33 treats each spectrum obtained based on the reference data as a fixed basis. For example, if S reference data are designated, the processor 33 sets a total of S fixed bases from each of the reference data and treats these fixed bases as an S × M matrix B'. The matrix B' constitutes a set of fixed bases, and the sequence of numbers in each row of the matrix B' represents the spectrum of each reference data of a specified reference sample.

[0042] Next, an approximate matrix Y for the target measurement data is generated. The approximate matrix Y includes a linear combination of the variable basis and the fixed basis. For example, the approximate matrix Y may include the following terms:

number

[0043] Here, B represents a set of bases treated as a variable parameter during optimization of the objective function, which will be described later, and can be expressed as an R×M matrix. Here, R is an integer greater than or equal to 1 and may correspond to the number of unidentified components contained in sample S. Each element of the R×M matrix corresponding to matrix B is treated as a variable parameter during optimization, which will be described later. W and W' are the coefficient matrices of matrix B corresponding to the variable base and matrix B' corresponding to the fixed base, respectively, and each element is treated as a variable parameter during optimization, which will be described later. For example, the coefficient matrix W of matrix B corresponding to the variable base is formulated using an N×R matrix, and the coefficient matrix W' of matrix B' corresponding to the fixed base is formulated using an N×S matrix. The approximate matrix Y may also include other elements, such as a constant matrix.

[0044] Next, the processor 33 sets an objective function Z based on the set approximate matrix Y. The objective function Z is configured to be optimized so that the approximate matrix Y approximates the matrix X representing the target measurement data. For example, the objective function Z can be defined as a norm as follows:

number

[0045] The norm may take any form, and examples include the L1 norm (Manhattan distance) and the L2 norm (Euclidean distance). Thus, the objective function Z may be configured to include a linear combination of a variable basis and a fixed basis, with at least the variable basis and the coefficients of the variable basis being variable parameters. As a result, as the approximate matrix Y approaches the matrix X corresponding to the target measurement data through optimization, the objective function Z decreases, ideally to 0. Among the parameters included in the objective function Z, the coefficient matrices W and W' and the elements of the matrix B corresponding to the variable basis are treated as variable parameters during optimization, while the elements of the matrix B' corresponding to the fixed basis are treated as fixed values. Each element of the matrices X, B, B', W, and W' is defined to be nonnegative. Thus, the objective function Z is configured to perform nonnegative matrix factorization, which decomposes the matrix X into nonnegative matrix factors B and B'.

[0046] [Activity A10] Returning to FIG. 4 , after activity A9, in activity A10, the processor 33 performs optimization based on nonnegative matrix factorization using the set objective function Z. Here, the processor 33 adjusts variable parameters to reduce the value of the objective function Z, and determines that the optimization is complete when the objective function Z satisfies a predetermined optimization condition. The processor 33 outputs, as an optimal solution, the parameter that provides the objective function Z closest to 0 among the parameters used until the optimization is complete. Specifically, by setting variable parameters and fixed values ​​as described above, the processor 33 optimizes the objective function Z for the matrix X corresponding to the target measurement data under the constraint that at least the fixed base components (corresponding to the elements of matrix B′) of the objective function Z are fixed, thereby performing nonnegative matrix factorization of the matrix X corresponding to the target measurement data. The specific embodiment of the algorithm for searching for the optimal solution by adjusting variable parameters is arbitrary, and examples include a local search method, an iterative improvement method, and a local search method. Furthermore, metaheuristic search methods such as cuckoo search, genetic algorithms, and particle swarm optimization may be used as algorithms for the optimization process.

[0047] [Activity A11] Next, in activity A11, the processor 33 displays visual information indicating the results of the optimization in activity A10 on the display unit 34. In this embodiment, the processor 33 displays the visual information on the analysis screen IM1.

[0048] According to the above configuration, the search range of parameters during optimization can be reduced by the components of the fixed basis while making the most of information about a known reference sample. Therefore, when analyzing the results of measurements using X-rays, the processing load when subjecting the target measurement data to nonnegative matrix factorization can be reduced. In particular, nonnegative matrix factorization can be applied even when there is a large overlap of multiple X-ray peaks that may be included in the target measurement data. Therefore, the processing load can be reduced while taking advantage of the characteristics of nonnegative matrix factorization, especially for measurement data with overlapping X-ray peaks.

[0049] 2.2.Example of analysis screen In this section, an example of the analysis screen IM1 described above will be described. FIG. 6 is a diagram showing an example of the analysis screen IM1. Here, as an example, the target measurement data indicates the results of XES measurement of a heat-treated Si-based material. As shown in FIG. 6, the analysis screen IM1 includes, for example, a read button 4, a setting area 5, a specification receiving area 6, an execute button 7, a quantitative information area 8, a result display area 9, a base display area 10, a residual area 11, and a score display area 12.

[0050] The read button 4 is configured to transmit a command to execute import of target measurement data in response to a user operation. When the read button 4 is operated, the processor 33 first accepts the designation of the target measurement data to be imported, and then imports the designated target measurement data.

[0051] The setting area 5 is a UI configured to receive specifications regarding conditions for performing optimization by nonnegative matrix factorization. The setting area 5 is configured to allow specification of, for example, the number of components contained in the sample S ("Num component") and the number of optimization iterations ("Num iteration"). In this embodiment, the setting area 5 may include a display setting area 51. The display setting area 51 is configured to allow specification of whether or not to visually display the results of a measurement using X-rays corresponding to a variable basis or a simulation related to the measurement.

[0052] The specification receiving area 6 is an example of visual information configured to display a list of reference data candidates that can be specified based on the acquired reference data. The specification receiving area 6 includes a name of the reference data ("Date Name") and a check box ("Use for supervisor") in which a specification for the reference data candidate can be entered. The processor 33 treats the reference data with the check box checked as the specified reference data. In this manner, the processor 33 displays the specification receiving area 6 to receive a specification from the user. In the present embodiment, as an example, the processor 33 displays the spectral patterns of a-Si (amorphous Si), a-SiO2 (amorphous SiO2), and c-Si (crystalline silicon) as reference samples as candidates for reference data, and receives specification of the spectral patterns of these three substances.

[0053] The execute button 7 is a UI for executing the optimization of the objective function. When the user operates the execute button 7, the processor 33 sets the objective function Z in accordance with the conditions set in the setting area 5 and the specification reception area 6, and starts the optimization up to the number of iterations input in "Num iteration".

[0054] The quantitative information area 8, the result display area 9, the basis display area 10, the residual area 11, and the score display area 12 are areas that show the process or results of optimization when optimization is performed.

[0055] The quantitative information area 8 is an area that quantitatively displays the results of non-negative matrix factorization of matrix X, which is based on a set of target measurement data. Each row displayed in the quantitative information area 8 corresponds to each row of target measurement data stored in matrix X. "Date Name" in the quantitative information area 8 indicates the file name of each target measurement data. "Component" in the quantitative information area 8 indicates the elements of the coefficient matrix W of the variable basis, i.e., the quantity of unknown components contained in sample S other than the components represented by the fixed basis, such as composition ratio.

[0056] The result display area 9 is an example of visual result information showing the result of nonnegative matrix factorization of the matrix X corresponding to the target measurement data. The result display area 9 is configured to display a first contribution associated with a variable basis of the target measurement data and a second contribution associated with a fixed basis, obtained as a result of the nonnegative matrix factorization, in a manner that allows them to be distinguished from each other. For example, the result display area 9 may include a spectrum of the optimized variable basis as the first contribution ("Component") and each spectrum of the optimized fixed basis as the second contribution ("a-Si", "a-SiO2", "c-Si"). These contributions are displayed as graphs in the result display area 9 in a visually distinguishable manner, for example, by line type, color, thickness, etc. The processor 33 may also cause the display unit 34 to display the first contribution and the second contribution in the result display area 9 in a visually distinguishable manner by erasing one of the first and second contributions (e.g., only the second contribution).

[0057] The result display area 9 may be configured to visually display the spectrum of the target measurement data to be analyzed ("Measurement date") and the optimized spectrum ("fitting") corresponding to the approximation matrix Y. With this configuration, the contribution of each component can be viewed while comparing the spectrum of the target measurement data with the optimized spectrum.

[0058] The basis display area 10 is configured to display the spectra of the variable basis and the fixed basis in a superimposed graph. This configuration makes it easier to understand the relationship between the spectrum of the basis and the spectrum of the target measurement data.

[0059] The horizontal axes of the result display area 9 and the base display area 10 indicate, for example, the values ​​of parameters (e.g., the energy of fluorescent X-rays X2, the scattering angle, etc.) in the measurement conditions for the measurement using X-rays. The vertical axes of the result display area 9 and the base display area 10 indicate the detection results by the X-ray detection unit 24, for example, the number of photons. Here, the vertical axis indicates the intensity of X-rays, which is a quantity corresponding to the number of photons. Note that the intensity of X-rays here is relative.

[0060] The basis display area 10 is preferably configured to be viewable together with the result display area 9. In particular, it is preferable that the horizontal axis of the basis display area 10 is displayed to correspond to the horizontal axis of the result display area 9. Such a configuration makes it easier to compare the target measurement data with each basis. In this case, it is preferable that the scale of the vertical axis in the basis display area 10 can be set independently so as to be different from the scale of the result display area 9. With such a configuration, the spectrum of minute components that may be contained in the target measurement data can be grasped by enlarging the basis display area 10.

[0061] The residual area 11 is configured to visually display the difference between the spectrum of the target measurement data and the spectrum of the optimized approximation matrix Y. The difference is an example of information regarding the transition of an index indicating the accuracy of optimization, such as the difference between the spectrum of the target measurement data and the spectrum of the optimized approximation matrix Y, i.e., the residual. The residual area 11 is configured to visually represent the residual as a spectrum. The residual area 11 includes, for example, a reference line 111 and a residual spectrum 112. The reference line 111 represents a graph (e.g., a linear graph) where the residual is zero. The residual spectrum 112 is a graph configured to show the difference for each energy band. By comparing the position of the residual spectrum 112 relative to the reference line 111 for each energy band, the user can analyze which energy bands have low optimization accuracy. The residual area 11 can be displayed together with the result display area 9 and / or the basis display area 10 in a list format. At this time, the horizontal axis of the residual area 11 is displayed so as to correspond to the horizontal axis of the result display area 9 and / or the base display area 10.

[0062] The score display area 12 is configured to display changes in the objective function Z as an example of information regarding the transition of an index indicating the accuracy of optimization during the optimization process. The score display area 12 can be updated to visually display the latest value of the objective function Z during the optimization process. This configuration makes it easier to understand whether the optimization is progressing smoothly, and can prompt the user to interrupt the optimization if, for example, a variable parameter of the objective function Z falls into a local optimum. The horizontal axis of the score display area 12 indicates the number of iterations, and the vertical axis of the score display area 12 indicates the value of the objective function Z at that number of iterations. The score display area 12 can include a theoretical value 121 and a transition graph 122. The theoretical value 121 indicates the value when the objective function Z is 0, i.e., when the approximate matrix Y exactly matches the matrix X corresponding to the target measurement data. The transition graph 122 shows the transition of the objective function Z as the number of iterations changes. If the optimization is progressing smoothly, the value of the objective function Z gradually converges to the theoretical value 121 as the number of iterations increases. On the other hand, if the optimization is not progressing smoothly because the optimization has fallen into a local optimum or the value of the objective function Z is fluctuating, the transition graph 122 will have a different shape than when the optimization is progressing smoothly. Such a difference in the shape of the transition graph 122 can prompt the user to decide whether or not to interrupt the optimization.

[0063] In this way, the processor 33 visually displays information regarding the transition of the index indicating the accuracy of the optimization through the residual area 11 and the score display area 12. With this configuration, it is possible to visually grasp problems such as the optimization remaining at a local optimum solution and being completed with insufficient accuracy.

[0064] 2.3.An example of optimization Next, an example of the results of component analysis of Si-based materials using the above-mentioned information processing method will be described. FIG. 7 shows the results of component analysis of an SiO-based negative electrode material for a lithium secondary battery based on this information processing. FIG. 8 shows the results of component analysis of an SiO-based negative electrode material for a lithium secondary battery based on linear combination fitting (LCF) as a comparative example. Optimization to obtain each component analysis result was performed under the assumption that the sample contained at least amorphous Si, amorphous SiO2, and crystalline Si, and that the number of unknown components was 1. Note that the sample S used for X-ray measurement was obtained by subjecting the SiO-based negative electrode material for a lithium secondary battery to a predetermined heat treatment.

[0065] 7 and 8, sample S corresponding to pattern 1 was obtained without heating the SiO negative electrode material of a lithium secondary battery. Samples S corresponding to patterns 2 to 6 were obtained by heating the SiO negative electrode material of a lithium secondary battery at heating temperatures of 700°C, 800°C, 900°C, 1000°C, and 1100°C, respectively, in an air atmosphere.

[0066] As shown in Figures 7 and 8, both the component analysis results based on this information processing and the LCF-based component analysis showed that the components included amorphous Si, amorphous SiO2, and crystalline Si, as well as unknown components. In the component analysis based on this information processing, the number of unknown components was assumed, and the spectra corresponding to the unknown components (corresponding to each row of matrix B) and their contributions (corresponding to elements of the coefficient matrix) were optimized as variable parameters to determine the spectra and contributions of the unknown components. Here, the number of unknown components was set to 1. In Figure 7, the unknown component in this information processing is denoted as "Component_1." In the component analysis based on LCF, the spectra corresponding to the unknown components were determined in advance using a predetermined analysis method, and then the contributions of the unknown components were determined by optimizing the spectra with the spectra fixed. In Figure 8, the unknown component in the LCF optimization is denoted as "peak 1."

[0067] The results of both component analyses showed a tendency for both the amorphous Si component and the unknown component to decrease as the heating temperature increased, while the amorphous SiO2 component and crystalline Si component tended to increase as the heating temperature increased.In this way, the component analysis results based on this information processing showed a similar tendency to the component analysis results based on LCF, which is often used especially when there is significant peak overlap.This demonstrates that, despite being an optimization algorithm based on nonnegative matrix factorization, this information processing method can provide analysis results with high accuracy that is comparable to LCF, even when there is significant overlap in the spectra derived from each component.

[0068] [others] The above embodiment may be modified as follows.

[0069] In the above embodiment, the execution entity of the optimization performed in activity A10 is not limited to the processor 33, and may be any device different from the information processing device 3.

[0070] In the above embodiment, the fluorescent X-rays X2 detected by the X-ray detection unit 24 are obtained by irradiating the sample S with incident X-rays X1 from the X-ray irradiator 21, but this is not limiting. For example, the fluorescent X-rays X2 may be obtained by irradiating the sample S with an electron beam having a certain energy or more. Specifically, the X-ray measurement device 2 may be an SEM-EDS system. Furthermore, the specific aspect of the measurement using X-rays performed by the X-ray measurement device 2 is arbitrary, and examples include measurement using X-ray fluorescence analysis (XRF), X-ray emission spectroscopy (XES), X-ray photoelectron spectroscopy (XPS), and X-ray absorption spectroscopy (XAFS). The specific aspect of the optical system of the X-ray measurement device 2 may be changed as appropriate depending on the type of measurement using X-rays.

[0071] In this embodiment, the information processing device 3 functions as both a measurement terminal that sends commands regarding measurements by the X-ray measurement device 2 and an analysis terminal that analyzes the target measurement data, but the analysis terminal may be separate from the measurement terminal.

[0072] The information processing device 3 may be an on-premise type or a cloud type. As the information processing device 3 in the cloud type, the above-mentioned functions and processes may be provided in the form of, for example, SaaS (Software as a Service) or cloud computing.

[0073] In the above embodiment, the information processing device 3 performs various storage and control operations, but multiple external devices may be used instead of the information processing device 3. That is, various information and programs may be distributed and stored in multiple external devices using block chain technology or the like.

[0074] The above embodiment is not limited to the information processing system 1, and may be an information processing method or an information processing program. The information processing method includes each step of the information processing system 1. The program causes at least one computer to execute each step of the information processing system 1.

[0075] The information processing system 1 and the like may be provided in the following aspects.

[0076] (1) An information processing system, comprising at least one processor, configured to execute a program to perform the following steps: an acquisition step acquires target measurement data indicating the results of a measurement using X-rays on a sample to be analyzed and at least one reference data; a specification acceptance step accepts specification of the reference data; and a setting step sets an objective function for performing non-negative matrix factorization based on the specification, the objective function including a linear combination of at least one variable basis and at least one fixed basis, wherein each of the fixed bases corresponds to the specified reference data, and components of the variable basis are set to be variable parameters during the non-negative matrix factorization, and components of the fixed basis are set to be fixed parameters during the non-negative matrix factorization.

[0077] With this configuration, the search range for parameters during optimization can be reduced by the components of the fixed basis while making the most of information about a known reference sample. Therefore, when analyzing the results of measurements using X-rays, the processing load when subjecting the target measurement data to nonnegative matrix factorization can be reduced. Here, nonnegative matrix factorization is particularly applicable even when there is a large overlap of multiple X-ray peaks that may be included in the target measurement data. Therefore, the processing load can be reduced while taking advantage of the characteristics of nonnegative matrix factorization, especially for measurement data with overlapping X-ray peaks.

[0078] (2) In the information processing system described in (1) above, the reference data includes the results of the measurement using the X-rays performed on a specified reference sample, or a prediction of the results of the measurement using the X-rays obtained by simulation on the reference sample.

[0079] (3) In the information processing system described in (1) or (2) above, the objective function further includes a linear combination of the variable basis and the fixed basis, and is set so that at least the variable basis and the coefficients of the variable basis are variable parameters.

[0080] (4) In the information processing system described in any one of (1) to (3) above, in the specification acceptance step, the system accepts the specification from the user by displaying visual information configured to list candidates for reference data that can be specified based on the acquired reference data.

[0081] (5) In the information processing system described in any one of (1) to (4) above, the optimization step further comprises optimizing the objective function for a matrix corresponding to the target measurement data under the constraint that at least the components of the fixed basis of the objective function are fixed, thereby performing non-negative matrix factorization of the matrix corresponding to the target measurement data.

[0082] (6) In the information processing system described in (5) above, the analysis and display step further visually displays information relating to the transition of an index indicating the accuracy of the optimization.

[0083] (7) In the information processing system according to any one of (1) to (6) above, the result of the measurement using X-rays indicates the result of a spectrum measurement using X-rays.

[0084] (8) In the information processing system described in any one of (1) to (7) above, the results of the measurement using X-rays indicate the results of measurement by X-ray fluorescence analysis (XRF), X-ray emission spectroscopy (XES), X-ray photoelectron spectroscopy (XPS), or X-ray absorption spectroscopy (XAFS).

[0085] (9) In the information processing system described in any one of (1) to (8) above, the result display step further comprises displaying result visual information showing the result of the nonnegative matrix factorization, and the result visual information displays a first contribution associated with the variable basis and a second contribution associated with the fixed basis of the target measurement data obtained as a result of the nonnegative matrix factorization in a manner that allows them to be distinguished from each other.

[0086] (10) The information processing system according to any one of (1) to (9) above, further comprising an X-ray measurement device, the X-ray measurement device comprising an X-ray irradiation unit, a sample stage, and an X-ray detection unit, the X-ray irradiation unit configured to irradiate the X-rays, the sample stage configured to allow the sample to be analyzed to be placed thereon, and the X-ray detection unit configured to detect the X-rays that have passed through the sample, thereby outputting the results of the X-ray measurement.

[0087] (11) An information processing method, comprising the steps of the information processing system according to any one of (1) to (10) above.

[0088] (12) A program that causes at least one computer to execute each step of the system described in any one of (1) to (10) above. Of course, this is not the case.

[0089] Finally, while various embodiments of the present disclosure have been described, they are presented as examples and are not intended to limit the scope of the invention. The novel embodiments may be embodied in various other forms, and various omissions, substitutions, and modifications may be made without departing from the spirit of the invention. Such embodiments and modifications are intended to be included within the scope and spirit of the invention, as well as within the scope of the inventions and their equivalents as defined in the claims. [Explanation of symbols]

[0090] 1: Information processing system 2:X-ray measuring device 21:X-ray irradiation section 22: Sample stage 23a: 1st spectroscopic crystal 23b:Second spectroscopic crystal 24: X-ray detector 3: Information processing equipment 30: Communication bus 31: Communications Department 32: Storage section 33: Processor 34:Display section 35: Input section 4: Load button 5: Settings area 51:Display setting area 6: Designated reception area 7: Execute button 8:Quantitative information area 9:Result display area 10: Base display area 11: Residual area 111: Reference line 112: Residual spectrum 12: Score display area 121: Theoretical value 122: Transition graph DB: Database IM1: Analysis screen S: Sample X1: Incident X-ray X2: Fluorescent X-ray

Claims

1. An information processing system, at least one processor; The processor is configured to execute a program to perform the following steps: In the acquisition step, object measurement data indicating a result of measurement using X-rays on a sample to be analyzed and at least one reference data are acquired; In the designation receiving step, designation of the reference data is received, In the setting step, an objective function for performing non-negative matrix factorization is set based on the specification, the objective function including a linear combination of at least one variable basis and at least one fixed basis; wherein each of the fixed bases corresponds to the designated reference data; the components of the variable basis are set to be variable parameters during the non-negative matrix factorization; The system, wherein the components of the fixed basis are set to be fixed parameters during the non-negative matrix factorization.

2. 2. The information processing system according to claim 1, The system, wherein the reference data includes results of the X-ray measurements performed on a predetermined reference sample or predictions of results of the X-ray measurements obtained by simulation on the reference sample.

3. 2. The information processing system according to claim 1, A system in which the objective function further includes a linear combination of the variable basis and the fixed basis, and is set so that at least the variable basis and coefficients of the variable basis are variable parameters.

4. 2. The information processing system according to claim 1, In the designation receiving step, the system receives the designation from the user by displaying visual information configured to list candidates for reference data that can be designated based on the acquired reference data.

5. 2. The information processing system according to claim 1, Furthermore, in the optimization step, the system performs non-negative matrix factorization of the matrix corresponding to the target measurement data by optimizing the objective function with respect to the matrix corresponding to the target measurement data under the constraint that at least the components of the fixed basis of the objective function are fixed.

6. 6. The information processing system according to claim 5, Furthermore, in the analysis and display step, the system visually displays information relating to the transition of the index indicating the accuracy of the optimization.

7. 2. The information processing system according to claim 1, The results of the x-ray measurements represent results of x-ray spectrum measurements.

8. 2. The information processing system according to claim 1, The system, wherein the results of the measurement using X-rays indicate the results of measurement using X-ray fluorescence analysis (XRF), X-ray emission spectroscopy (XES), X-ray photoelectron spectroscopy (XPS), or X-ray absorption spectroscopy (XAFS).

9. 2. The information processing system according to claim 1, Furthermore, in the result display step, visual result information showing the result of the non-negative matrix factorization is displayed; The system, wherein the result visual information displays a first contribution associated with the variable basis and a second contribution associated with the fixed basis of the target measurement data obtained as a result of the non-negative matrix factorization in a manner that allows them to be distinguished from each other.

10. 2. The information processing system according to claim 1, Furthermore, an X-ray measuring device is provided. the X-ray measurement device includes an X-ray irradiation unit, a sample stage, and an X-ray detection unit; the X-ray irradiation unit is configured to irradiate the X-rays, the sample stage is configured to allow the sample to be placed thereon; The X-ray detector is configured to detect X-rays transmitted through the sample and thereby output a measurement of the X-rays.

11. An information processing method, comprising: A method comprising the steps of the information processing system according to any one of claims 1 to 10.

12. A program, A program causing at least one computer to execute each step of the system according to any one of claims 1 to 10.

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