Information processing apparatus, information processing method, generation method, and program

Abstract

Technique capable of suitably estimating macroscopic characteristics or microscopic characteristics of target structure SOLUTION: An information processing apparatus (1) includes an acquisition unit (11) that acquires one or a plurality of macroscopic feature amounts characterizing a phenomenon of a subject structural body on a macroscopic scale and one or a plurality of microscopic feature amounts characterizing at least one of a structure and a behavior of a component of the subject structural body on a microscopic scale of the subject structural body, and a generation unit (12) that generates a model defining a correspondence relationship between the phenomenon of the subject structural body on the macroscopic scale and at least one of the structure and the behavior of the component of the subject structural body on the microscopic scale of the subject structural body and including one or a plurality of stages of scaling processing with reference to the one or the plurality of macroscopic feature amounts and the one or the plurality of microscopic feature amounts acquired by the acquisition unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

[0002] In recent years, there has been progress in the technology of so-called materials informatics, which aims to analyze the properties of substances or materials using information science techniques. For example, Non-Patent Document 1 proposes upscaling using metamodels in materials design optimization. [Prior art documents] [Non-patent literature]

[0003] [Non-Patent Document 1] Coelho, L., Lucor, D., Fabbiane, N. et al. Multi-scale approach for reliability-based design optimization with metamodel upscaling. Struct Multidisc Optim 66, 205 (2023). https: / / doi.org / 10.1007 / s00158-023-03643-4 Summary of the Invention [Problem to be solved by the invention]

[0004] Generally, when attempting to estimate the macroscopic properties of a target structure such as a substance or material from the microscopic properties of the target structure, or conversely, when attempting to estimate the microscopic properties of the target structure from the macroscopic properties of the target structure, it is not easy to improve the estimation accuracy because there is a large scale difference between the macroscopic scale and the microscopic scale.

[0005] An object of one aspect of the present invention is to realize a technology that can suitably estimate macroscopic or microscopic properties of a target structure. [Means for solving the problem]

[0006] In order to solve the above problem, an information processing device according to one embodiment of the present invention includes an acquisition unit that acquires one or more macroscopic features that characterize a phenomenon at a macroscopic scale of a target structure and one or more microscopic features that characterize at least one of the structure and behavior of components of the target structure at a microscopic scale of the target structure, and a generation unit that generates a model that defines a correspondence between a phenomenon at a macroscopic scale of the target structure and at least one of the structure and behavior of components of the target structure at a microscopic scale of the target structure, the model including one or more stages of scaling processing, by referring to the one or more macroscopic features and the one or more microscopic features acquired by the acquisition unit.

[0007] In order to solve the above problem, an information processing device according to one embodiment of the present invention includes an acquisition unit that acquires either one or more macroscopic features that characterize a phenomenon in a target structure at a macroscopic scale, or one or more microscopic features that characterize at least one of the structure and behavior of components of the target structure at a microscopic scale, and a derivation unit that derives the other of the one or more macroscopic features and the one or more microscopic features by inputting the features acquired by the acquisition unit into a model that defines a correspondence between a phenomenon in the target structure at a macroscopic scale and at least one of the structure and behavior of components of the target structure at a microscopic scale, the model including one or more stages of scaling processing.

[0008] In order to solve the above-mentioned problems, a generation method according to one aspect of the present invention includes an acquisition step of acquiring one or more macroscopic feature amounts that characterize a phenomenon at a macroscopic scale of a target structure and one or more microscopic feature amounts that characterize at a microscopic scale of the target structure at a structure and / or behavior of a component of the target structure, and a generation step of generating a model that defines a correspondence between the phenomenon at a macroscopic scale of the target structure and at least one of the structure and behavior of a component of the target structure at a microscopic scale of the target structure, the model including one or more stages of scaling processing, by referring to the one or more macroscopic feature amounts and the one or more microscopic feature amounts acquired in the acquisition step. Contains:

[0009] In order to solve the above-mentioned problems, an information processing method according to one aspect of the present invention includes an acquisition step of acquiring one or more macroscopic features that characterize a phenomenon in a target structure at a macroscopic scale, and one or more microscopic features that characterize at least one of the structure and behavior of components of the target structure at a microscopic scale, and a derivation step of deriving the other of the one or more macroscopic features and the one or more microscopic features by inputting the features acquired in the acquisition step into a model that defines a correspondence between the phenomenon in the target structure at a macroscopic scale and at least one of the structure and behavior of components of the target structure at a microscopic scale, the model including one or more stages of scaling processing.

[0010] In order to solve the above problem, an information processing device according to one embodiment of the present invention includes an acquisition unit that acquires one or more macroscopic features that characterize a phenomenon at a macroscopic scale of a target structure, and a generation unit that references the one or more macroscopic features to generate one or more microscopic features that are correlated with a phenomenon at a macroscopic scale of the target structure and that characterize at least one of the structure and behavior of components of the target structure at the microscopic scale of the target structure, and a model that defines a correspondence between the one or more macroscopic features and the one or more microscopic features, the model including one or more stages of scaling processing.

[0011] In order to solve the above problem, a generation device according to one aspect of the present invention includes an acquisition step of acquiring one or more macroscopic features that characterize a phenomenon at a macroscopic scale of a target structure, and a generation step of generating, by referring to the one or more macroscopic features, one or more microscopic features that are correlated with a phenomenon at a macroscopic scale of the target structure and that characterize at least one of the structure and behavior of components of the target structure at the microscopic scale of the target structure, and a model that defines a correspondence between the one or more macroscopic features and the one or more microscopic features, the model including one or more stages of scaling processing.

[0012] The information processing device according to each aspect of the present invention may be realized by a computer. In this case, the program for realizing the information processing device on a computer by causing the computer to operate as each part (software element) of the information device, and the computer-readable recording medium on which the program is recorded, also fall within the scope of the present invention. [Effects of the Invention]

[0013] According to one aspect of the present invention, it is possible to suitably estimate macroscopic or microscopic properties of a target structure. [Brief explanation of the drawings]

[0014] [Figure 1] 1 is a block diagram showing a configuration of an information processing system according to a first embodiment of the present invention. [Figure 2] FIG. 2 is a diagram for explaining processing by the information processing system according to the first embodiment of the present invention. [Figure 3] FIG. 2 is a diagram for explaining processing by the information processing system according to the first embodiment of the present invention. [Figure 4] FIG. 2 is a diagram for explaining processing by the information processing system according to the first embodiment of the present invention. [Figure 5] 1 is a flowchart showing a flow of processing by the information processing system according to the first embodiment of the present invention. [Figure 6] FIG. 2 is a diagram for explaining processing by the information processing system according to the first embodiment of the present invention. [Figure 7] FIG. 2 is a diagram for explaining processing by the information processing system according to the first embodiment of the present invention. [Figure 8] 1 is a flowchart showing a flow of processing by the information processing system according to the first embodiment of the present invention. [Figure 9] 1 is a flowchart showing a flow of processing by the information processing system according to the first embodiment of the present invention. [Figure 10] FIG. 2 is a diagram for explaining processing by the information processing system according to the first embodiment of the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0015] [Embodiment 1] An information processing system 100 according to this embodiment will be described below. Fig. 1 is a block diagram showing an example of the configuration of the information processing system 100 according to this embodiment. As shown in Fig. 1, the information processing system 100 includes, for example, an information processing device 1 and a measurement device 50.

[0016] (Measuring device 50) The measurement device 50 performs measurements on a target structure OBJ that is the target of processing or analysis by the information processing system 100. As shown in FIG. 1, the measurement device 50 includes a measurement unit 51 and a communication unit 52. As an example, the measurement device 50 measures a target structure OBJ by the measurement unit 51. Current flowing through the target structure OBJ Reaction yield, electronic state, or composition data of the target structure OBJ For example, the measurement unit 51 includes components for performing measurements using XPS (X-ray photoelectron spectroscopy), and performs the various measurements described above. The measured data is provided to the information processing device 1 via the communication unit 52 and the network N.

[0017] The target structure OBJ is configured with, as an example, atoms, ions, electrons, molecules, and local structures of atoms or molecules as constituent elements. Here, in the following description of this embodiment, the term "atom" may also include ions. The target structure OBJ may be any substance or material, and may be, as an example, a solid or liquid whose structure does not change significantly over a predetermined time scale. For example, Solids with a defined crystalline structure (including surfaces or interfaces) A solid or liquid that does not have a specific crystalline structure and is in an amorphous state The target structure OBJ may be, for example, a structure used in a battery, a catalyst, or the like. In this embodiment, one or more stages of scaling processing, which will be described later, can be suitably applied to these target structures OBJ. However, this example does not limit the present embodiment.

[0018] (Information processing device 1) As shown in FIG. 1, the information processing device 1 includes a control unit 10, a storage unit 20, a communication unit 30, and an input / output unit 40.

[0019] (Communication unit 30) The communication unit 30 is an interface for transmitting and receiving data via the network N. As an example, the communication unit 30 receives measurement data regarding the target structure OBJ measured by the measuring device 50, and supplies the received measurement data to the control unit 10.

[0020] (Input / output section 40) The input / output unit 40 is an interface with an input device that accepts input of data and an output device that outputs data. Examples of input devices include, but are not limited to, a microphone, a camera, an eye-gaze input device, a keyboard, and a touchpad. Examples of output devices include, but are not limited to, a speaker and a liquid crystal display.

[0021] (Storage unit 20) The storage unit 20 stores various data referenced by the control unit 10 and various data generated or derived by the control unit 10. As an example, the storage unit 20 stores: Macroscopic data DA ·Microscopic data DI Intermediate data DE Output data OUT Compatible model CM Here, the macroscopic data DA is data on a macroscopic scale, Data indicating macroscopic phenomena or macroscopic properties of the target structure OBJ, in other words, Data characterizing the macroscopic phenomena or properties of the target structure OBJ Here, the macroscopic phenomena and macroscopic properties are examples of macroscopic characteristics of the target structure OBJ. In addition, as an example, the macroscopic data DA includes: One or more macroscopic features that characterize the phenomenon at the macroscopic scale of the target structure OBJ Here, the macroscopic feature includes: At least one of current, reaction yield, electronic state, and composition data on the macroscopic scale of the target structure OBJ The macroscopic feature amount may be, for example, one acquired by measurement using the measurement device 50, one acquired from another device, or one derived or generated by the information processing device 1.

[0022] On the other hand, microscopic data DI is data on a microscopic scale, Data showing the microscopic phenomena or microscopic properties of the target structure OBJ, in other words, Data characterizing the microscopic structure or microscopic behavior of the target structure OBJ Here, the microscopic phenomenon, the microscopic property, the microscopic structure, and the microscopic behavior are examples of the microscopic characteristics of the target structure OBJ. In addition, as an example, the microscopic data DI includes: One or more microscopic features that characterize at least one of the structure and behavior of the components of the target structure OBJ at a microscopic scale of the target structure OBJ. Here, the microscopic feature amount includes: At least one of current, reaction yield, and structural data at the microscopic scale of the target structure OBJ The microscopic feature amount may be, for example, acquired or generated by another device, or may be derived or generated by the information processing device 1.

[0023] In addition, intermediate data DE is data at a mesoscopic scale, Data showing mesoscopic phenomena or mesoscopic properties of the target structure OBJ, in other words, Data characterizing the mesoscopic structure or mesoscopic behavior of the target structure OBJ As an example, the intermediate data DE includes: One or more intermediate features that characterize at least one of the structure and behavior of the components of the target structure OBJ at the mesoscopic scale of the target structure OBJ. The intermediate features include: At least one of the current, reaction yield, and structural data at the mesoscopic scale of the target structure OBJ The intermediate feature amount may be derived or generated by the information processing device 1, for example.

[0024] In this embodiment, the intermediate data DE is not essential. In other words, if the one or more stages of scaling processing described below are configured by one (one stage) scaling processing that associates the microscopic feature values ​​FI with the macroscopic feature values ​​FA, the intermediate data DE may not be necessary.

[0025] The output data OUT is data generated by the control unit 10 with reference to at least one of the macroscopic data DA, the microscopic data DI, and the intermediate data DE. For example, the output data OUT is transmitted to another device via the communication unit 30 or presented to a user via the input / output unit 40.

[0026] Compatible model CM is, Correspondence between the microscopic structure data and the microscopic behavior data in the above microscopic data DI In addition, Correspondence between the macroscopic data DA and the microscopic data DI The correspondence between the macroscopic data DA and the intermediate data DE, and Correspondence between the intermediate data DA and the microscopic data DI The correspondence model CM is a model that defines at least one of the macroscopic data DA, the microscopic data DI, and the intermediate data DE. As an example, the correspondence model CM is generated by the control unit 10 with reference to at least one of the macroscopic data DA, the microscopic data DI, and the intermediate data DE. Specific examples of the correspondence model CM will be described later.

[0027] In this embodiment, the term "microscopic" can include both the meanings of "microscopic spatial scale" and "microscopic time scale." Here, "microscopic spatial scale" typically refers to a spatial scale of about nanometers, and "microscopic time scale" typically refers to a time scale of about picoseconds to nanoseconds. However, these examples do not limit this embodiment.

[0028] In addition, in this embodiment, the terms "intermediate" and "mesoscopic" can include both the meanings of "mesoscopic spatial scale" and "mesoscopic time scale." Here, "mesoscopic spatial scale" typically refers to a spatial scale of about micrometers, and "mesoscopic time scale" typically refers to a time scale of about microseconds. However, these examples do not limit this embodiment.

[0029] In addition, in this embodiment, the term "macroscopic" can include both the meanings of "macroscopic spatial scale" and "macroscopic time scale." Here, "macroscopic spatial scale" typically refers to a spatial scale of the order of millimeters, and "macroscopic time scale" typically refers to a time scale of the order of milliseconds. However, these examples do not limit this embodiment.

[0030] Furthermore, in this specification, the expressions "microscopic," "mesoscopic," and "macroscopic" are used merely to distinguish one scale from another in terms of wording, and are not to be interpreted as being exclusive. For example, the expression "microscopic scale" may include a part of the "mesoscopic scale." Similarly, the expression "mesoscopic scale" may include a part of the "microscopic scale" or a part of the "macroscopic scale." Furthermore, the expression "macroscopic scale" may include a part of the "mesoscopic scale."

[0031] (Control unit 10) 1, the control unit 10 includes an acquisition unit 11, a generation unit 12, a derivation unit 13, and an output data generation unit 14. Specific processes performed by each of these units included in the control unit 10 will be described later.

[0032] (Technical principles of processing by information processing device 1) Prior to a detailed description of the processing by the information processing device 1, an explanation will be given of the technical principle of the processing by the information processing device 1. FIG.

[0033] 2, the data to be processed by the information processing device 1 covers a wide range of spatial scales, from microscopic to macroscopic scales, and also covers a wide range of time scales, from microscopic to macroscopic time scales.

[0034] Figure 2 shows the following microscopic features on a microscopic time scale: ·Microscopic time t1,t2,...,t N The microscopic features FI(t1), FI(t2), . . ., FI(t N ) Here, the microscopic feature at each time may include a plurality of different types of feature. For example, in the example shown in FIG. 2, the microscopic feature FI(t i )teeth, Microscopic currents at the microscopic scale of the target structure OBJ, and Atomic coordinates of all atoms that make up the target structure OBJ FIG. 2 shows an example of time series data of the microscopic current on a microscopic time scale.

[0035] In addition, Figure 2 shows the macroscopic feature quantities on a macroscopic time scale, ·Macroscopic time t'1,t'2,...,t' N The macroscopic features FA(t'1), FA(t'2), . . . , FA(t'N ) Here, the macroscopic feature amount at each time may include a plurality of different types of feature amounts. For example, in the example shown in FIG. 2, the macroscopic feature amount FA(t' k )teeth, Macroscopic currents at the macroscopic scale of the target structure OBJ, and - Valence and composition of atoms (ions) that make up the target structure OBJ 2 illustrates time series data of the macroscopic current on a macroscopic time scale. Here, the time series data is obtained by XPS measurement using the measurement device 50, as an example, but this does not limit the present embodiment.

[0036] The information processing device 1 according to this embodiment performs one or more stages of scaling processing (also referred to as multi-scaling processing) to be described later, thereby associating the microscopic feature values ​​FI with the macroscopic feature values ​​FA. As an example, the information processing device 1 performs the following: Figure 2 shows multiple microscopic times t1, t2, t N The microscopic features FI(t1), FI(t2), . . ., FI(t N ) is a set of · Correspond to the macroscopic feature FA(t'1) at macroscopic time t'1 shown in Figure 2 In other words, the information processing device 1 performs a process of associating a set of microscopic feature amounts at each of a plurality of microscopic times with a macroscopic feature amount at one macroscopic time.

[0037] In this way, the information processing device 1 performs a multi-scaling process to associate microscopic feature quantities and macroscopic feature quantities that differ greatly in time scale and spatial scale. More specifically, as an example of the multi-scaling process, at least one of a combinatorial optimization process using at least some of the macroscopic feature quantities as constraint conditions, a data assimilation process using a Kalman filter, and a machine learning process is executed. Here, more specifically, the combinatorial optimization process is a combinatorial optimization process that uses the valence and composition of atoms (ions) that constitute the target structure OBJ, which are the macroscopic feature quantities, as constraint conditions. However, these examples do not limit the present embodiment.

[0038] Figure 2 shows the data representing the correspondence relationships identified in this way: ·Macroscopic time t'1,t'2,...,t' N The macroscopic and microscopic features FI'(t'1), FI'(t'2), . . ., FI'(t' N ) Here, the macroscopic and microscopic feature quantities FI' are expressed as follows: Macroscopic currents at the macroscopic scale of the target structure OBJ, and Atomic coordinates of all atoms that make up the target structure OBJ, or local (microscopic) 3D structural data of the atoms, molecules, and ions that make up the target structure OBJ In this embodiment, the "three-dimensional structure" or "structure" expressed by the "three-dimensional structure data" or "structure data" is also referred to as a "three-dimensional structure model" or "structure model." Furthermore, the "(three-dimensional) structure data" and "(three-dimensional) structure model" in this embodiment include at least any of the following: relative directions and distances between the multiple atoms, molecules, and ions that make up the target structure OBJ, and information about the crystal structure made up of the multiple atoms, molecules, and ions.

[0039] 3 is a diagram for explaining the multi-scaling process executed by the information processing device 1. In FIG. 3, as shown in the upper part, the multi-scaling process illustrated in FIG. ·Macroscopic time t'1,t'2,...,t' N The macroscopic and microscopic features FI'(t'1), FI'(t'2), . . ., FI'(t' N ) are expressed as one dataset ("time-series dataset DS" in FIG. 3). The information processing device 1 generates combinations of various microscopic features corresponding to macroscopic features ("DS1, DS2, DS3, . . . , DSn" in FIG. 3), Classical or quantum combinatorial optimization processing, Data assimilation processing, Machine learning processing By performing a multi-scaling process including one or more of the processes above, a combination of microscopic features that best matches the macroscopic features is identified. In the example shown in FIG. 3, the microscopic features obtained by the multi-scaling process are: ·Microscopic 3D structure data that best matches the macroscopic current time series is shown.

[0040] The upper part of Fig. 4 is a diagram for explaining an example of multi-scaling processing with reference to the XPS (X-ray photoelectron spectroscopy) measurement results for the target structure OBJ. In the XPS measurement, as shown in the upper part of Fig. 4, for example, an X-ray beam with a diameter of about several µm is irradiated onto the target structure OBJ, and the macroscopic feature amount of the target structure OBJ in the X-ray irradiated area is calculated. Reaction yield, electronic state, or composition data of the target structure OBJ is measured.

[0041] The information processing device 1 includes: As microscopic features or intermediate features to be associated with the macroscopic features, for example, a plurality of local structure models (microscopic structure models) each having a diameter of 0.5 μm generated at atomic level resolution are generated, By performing multi-scale calculations and combinatorial optimization using these local structure models, Identify a combination of local structural models that match (are consistent with) the macroscopic features. Here, the local structure model is an example of the microscopic feature amounts included in the time-series data sets DS1 to DSn described with reference to Fig. 3. The local structure model is also an example of an atom, an ion, an electron, a molecule, and a microscopic three-dimensional structure model having atoms or molecules as components.

[0042] As an example, the information processing device 1 generates 20 patterns of the local structure model and combines 8 patterns from the 20 patterns (in other words, assigns one of the 20 patterns to each of the 8 partial regions that make up the region with a diameter of several μm), thereby expressing the X-ray irradiation region with a diameter of about several μm. In this case, the total number of combinations is 20 8 ~100 million ways Although the total number of combinations is enormous, it is a number that can be executed by combinatorial optimization using the information processing device 1. Of course, the number of local structure models, the number of partial regions, etc. are not limited to the above example. As an example, if the total number of models is 6 (6 patterns) and the 6 patterns are assigned to each of the 9 partial regions, the total number of combinations is 6 9 ~ 10 million ways Similarly, if the total number of models is 10, 10 9 ~ 1 billion ways Similarly, if the total number of models is 20, 20 9 ~ 100 billion ways The total number of these combinations is the number that can be performed in combinatorial optimization processing using pseudo quantum annealing and combinatorial optimization processing using a classical algorithm.

[0043] The above-described processing by the information processing device 1 identifies microscopic feature quantities that match (are consistent with) the macroscopic feature quantities of the target structure OBJ. Here, the multi-scaling processing for deriving microscopic feature quantities that match (are consistent with) the macroscopic feature quantities from the macroscopic feature quantities is performed as follows, as shown in the lower part of FIG. A correspondence model that defines the correspondence between the macroscopic feature and the microscopic feature that matches (is consistent with) the macroscopic feature. A model that defines the correspondence between a phenomenon at the macroscopic scale of the target structure OBJ and a microscopic feature that characterizes at least one of the structure and behavior of the components of the target structure OBJ at the microscopic scale of the target structure OBJ. It has the meaning as.

[0044] As an example, by inputting a microscopic feature into the corresponding model (or its inverse model), information on a macroscopic phenomenon corresponding to the microscopic feature can be obtained (corresponding to "forward design" in the lower part of FIG. 4), and by inputting a macroscopic feature related to a macroscopic phenomenon into the corresponding model (or its inverse model), information on a microscopic structure or microscopic behavior corresponding to the macroscopic feature can be obtained (corresponding to "inverse design" in the lower part of FIG. 4). Note that in this specification, the "inverse model" refers to, for example, when a "corresponding model f" is expressed as y=f(x) using an input x and an output y, x=f -1 (y) gives f -1 When a corresponding model f is given, a search process using the corresponding model is performed to obtain an inverse model f -1 Furthermore, if the "corresponding model" is a model including the "scale-up processing" described later, the "inverse model" can also be defined as a model including the "scale-down processing" described later. Similarly, if the "corresponding model" is a model including the "scale-down processing" described later, the "inverse model" can also be defined as a model including the "scale-up processing" described later.

[0045] (Example 1 of processing flow by information processing device 1) Next, the flow of processing by each unit of the information processing device 1 will be described with reference to Fig. 5. Fig. 5 is a flow chart showing the flow of processing by the information processing device 1.

[0046] (Step S111A) First, in step S111A, the acquisition unit 11 acquires macroscopic data DA. Here, the macroscopic data DA is data on a macroscopic scale, as described above. Data indicating macroscopic phenomena or macroscopic properties of the target structure OBJ, in other words, Data characterizing the macroscopic phenomena or properties of the target structure OBJ As an example, the macroscopic data DA includes: One or more macroscopic features that characterize the phenomenon at the macroscopic scale of the target structure OBJ Here, the macroscopic feature includes: At least one of current, reaction yield, electronic state, and composition data on the macroscopic scale of the target structure OBJ The macroscopic feature FA shown in FIG. 2 is an example of the macroscopic feature included in the macroscopic data DA.

[0047] The macroscopic feature amount may be, for example, obtained by measurement using the measurement device 50 (for example, the XPS measurement illustrated in FIG. 2), or may be obtained from another device. The macroscopic feature amount is not limited to actual measurement data, and may be obtained by a simulation using a macroscopic model, or the like.

[0048] (Step S112A) Subsequently, in step S112A, the generating unit 12 or the acquiring unit 11 generates or acquires microscopic data DI by referring to the one or more macroscopic feature amounts acquired in step S111A. Here, the microscopic data DI is data on a microscopic scale, as described above. Data showing the microscopic phenomena or microscopic properties of the target structure OBJ, in other words, Data characterizing the microscopic structure or microscopic behavior of the target structure OBJ As an example, the microscopic data DI includes: One or more microscopic features that characterize at least one of the structure and behavior of the components of the target structure OBJ at a microscopic scale of the target structure OBJ. Here, the microscopic feature amount includes: At least one of current, reaction yield, and structural data at the microscopic scale of the target structure OBJ The microscopic feature FI shown in FIG. 2 is an example of a microscopic feature included in the microscopic data DI. The local structure model with a diameter of 0.5 μm shown in FIG. 4 is an example of a structure model expressed by the microscopic feature included in the microscopic data DI.

[0049] Of course, examples of microscopic data DI are not limited to the above examples, and examples of local structures may include at least one of molecular adsorption, bond dissociation, multidentate coordination structures, and ion flow paths.

[0050] (Step S121A) Subsequently, in step S121A, the generation unit 12 performs a multi-scaling process to associate the macroscopic data DA acquired in step S111A with the microscopic data DI generated (acquired) in step S112A. Here, the multi-scaling process includes one or more stages of scaling process.

[0051] (Example 1 of multi-scaling processing in step S121A) 6 shows Example 1 of the multi-scaling process executed in this step. In the process according to this example, this step S121A is configured to include the following steps S121A1 to S121A3. However, this configuration does not limit this embodiment. Furthermore, for the sake of convenience, the following description will be given assuming the order of steps S121A1 to S121A3 (in other words, scaling-up process from a microscopic scale to a macroscopic scale). However, the actual multi-scaling process is not limited to this order, and may be executed in the order of steps S121A3 to S121A1 (in other words, scaling-down process from a macroscopic scale to a microscopic scale), or may be executed in such a manner that one step is executed while another step is executed multiple times.

[0052] Furthermore, the "combinatorial optimization process" in the following description may be, for example, quantum combinatorial optimization using a quantum annealing device, pseudo-quantum combinatorial optimization using a pseudo-quantum annealing device such as a simulated annealer, or classical combinatorial optimization using another algorithm or device.

[0053] (Step S121A1) In step S121A1, the generation unit 12 generates a plurality of microscopic structure models ("small-scale surface-interface structures" shown in FIG. 6) by simulation, as shown on the left side of FIG. 6. Here, each of the plurality of microscopic structure models is an example of a structure model expressed by microscopic feature amounts generated by the generation unit 12, which are microscopic feature amounts that characterize the structure of the components of the target structure OBJ at the microscopic scale of the target structure OBJ.

[0054] Note that specific examples of simulation techniques for generating a microscopic structure model do not limit the present embodiment, but examples that can be used include a machine learning model, a quantum machine learning model, a molecular dynamics method, a Monte Carlo method, a genetic algorithm, etc. In other words, the multi-scaling process according to this example can include processes that use a machine learning model, a quantum machine learning model, a molecular dynamics method, a Monte Carlo method, a genetic algorithm, etc. as processes for generating a microscopic structure model.

[0055] As shown in FIG. 6 , the generation unit 12 identifies the behavior of the target structure OBJ at the microscopic scale of the target structure OBJ, as indicated by each of the plurality of microscopic structure models. FIG. 6 shows time-series data on a microscopic time scale of the microscopic current (the “diffusion current” on the left side of FIG. 6 ) indicated by one of the plurality of microscopic structure models (small-scale surface / interface structures) as the behavior indicated by the microscopic structure model. While specific examples of simulation techniques for generating behavior data of the microscopic structure model (the “diffusion current” on the left side of FIG. 6 , for example) are not limited to this embodiment, molecular dynamics simulation, kinetic Monte Carlo simulation, etc., can be used. In other words, the multi-scaling process according to this example may include processing using molecular dynamics simulation, kinetic Monte Carlo simulation, etc., as processing for generating behavior data of the microscopic structure model.

[0056] (Step S121A2) In step S121A2, the generation unit 12 generates a plurality of intermediate structural models (structural models on a mesoscopic scale) ("large-scale surface-interface structure" shown in the center of FIG. 6) by executing a combinatorial optimization process using the plurality of microscopic structural models generated in step S121A1. Here, each of the plurality of intermediate structural models is an example of a structural model expressed by intermediate features generated by the generation unit 12, which intermediate features characterize the structure of the components of the target structure OBJ on the mesoscopic scale of the target structure OBJ.

[0057] Note that the specific example of the simulation method for generating the intermediate structural model does not limit the present embodiment, but examples that can be used include a machine learning model, a Monte Carlo method, a genetic algorithm, etc. In other words, the multi-scaling process according to this example can include a process that uses a machine learning model, a Monte Carlo method, a genetic algorithm, etc. as a process for generating the intermediate structural model.

[0058] As shown in FIG. 6 , the generation unit 12 identifies the behavior of the target structure OBJ at the mesoscopic scale of the target structure OBJ, as indicated by each of the plurality of intermediate structure models. FIG. 6 shows time-series data on the mesoscopic time scale of the current at the mesoscopic scale indicated by the intermediate structure model ("Current" in the center of FIG. 6 ) as the behavior indicated by the intermediate structure model. A specific example of a simulation method for generating behavior data of the intermediate structure model ("Diffusion Current" in the center of FIG. 6 , for example) is not limited to this embodiment, but a large-scale non-equilibrium kinetic Monte Carlo method or the like can be used as an example. In other words, the multi-scaling process according to this example may include a process using a large-scale non-equilibrium kinetic Monte Carlo method or the like as a process for generating behavior data of the intermediate structure model.

[0059] In step S121A2, the generation unit 12 executes the combinatorial optimization process using the intermediate feature values ​​as constraints, for example. In other words, the current indicated by the intermediate feature amount is reproduced by the combination of the microscopic currents generated (identified) in step S121A1. The constraint is that the current indicated by the intermediate feature amount is reproduced by the combination of the microscopic currents generated (identified) in step S121A1. Combinatorial optimization of the multiple microscopic structural models generated in step S121A1 is performed. Here, in order to express the constraint conditions in the combinatorial optimization process, data assimilation processing of the microscopic currents and currents on a mesoscopic scale, using a Kalman filter, may be performed.

[0060] The combination optimization process executed in step S121A2 is an example of a first scaling process in this embodiment that associates the one or more microscopic feature amounts with one or more intermediate feature amounts.

[0061] In addition, in this step S121A2, a machine learning process for associating the microscopic structural model with the intermediate structural model may be executed in the combinatorial optimization process or instead of the combinatorial optimization process.

[0062] (Step S121A3) In step S121A3, the generation unit 12 performs a combinatorial optimization process using the multiple intermediate structural models generated in step S121A2, thereby identifying an intermediate structural model that matches (is consistent with) the macroscopic data DA (the "actual surface interface operating state" shown on the right side of Figure 6).

[0063] 6 shows time-series data of a current on a macroscopic scale flowing through the target structure OBJ as an example of the macroscopic data DA. The time-series data of the current is obtained by DC / AC-XPS coupled measurement, as an example, but this example does not limit the present embodiment.

[0064] In step S121A3, the generation unit 12 executes the combinatorial optimization process using the macroscopic data DA as a constraint, for example. In other words, the macroscopic current indicated by the macroscopic data DA is reproduced by the combination of the mesoscopic-scale currents generated (specified) in step S121A2. A constraint is that the macroscopic current indicated by the macroscopic data DA is reproduced by the combination of the mesoscopic-scale currents generated (specified) in step S121A2, Combinatorial optimization of the intermediate structural models generated in step S121A2 is performed. Here, in order to express the constraint conditions in the combinatorial optimization process, data assimilation processing of the mesoscopic-scale current and the macroscopic current may be performed using a Kalman filter.

[0065] The combinatorial optimization process executed in step S121A3 is an example of a second scaling process in this embodiment that associates the one or more intermediate feature amounts with one or more macroscopic feature amounts.

[0066] In addition, in this step S121A3, a machine learning process for associating the intermediate structural model with the macroscopic data DA may be executed in the combinatorial optimization process or instead of the combinatorial optimization process.

[0067] 6 has been described as an example in which the scaling process according to this embodiment includes a first scaling process and a second scaling process, but this does not limit this embodiment. For example, the scaling process according to this embodiment may be configured by one scaling process (one scale-up process or one scale-down process) that associates the above-mentioned microscopic structural model with the above-mentioned macroscopic data DA. Furthermore, this one scaling process also has the meaning of a model that defines the correspondence between a phenomenon at the macroscopic scale of the target structure OBJ and a microscopic feature amount that characterizes at least one of the structure and behavior of the constituent elements of the target structure OBJ at the microscopic scale of the target structure OBJ.

[0068] According to the findings of the inventors, the scaling process according to this embodiment: Even if one scaling process for associating the above-described microscopic structural model with the above-described macroscopic data DA is included, Even if multiple scaling processes including a first scaling process and a second scaling process are included, By using at least one of the above-mentioned machine learning model, quantum machine learning model, molecular dynamics method, Monte Carlo method, kinetic Monte Carlo method, large-scale non-equilibrium kinetic Monte Carlo method, and genetic algorithm, or a combination of these, it is possible to suitably associate the microscopic structural model with the macroscopic data DA.

[0069] In particular, according to the findings of the inventors, by combining machine learning techniques (machine learning models, quantum machine learning models, etc.) with Monte Carlo techniques (Monte Carlo methods, kinetic Monte Carlo methods, large-scale non-equilibrium kinetic Monte Carlo methods, etc.), it is possible to more appropriately match the microscopic structural model with the macroscopic data DA, whether the scaling process according to this embodiment includes one of the above-mentioned scaling processes or multiple of the above-mentioned scaling processes.

[0070] (Example 2 of multi-scaling processing in step S121A) Fig. 7 shows Example 2 of the multi-scaling process executed in step S121A. The example shown in Fig. 7 shows data assimilation processing of time series data of macroscopic current in the target structure OBJ (left side of Fig. 7) and time series data of microscopic current (or current on a mesoscopic scale) in the target structure OBJ (right side of Fig. 7).

[0071] As shown in FIG. 7, the generation unit 12 performs a data assimilation process (data assimilation (t1) in FIG. 7) between time series data of a microscopic current or a current on a mesoscopic scale for a predetermined time interval (a time interval including time t1 on the right side of FIG. 7) and a macroscopic current at a predetermined time (time t1 on the left side of FIG. 7). Similarly, the generation unit 12 performs a data assimilation process (data assimilation (t2) in FIG. 7) between time series data of a microscopic current or a current on a mesoscopic scale for a predetermined time interval (a time interval including time t2 on the right side of FIG. 7) and a macroscopic current at a predetermined time (time t2 on the left side of FIG. 7). Such a data assimilation process is performed for each time in the time series data of the macroscopic current.

[0072] Furthermore, in the data assimilation process, a multi-scale data assimilation process is performed using a state vector V (e.g., coordinate data, ion composition information) and a scalar quantity (e.g., current). Here, a Kalman filter or the like can be used for the data assimilation process, as an example, but this does not limit the present embodiment.

[0073] The above state vector V is · Macroscopic feature (macroscopic current) and - Microscopic or intermediate features (microscopic 3D structure data or mesoscopic 3D structure data) that match (match) macroscopic features The model for performing the data assimilation process can be considered as an example of correspondence information that defines the correspondence relationship between the above. · Macroscopic feature (macroscopic current) and - Microscopic or intermediate features (microscopic 3D structure data or mesoscopic 3D structure data) that match (match) macroscopic features This can be seen as an example of a correspondence model that defines the correspondence relationship between

[0074] In addition, the multi-scaling process may be configured to execute a machine learning process in conjunction with or instead of the data assimilation process, which associates the time series data of the microscopic current or the current at the mesoscopic scale with the macroscopic current.

[0075] Thus, in this embodiment, as an example, at least one of the first scaling process and the second scaling process includes at least one of a classical or quantum combinatorial optimization process, a data assimilation process, and a machine learning process.

[0076] The generation unit 12 searches for microscopic data DI that matches (matches) the macroscopic data DA by performing the above-described process in step S121A shown in Fig. 5. In other words, by performing the above-described process, the generation unit 12 searches for one or more microscopic feature amounts that match (match) a phenomenon on the macroscopic scale of the target structure OBJ and that characterize at least one of the structure and behavior of the components of the target structure on the microscopic scale of the target structure OBJ.

[0077] (Step S122A) Subsequently, in step S122A, the generation unit 12 determines whether or not microscopic data DI that matches (matches) the macroscopic data DA has been identified by the multi-scaling process executed in step S121A. If microscopic data DI that matches (matches) the macroscopic data DA has been identified (YES in step S122A), the process proceeds to step S123A, and if not (NO in step S122A), the process returns to step 112A, where the generation or acquisition of microscopic data DI is executed again, and in step S121A, a further search for microscopic data DI that matches (matches) the macroscopic data DA is executed.

[0078] (Step S123A) Subsequently, in step S123A, the generation unit 12 determines whether the macroscopic data DA is sufficient as a result of the processing in step S121A. If it is determined that the macroscopic data DA is sufficient (YES in step S123A), the process proceeds to step S124A, and if it is determined that the macroscopic data is insufficient (NO in step S123A), the process returns to S111A and the macroscopic data DA is acquired again.

[0079] (Steps S124A, S125A) In step S124A, the microscopic data DI identified in step S121A, that is, the microscopic data DI that matches (conforms to) the macroscopic data DI, is stored in the storage unit 20.

[0080] Then, in step S125A, a correspondence model CM that defines the correspondence relationship between the macroscopic data DI and the microscopic data DI is stored in the storage unit 20. In other words, in step S125A, the generation unit 12 A model CM that defines the correspondence between a phenomenon at the macroscopic scale of the target structure OBJ and a microscopic feature that characterizes at least one of the structure and behavior of the components of the target structure OBJ at the microscopic scale of the target structure OBJ. is stored in the storage unit 20. The corresponding model CM is, for example, Correspondence information that defines the correspondence between microscopic features and macroscopic features that match the microscopic features As another example, the corresponding model CM includes: First correspondence information that defines a correspondence relationship between a microscopic feature and an intermediate feature that matches (matches) the microscopic feature. Second correspondence information that defines the correspondence between the intermediate feature and the macroscopic feature that matches (matches) the intermediate feature. The intermediate feature may include one or more intermediate feature at a plurality of intermediate scales. Correspondingly, the correspondence model CM may include First correspondence information that defines a correspondence relationship between a microscopic feature and a first intermediate feature at a first mesoscopic scale that matches the microscopic feature. Third correspondence information defining a correspondence relationship between the first intermediate feature and a second intermediate feature at a second mesoscopic scale that matches (matches) the first intermediate feature. Second correspondence information that defines a correspondence relationship between the second intermediate feature and a macroscopic feature that matches (matches) the second intermediate feature. The intermediate feature amount may have three or more scales.

[0081] The correspondence model CM may be regarded as a model that derives microscopic features from macroscopic features, or as a model that derives macroscopic features from microscopic features. When macroscopic features are input to such a correspondence model CM (or its inverse model), microscopic features that match (match) the macroscopic features are output. Similarly, when microscopic features are input to the correspondence model CM (or its inverse model), macroscopic features that match (match) the microscopic features are output.

[0082] As described above, the information processing device 1 an acquisition unit 11 that acquires one or more macroscopic features (e.g., current, reaction yield, electronic state, composition data) that characterize a phenomenon at a macroscopic scale of the target structure OBJ, and one or more microscopic features (e.g., current, reaction yield, structural data) that characterize at least one of the structure and behavior of the constituent elements of the target structure at a microscopic scale of the target structure; a generation unit (12) that generates a model (correspondence model) CM that defines a correspondence relationship between a phenomenon at a macroscopic scale of the target structure (OBJ) and at least one of the structure and behavior of a component of the target structure at a microscopic scale of the target structure, the model including one or more stages of scaling processing, by referring to the one or more macroscopic feature amounts and the one or more microscopic feature amounts acquired by the acquisition unit (11); It is equipped with:

[0083] In addition, the information processing device 1 that executes such processing: an acquisition step of acquiring one or more macroscopic features (e.g., current, reaction yield, electronic state, composition data) that characterize a phenomenon at a macroscopic scale of the target structure OBJ, and one or more microscopic features (e.g., current, reaction yield, structural data) that characterize at least one of the structure and behavior of a component of the target structure at a microscopic scale of the target structure; a generating step of generating a model (corresponding model CM) that defines a correspondence relationship between a phenomenon at a macroscopic scale of the target structure OBJ and at least one of a structure and a behavior of a component of the target structure at a microscopic scale of the target structure, the model including one or more stages of scaling processing, by referring to the one or more macroscopic feature amounts and the one or more microscopic feature amounts acquired in the acquiring step; It can also be expressed as an apparatus (generation apparatus) that executes a generation method (manufacturing method) including the above.

[0084] Furthermore, the above-described information processing device 1 an acquisition unit 11 that acquires one or more macroscopic features (e.g., current, reaction yield, electronic state, composition data) that characterize a phenomenon on a macroscopic scale of the target structure OBJ; With reference to the one or more macroscopic feature amounts, one or more microscopic features (e.g., current, reaction yield, structural data) that correlate with a macroscopic phenomenon of the target structure, and characterize at least one of the structure and behavior of components of the target structure at the microscopic scale of the target structure (e.g., a microscopic structural model); a model (correspondence model CM) that defines a correspondence relationship between the one or more macroscopic feature quantities and the one or more microscopic feature quantities and includes one or more stages of scaling processing; a generator that generates It can also be expressed as having the following.

[0085] In addition, the information processing device 1 that executes such processing: an acquisition step of acquiring one or more macroscopic features (e.g., current, reaction yield, electronic state, composition data) that characterize a phenomenon on a macroscopic scale of the target structure OBJ; With reference to the one or more macroscopic feature amounts, one or more microscopic features (e.g., current, reaction yield, structural data) that correlate with a macroscopic phenomenon of the target structure, and characterize at least one of the structure and behavior of components of the target structure at the microscopic scale of the target structure (e.g., a microscopic structural model); a model (correspondence model CM) that defines a correspondence relationship between the one or more macroscopic feature quantities and the one or more microscopic feature quantities and includes one or more stages of scaling processing; a generation step to generate It can also be expressed as an apparatus (generation apparatus) that executes a generation method (manufacturing method) including the above.

[0086] (Effect 1 of Information Processing Device 1) The information processing device 1 that executes the above process generates a model (correspondence model CM) that defines the correspondence relationship between the one or more macroscopic feature amounts and the one or more microscopic feature amounts, and that includes one or more stages of scaling processing. Here, since the correspondence model CM includes one or more stages of scaling processing, it is possible to appropriately associate a phenomenon at the macroscopic scale of the target structure OBJ with the structure and behavior of the components of the target structure at the microscopic scale of the target structure OBJ. Therefore, the information processing device 1 that executes the above process realizes a technology that can appropriately estimate the macroscopic or microscopic characteristics of the target structure.

[0087] The above-mentioned effect 1 by the information processing device 1 will be explained more specifically as follows. Consider a certain device as an example of a target structure OBJ. The scale of material design for the device is a microscopic scale, which is the scale of chemical reactions and reaction-diffusion. On the other hand, the scale at which the performance of the device is exhibited is a macroscopic scale. Because there is a large scale difference between the microscopic scale and the macroscopic scale, it has been difficult with conventional technology to associate feature quantities at both scales with each other. This can also be expressed as a missing link between the scale of material design and the scale at which device performance is exhibited.

[0088] According to the information processing device 1 described in this embodiment, it is possible to associate feature quantities on a microscopic scale with feature quantities on a macroscopic scale through a multi-stage scaling process. By employing a multi-stage scaling process, the total number of combinations to be optimized in each scaling process (e.g., the first scaling process and the second scaling process described above) falls within a range in which the scaling process can be performed. In other words, by employing the multi-stage scaling process, it is possible to realistically resolve the missing link. In this way, according to the information processing device 1, it is possible to suitably associate a phenomenon on a macroscopic scale of the target structure OBJ with the structure and behavior of the components of the target structure on a microscopic scale of the target structure OBJ.

[0089] (Effect 2 of Information Processing Device 1) The correspondence model CM generated by the information processing device 1 can be suitably applied to the structure near the surface or interface of the target structure OBJ (also called the surface-interface structure). More specifically, the correspondence model CM generated by the information processing device 1 is - Phenomena including the influence of the surface of the target structure OBJ - Phenomena including the influence of the boundary surface or grain boundary of a single material that constitutes the target structure OBJ - Phenomena including the influence of the interface between multiple materials that make up the target structure OBJ The present invention can be suitably applied to the following cases:

[0090] It has been known that the above-mentioned missing link problem is more pronounced for structures near the surface and interface of a device. Therefore, it has been even more difficult to appropriately associate features on a microscopic scale with features on a macroscopic scale for the surface / interface structure of a device. According to the information processing device 1 of this embodiment, by employing the one- or multiple-stage scaling process, the total number of combinations to be optimized in each scaling process (e.g., the first scaling process and the second scaling process) falls within a range in which the scaling process can be performed even for the surface / interface structure.

[0091] Therefore, according to the information processing device 1, it is possible to suitably associate phenomena at the macroscopic scale of the target structure OBJ with the structure and behavior of the components of the target structure at the microscopic scale of the target structure OBJ, even with regard to the surface and interface structure of the target structure OBJ.

[0092] (Example 2 of processing flow by information processing device 1) Next, a second example of the flow of processing by each unit of the information processing device 1 will be described with reference to Fig. 8. This example is an example of processing for deriving macroscopic data DA from microscopic data DI using a correspondence model CM. Fig. 8 is a flow diagram showing the second example of the flow of processing by the information processing device 1.

[0093] (Step S111B) First, in step S111B, the acquisition unit 11 acquires microscopic data DI including one or more microscopic feature amounts. Details of the microscopic data DI have been described above, and therefore will not be described here.

[0094] (Step S131B) Subsequently, in step S131B, the derivation unit 13 inputs the microscopic data DI acquired in step S111B into the corresponding model CM, thereby deriving macroscopic data DA that is consistent (matches) with the microscopic data DI and includes one or more macroscopic feature amounts. Here, as an example, the corresponding model CM can be the corresponding model CM generated in (Example 1 of the processing flow by the information processing device 1) described with reference to FIG. 5.

[0095] The corresponding model CM may be a model that derives a microscopic feature from a macroscopic feature, or may be a model that derives a macroscopic feature from a microscopic feature. As an example, if the corresponding model CM is a model that derives a macroscopic feature from a microscopic feature, when the microscopic feature acquired in step S111B is input to the corresponding model CM, a macroscopic feature that matches (matches) the microscopic feature is output. As another example, if the corresponding model CM is a model that derives a microscopic feature from a macroscopic feature, Generate an inverse model of the corresponding model CM and input the microscopic features acquired in step S111B into the inverse model, or By inputting candidates for macroscopic features into the corresponding model CM and performing a search process, It is possible to obtain macroscopic features that match (match) the microscopic features acquired in step S111B.

[0096] (Step S132B) Subsequently, in step S132B, the output data generation unit 14 generates output data OUT by referring to the macroscopic data DA derived in step S131B. Here, specific examples of the output data OUT generated by the output data generation unit 14 do not limit the present embodiment, but as an example, the output data OUT may include information on one or more macroscopic feature amounts included in the macroscopic data DA. For example, the output data OUT may include The macroscopic properties of the target structure OBJ or the macroscopic phenomena in the target structure OBJ indicated by the macroscopic data DA As an example, the output data OUT may include specific information regarding current, reaction yield, electronic state, and composition data on a macroscopic scale of the target structure OBJ.

[0097] (Example 3 of processing flow by information processing device 1) Next, a third example of the flow of processing by each unit of the information processing device 1 will be described with reference to Fig. 9. This example is an example of processing for deriving microscopic data DI from macroscopic data DA using a correspondence model CM. Fig. 9 is a flow diagram showing the third example of the flow of processing by the information processing device 1.

[0098] (Step S111C) First, in step S111C, the acquisition unit 11 acquires macroscopic data DA including one or more macroscopic feature amounts. Details of the macroscopic data DA have been described above, and therefore will not be described here.

[0099] (Step S131C) Subsequently, in step S131C, the derivation unit 13 inputs the macroscopic data DA acquired in step S111C into the corresponding model CM, thereby deriving microscopic data DI that is consistent (matches) with the macroscopic data DA and includes one or more microscopic feature amounts. Here, as an example, the corresponding model CM can be the corresponding model CM generated in (Example 1 of the processing flow by the information processing device 1) described with reference to FIG. 5.

[0100] The corresponding model CM may be a model that derives a microscopic feature from a macroscopic feature, or may be a model that derives a macroscopic feature from a microscopic feature. As an example, if the corresponding model CM is a model that derives a microscopic feature from a macroscopic feature, when the macroscopic feature acquired in step S111C is input to the corresponding model CM, a microscopic feature that matches (matches) the macroscopic feature is output. As another example, if the corresponding model CM is a model that derives a macroscopic feature from a microscopic feature, Generate an inverse model of the corresponding model CM and input the macroscopic feature amount obtained in step S111C into the inverse model, or By inputting candidates for microscopic features into the corresponding model CM and performing a search process, It is possible to obtain microscopic features that match (match) the macroscopic features acquired in step S111C.

[0101] (Step S132C) Subsequently, in step S132C, the output data generation unit 14 generates output data OUT by referring to the microscopic data DI derived in step S131C. Here, a specific example of the output data OUT generated by the output data generation unit 14 does not limit the present embodiment, but as an example, the output data OUT may include information on one or more microscopic feature amounts included in the microscopic data DI. For example, the output data OUT may include The microscopic data DI indicates the structure and behavior of the components of the target structure OBJ at the microscopic scale of the target structure OBJ. For example, the output data OUT may include specific information regarding current, reaction yield, and structural data at the microscopic scale of the target structure OBJ.

[0102] The upper part of Fig. 10 is a diagram showing an example of the microscopic three-dimensional structure data included in the microscopic data DI derived in step S131C. In the example shown in the upper part of Fig. 10, the microscopic three-dimensional structure data is First material M1 Second material M2 The interface structure ST between the first material M1 and the second material M2 Information is derived for each of the

[0103] The bottom part of Fig. 10 shows an example of output data OUT generated by the output data generation unit 14 with reference to the microscopic three-dimensional structure data included in the microscopic data DI. In this example, the output data generation unit 14 ·Macroscopic properties included in macroscopic data DA and Information on the interface structure ST included in the above microscopic three-dimensional structure data that matches (conforms to) the above macroscopic properties, and , and generates output data OUT stating, "To realize the macroscopic property AAA, bond materials M1 and M2 so that the interface structure becomes BBB." Here, the expression "bond materials M1 and M2 so that the interface structure becomes BBB" indicates a manufacturing method for realizing the interface structure ST included in the microscopic three-dimensional structure data.

[0104] As described above, the information processing device 1 an acquisition unit 11 that acquires one or more macroscopic feature quantities (e.g., current, reaction yield, electronic state, composition data) that characterize a phenomenon at a macroscopic scale of the target structure OBJ, and one or more microscopic feature quantities (e.g., current, reaction yield, structural data) that characterize at least one of the structure and behavior of a component of the target structure OBJ at a microscopic scale of the target structure OBJ; a derivation unit (13) that derives the other of the one or more macroscopic feature quantities and the one or more microscopic feature quantities by inputting the feature quantities acquired by the acquisition unit into a model (correspondence model (CM)) that defines a correspondence relationship between a phenomenon at a macroscopic scale of the target structure (OBJ) and at least one of a structure and a behavior of a component of the target structure at a microscopic scale of the target structure, the model including one or more stages of scaling processing; It is equipped with:

[0105] In addition, the information processing device 1 that executes such processing: an acquisition step of acquiring one or more macroscopic features (e.g., current, reaction yield, electronic state, composition data) that characterize a phenomenon at a macroscopic scale of the target structure OBJ, and one or more microscopic features (e.g., current, reaction yield, structural data) that characterize at least one of the structure and behavior of a component of the target structure at a microscopic scale of the target structure OBJ; a derivation step of deriving the other of the one or more macroscopic feature quantities and the one or more microscopic feature quantities by inputting the feature quantities acquired in the acquisition step into a model (correspondence model CM) that defines a correspondence relationship between a phenomenon at a macroscopic scale of the target structure OBJ and at least one of the structure and behavior of a component of the target structure at a microscopic scale of the target structure, the model including one or more stages of scaling processing; It can also be expressed as a device (generation device) that executes an information processing method (generation method, manufacturing method) including the above.

[0106] (Effect 3 of Information Processing Device 1) According to the information processing device 1 that executes the above process, one of macroscopic features and microscopic features is input into a correspondence model CM that defines a correspondence relationship between a phenomenon at the macroscopic scale of the target structure OBJ and at least one of the structure and behavior of the constituent elements of the target structure at the microscopic scale of the target structure, and the correspondence model CM includes one or more stages of scaling processing, thereby deriving the other. Here, because the correspondence model CM includes one or more stages of scaling processing, it is possible to appropriately associate a phenomenon at the macroscopic scale of the target structure OBJ with the structure and behavior of the constituent elements of the target structure at the microscopic scale of the target structure OBJ. Therefore, the information processing device 1 that executes the above process can appropriately estimate microscopic features that match (correspond) to the macroscopic characteristics of the target structure, or can appropriately estimate macroscopic characteristics that match (correspond) to the microscopic features of the target structure.

[0107] Furthermore, as explained in (Effect 2 of the Information Processing Device 1), the correspondence model CM generated by the information processing device 1 that executes the above processing can be suitably applied to the structure near the surface or interface of the target structure OBJ (also referred to as the surface-interface structure). Therefore, with the information processing device 1, it is possible to suitably estimate microscopic feature amounts that match (match) the macroscopic characteristics of the target structure, and to suitably estimate macroscopic characteristics that match (match) the microscopic feature amounts of the target structure, also with respect to the surface-interface structure of the target structure OBJ.

[0108] (Effect 4 of Information Processing Device 1) The effects of the information processing device 1 can also be expressed as follows. The information processing device 1 calculates three-dimensional structural information of atoms and molecules, which is an example of a microscopic feature, and surface and interface characteristics (e.g., current) associated with (matching with) the structural information by the above-mentioned multi-scale calculation method (one or multiple-stage scaling processing) (processing by the above-mentioned acquisition unit 11 and generation unit 12), On the other hand, the information processing device 1 measures a macroscale physical quantity (e.g., current), which is an example of a macroscopic feature, and then performs data assimilation or machine learning while filling the scale gap (size scale, time scale) between the microscopic feature and the macroscopic feature, thereby determining the surface and interface structure between different materials as three-dimensional structural information of atoms and molecules when the materials are operating as a device (processing by the generation unit 12 and derivation unit 13 described above).

[0109] This allows us to obtain the most reliable 3D structural information (surface and interface operating conditions) of atoms and molecules that can explain experimental measurement data, and this data set can then be used to infer the material information necessary to achieve desired device properties (e.g., current).

[0110] The above-described effects achieved by the information processing device 1 are more pronounced than those achieved by a method using so-called "continuum modeling." With the method using "continuum modeling," it is difficult to extract the correspondence between the "microscopic features" and "macroscopic features" described in the information processing device 1, and as a result, it is difficult for the information processing device 1 to perform simulations with the accuracy that can be achieved. On the other hand, as described above, the information processing device 1 can, for example, acquire highly reliable three-dimensional structural information of atoms and molecules that can explain experimental measurement data, and therefore, can use the data set to inversely estimate material information required to realize desired device properties. [Software implementation example] The functions of the information processing device 1 (hereinafter referred to as the "device") can be realized by a program that causes a computer to function as the device, and a program that causes a computer to function as each control block of the device (particularly each part included in the control unit 10).

[0111] In this case, the device includes a computer having at least one control device (e.g., a processor) and at least one storage device (e.g., a memory) as hardware for executing the program. The control device and storage device execute the program, thereby realizing the functions described in each of the above embodiments.

[0112] The program may be non-transitory and may be recorded on one or more computer-readable recording media. The recording media may or may not be included in the device. In the latter case, the program may be supplied to the device via any wired or wireless transmission medium.

[0113] Furthermore, some or all of the functions of the control blocks can be realized by logic circuits. For example, an integrated circuit in which a logic circuit that functions as each of the control blocks is formed is also included in the scope of the present invention. In addition, the functions of the control blocks can also be realized by, for example, a quantum computer.

[0114] Furthermore, each process described in each of the above embodiments may be executed by AI (Artificial Intelligence). In this case, the AI ​​may run on the control device or on another device (for example, an edge computer or a cloud server).

[0115] The present invention is not limited to the above-described embodiments, and various modifications are possible within the scope of the claims. Embodiments obtained by appropriately combining the technical means disclosed in different embodiments are also included in the technical scope of the present invention.

[0116] (Additional notes) This specification describes at least the following configurations.

[0117] (Configuration A1) an acquisition unit that acquires one or more macroscopic features (current, reaction yield, electronic state, composition data) that characterize a phenomenon at a macroscopic scale of the target structure, and one or more microscopic features (current, reaction yield, structure data) that characterize at least one of the structure and behavior of a component of the target structure at a microscopic scale of the target structure; a generation unit that generates a model that defines a correspondence relationship between a phenomenon on a macroscopic scale of the target structure and at least one of the structure and behavior of a component of the target structure on a microscopic scale of the target structure, the model including one or more stages of scaling processing, by referring to the one or more macroscopic feature amounts (current, reaction yield, electronic state, composition data) acquired by the acquisition unit and the one or more microscopic feature amounts (current, reaction yield, structure data); An information processing device comprising:

[0118] (Configuration A2) In the above-mentioned configuration A1, the components of the target structure characterized by the one or more microscopic features may include at least any of atoms, ions, electrons, molecules, and local structures of atoms or molecules that constitute the target structure.

[0119] (Configuration A3) In the above configuration A2, the macroscopic phenomenon of the target structure that the model targets includes: a phenomenon involving the influence of the surface of the target structure; and Phenomena including the influence of interfaces or grain boundaries of a single material that constitutes the target structure; and Phenomena including the influence of the interface between the multiple materials that make up the target structure At least one of the above may be included.

[0120] (Configuration A4) In the above configurations A1 to A3, the scaling process in one or more stages is a first scaling process for associating the one or more microscopic feature amounts with one or more intermediate feature amounts; a second scaling process for associating the one or more intermediate features with the one or more macroscopic features; may also include:

[0121] (Configuration A5) In the above-mentioned configuration A4, at least one of the first scaling process and the second scaling process is Classical or quantum combinatorial optimization processes, Data assimilation processing, and Machine Learning Processing It may include at least one of the above.

[0122] (Configuration A6) In the above-mentioned configuration A4 or A5, the one or more intermediate feature amounts include intermediate feature amounts at a plurality of intermediate scales; the first scaling process associates the one or more microscopic feature amounts with one or more first intermediate feature amounts at a first intermediate scale; the multi-stage scaling process includes a third scaling process that associates the one or more first intermediate feature amounts with one or more second intermediate feature amounts at a second intermediate scale; The second scaling process may associate the one or more second intermediate features with the one or more macroscopic features.

[0123] (Configuration A7) In the above configurations A1 to A6, the one or more macroscopic feature amounts include: At least one of current, reaction yield, electronic state, and composition data on a macroscopic scale of the target structure is included; The one or more microscopic features include: The data may include current, reaction yield, and / or structural data at the microscopic scale of the target structure.

[0124] (Configuration B1) an acquisition unit that acquires one or more macroscopic features (current, reaction yield, electronic state, composition data) that characterize a phenomenon at a macroscopic scale of the target structure, and one or more microscopic features (current, reaction yield, structure data) that characterize at least one of the structure and behavior of a component of the target structure at a microscopic scale of the target structure; a derivation unit that derives the other of the one or more macroscopic feature quantities (current, reaction yield, electronic state, composition data) and the one or more microscopic feature quantities (current, reaction yield, structure data) by inputting the feature quantities acquired by the acquisition unit into a model that defines a correspondence relationship between a phenomenon on a macroscopic scale of the target structure and at least one of the structure and behavior of components of the target structure on a microscopic scale of the target structure, the model including one or more stages of scaling processing; An information processing device comprising:

[0125] (Configuration B2) In the above-mentioned configuration B1, the components of the target structure characterized by the one or more microscopic features may include at least any of atoms, ions, electrons, molecules, and local structures of atoms or molecules that constitute the target structure.

[0126] (Configuration B3) In the above-mentioned configuration B2, the macroscopic phenomenon of the target structure that the model targets includes: a phenomenon involving the influence of the surface of the target structure; and Phenomena including the influence of interfaces or grain boundaries of a single material that constitutes the target structure; and Phenomena including the influence of the interface between the multiple materials that make up the target structure At least one of the above may be included.

[0127] (Configuration B4) In the above configurations B1 to B3, the scaling process in one or more stages is a first scaling process for associating the one or more microscopic feature amounts with one or more intermediate feature amounts; a second scaling process for associating the one or more intermediate features with the one or more macroscopic features; may also include:

[0128] (Configuration B5) In the above-mentioned configuration B4, at least one of the first scaling process and the second scaling process is Classical or quantum combinatorial optimization processes, Data assimilation processing, and Machine Learning Processing It may include at least one of the above.

[0129] (Configuration B6) In the above-mentioned configuration B4 or B5, the one or more intermediate feature amounts include intermediate feature amounts at a plurality of intermediate scales; the first scaling process associates the one or more microscopic feature amounts with one or more first intermediate feature amounts at a first intermediate scale; the multi-stage scaling process includes a third scaling process that associates the one or more first intermediate feature amounts with one or more second intermediate feature amounts at a second intermediate scale; The second scaling process may associate the one or more second intermediate features with the one or more macroscopic features.

[0130] (Configuration B7) In the above configurations B1 to B6, the one or more macroscopic feature amounts include: At least one of current, reaction yield, electronic state, and composition data on a macroscopic scale of the target structure is included; The one or more microscopic features include: The data may include current, reaction yield, and / or structural data at the microscopic scale of the target structure.

[0131] (Configuration C1) an acquisition step of acquiring one or more macroscopic features (current, reaction yield, electronic state, composition data) that characterize a phenomenon at a macroscopic scale of the target structure, and one or more microscopic features (current, reaction yield, structure data) that characterize at least one of the structure and behavior of a component of the target structure at a microscopic scale of the target structure; a generation step of generating a model that defines a correspondence between a phenomenon on a macroscopic scale of the target structure and at least one of the structure and behavior of a component of the target structure on a microscopic scale of the target structure, the model including one or more stages of scaling processing, by referring to the one or more macroscopic feature amounts (current, reaction yield, electronic state, composition data) acquired in the acquisition step and the one or more microscopic feature amounts (current, reaction yield, structure data); A generating method including:

[0132] (Configuration D1) an acquisition step of acquiring one or more macroscopic features (current, reaction yield, electronic state, composition data) that characterize a phenomenon at the macroscopic scale of the target structure, and one or more microscopic features (current, reaction yield, structure data) that characterize at least one of the structure and behavior of a component of the target structure at the microscopic scale of the target structure; a derivation step of deriving the other of the one or more macroscopic feature quantities (current, reaction yield, electronic state, composition data) and the one or more microscopic feature quantities (current, reaction yield, structure data) by inputting the feature quantities acquired in the acquisition step into a model that defines a correspondence relationship between a phenomenon on a macroscopic scale of the target structure and at least one of the structure and behavior of components of the target structure on a microscopic scale of the target structure, the model including one or more stages of scaling processing; An information processing method comprising:

[0133] (Configuration E1) an acquisition unit that acquires one or more macroscopic feature quantities (current, reaction yield, electronic state, and composition data) that characterize a phenomenon on a macroscopic scale of the target structure; With reference to the one or more macroscopic feature amounts, one or more microscopic features (current, reaction yield, structural data) that are correlated with a phenomenon at a macroscopic scale of the target structure, and characterize at least one of the structure and behavior of components of the target structure at a microscopic scale of the target structure; a model that defines a correspondence relationship between the one or more macroscopic feature quantities and the one or more microscopic feature quantities, the model including one or more stages of scaling processing; a generator that generates An information processing device comprising:

[0134] (Configuration F1) an acquisition step of acquiring one or more macroscopic features (current, reaction yield, electronic state, composition data) that characterize a phenomenon on a macroscopic scale of the target structure; With reference to the one or more macroscopic feature amounts, one or more microscopic features (current, reaction yield, structural data) that are correlated with a phenomenon at a macroscopic scale of the target structure, and characterize at least one of the structure and behavior of components of the target structure at a microscopic scale of the target structure; a model that defines a correspondence relationship between the one or more macroscopic feature quantities and the one or more microscopic feature quantities, the model including one or more stages of scaling processing; a generation step to generate A generation method including:

[0135] (Configuration G1) A program for causing a computer to function as the information processing device according to claim A1 or E1, the program causing a computer to function as the acquisition unit and the generation unit.

[0136] (Configuration H1) A program for causing a computer to function as the information processing device according to claim B1, the program causing a computer to function as the acquisition unit and the derivation unit. [Explanation of symbols]

[0137] 1. Information processing device 10 Control section 11...Acquisition part 12...Generation section 13...Derivation part 14 Output data generation unit 20...Storage section 30 Communications Department 40...Input / output section 100 Information Processing Systems

Claims

1. an acquisition unit that acquires one or more macroscopic feature quantities that characterize a phenomenon at a macroscopic scale of the target structure, and one or more microscopic feature quantities that characterize at least one of the structure and behavior of a component of the target structure at a microscopic scale of the target structure; a generation unit that generates a model that defines a correspondence relationship between a phenomenon at a macroscopic scale of the target structure and at least one of a structure and a behavior of a component of the target structure at a microscopic scale of the target structure, the model including one or more stages of scaling processing, by referring to the one or more macroscopic feature amounts and the one or more microscopic feature amounts acquired by the acquisition unit; An information processing device comprising:

2. The constituent elements of the target structure characterized by the one or more microscopic feature amounts include at least one of atoms, ions, electrons, molecules, and local structures of atoms or molecules that constitute the target structure. The information processing device according to claim 1 .

3. The macroscopic phenomena of the target structure that the model targets include: a phenomenon involving the influence of the surface of the target structure; and Phenomena including the influence of interfaces or grain boundaries of a single material that constitutes the target structure; and Phenomena including the influence of the interface between the multiple materials that make up the target structure At least one of the following is included: The information processing device according to claim 2 .

4. The one or more stages of scaling process include: a first scaling process for associating the one or more microscopic feature amounts with one or more intermediate feature amounts; a second scaling process for associating the one or more intermediate features with the one or more macroscopic features; The information processing device according to claim 1 , further comprising:

5. At least one of the first scaling process and the second scaling process includes: Classical or quantum combinatorial optimization processes, and Data assimilation processing, and Machine Learning Processing 5. The information processing apparatus according to claim 4, comprising at least one of the following:

6. the one or more intermediate feature amounts include intermediate feature amounts at a plurality of intermediate scales; the first scaling process associates the one or more microscopic feature amounts with one or more first intermediate feature amounts at a first intermediate scale; the one or more stages of scaling processing include a third scaling processing that associates the one or more first intermediate feature amounts with one or more second intermediate feature amounts at a second intermediate scale; The second scaling process associates the one or more second intermediate features with the one or more macroscopic features. The information processing device according to claim 4 .

7. The one or more macroscopic features include: At least one of current, reaction yield, electronic state, and composition data on a macroscopic scale of the target structure is included; The one or more microscopic features include: and at least one of current, reaction yield, and structural data at the microscopic scale of the target structure. The information processing device according to claim 1 .

8. an acquisition unit that acquires one or more macroscopic feature quantities that characterize a phenomenon at a macroscopic scale of the target structure, and one or more microscopic feature quantities that characterize at least one of the structure and behavior of a component of the target structure at a microscopic scale of the target structure; a derivation unit that derives the other of the one or more macroscopic feature quantities and the one or more microscopic feature quantities by inputting the feature quantities acquired by the acquisition unit into a model that defines a correspondence relationship between a phenomenon at a macroscopic scale of the target structure and at least one of a structure and a behavior of a component of the target structure at a microscopic scale of the target structure, the model including one or more stages of scaling processing; An information processing device comprising:

9. The constituent elements of the target structure characterized by the one or more microscopic feature amounts include at least one of atoms, ions, electrons, molecules, and local structures of atoms or molecules that constitute the target structure. The information processing device according to claim 8 .

10. The macroscopic phenomena of the target structure that the model targets include: a phenomenon involving the influence of the surface of the target structure; and Phenomena including the influence of interfaces or grain boundaries of a single material that constitutes the target structure; and Phenomena including the influence of the interface between the multiple materials that make up the target structure At least one of the following is included: The information processing device according to claim 9 .

11. The one or more stages of scaling process include: a first scaling process for associating the one or more microscopic feature amounts with one or more intermediate feature amounts; a second scaling process for associating the one or more intermediate features with the one or more macroscopic features; The information processing device according to claim 8 , further comprising:

12. At least one of the first scaling process and the second scaling process includes: Classical or quantum combinatorial optimization processes, and Data assimilation processing, and Machine Learning Processing The information processing device according to claim 11, comprising at least one of the following:

13. the one or more intermediate feature amounts include intermediate feature amounts at a plurality of intermediate scales; the first scaling process associates the one or more microscopic feature amounts with one or more first intermediate feature amounts at a first intermediate scale; the one or more stages of scaling processing include a third scaling processing that associates the one or more first intermediate feature amounts with one or more second intermediate feature amounts at a second intermediate scale; The second scaling process associates the one or more second intermediate features with the one or more macroscopic features. The information processing device according to claim 11.

14. The one or more macroscopic features include: At least one of current, reaction yield, electronic state, and composition data on a macroscopic scale of the target structure is included; The one or more microscopic features include: and at least one of current, reaction yield, and structural data at the microscopic scale of the target structure. The information processing device according to claim 8 .

15. an acquisition step of acquiring one or more macroscopic features that characterize a phenomenon at a macroscopic scale of the target structure, and one or more microscopic features that characterize at least one of the structure and behavior of a component of the target structure at a microscopic scale of the target structure; a generating step of generating a model that defines a correspondence between a phenomenon at a macroscopic scale of the target structure and at least one of a structure and a behavior of a component of the target structure at a microscopic scale of the target structure, the model including one or more stages of scaling processing, by referring to the one or more macroscopic feature amounts and the one or more microscopic feature amounts acquired in the acquiring step; A generating method including:

16. an acquisition step of acquiring one or more macroscopic features that characterize a phenomenon at a macroscopic scale of the target structure, and one or more microscopic features that characterize at least one of the structure and behavior of a component of the target structure at a microscopic scale of the target structure; a derivation step of deriving the other of the one or more macroscopic feature quantities and the one or more microscopic feature quantities by inputting the feature quantities acquired in the acquisition step into a model that defines a correspondence relationship between a phenomenon at a macroscopic scale of the target structure and at least one of a structure and a behavior of a component of the target structure at a microscopic scale of the target structure, the model including one or more stages of scaling processing; An information processing method comprising:

17. an acquisition unit that acquires one or more macroscopic feature quantities that characterize a phenomenon on a macroscopic scale of the target structure; With reference to the one or more macroscopic feature amounts, one or more microscopic feature quantities that are correlated with a phenomenon at a macroscopic scale of the target structure, and that characterize at least one of the structure and behavior of components of the target structure at the microscopic scale of the target structure; a model that defines a correspondence relationship between the one or more macroscopic feature quantities and the one or more microscopic feature quantities, the model including one or more stages of scaling processing; A generator that generates An information processing device comprising:

18. an acquisition step of acquiring one or more macroscopic feature quantities that characterize a phenomenon at a macroscopic scale of the target structure; With reference to the one or more macroscopic feature amounts, one or more microscopic feature quantities that are correlated with a phenomenon at a macroscopic scale of the target structure, and that characterize at least one of the structure and behavior of components of the target structure at the microscopic scale of the target structure; a model that defines a correspondence relationship between the one or more macroscopic feature quantities and the one or more microscopic feature quantities, the model including one or more stages of scaling processing; a generation step to generate A generation method including:

19. 20. A program for causing a computer to function as the information processing device according to claim 1, wherein the program causes the computer to function as the acquisition unit and the generation unit.

20. 9. A program for causing a computer to function as the information processing device according to claim 8, the program causing the computer to function as the acquisition unit and the derivation unit.

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