Patent Publication Number: US-2023153491-A1

Title: System for estimating feature value of material

Description:
INCORPORATION BY REFERENCE 
     This application claims priority to Japanese Patent Application No. 2020-079791 filed on Apr. 28, 2020, the content of which is incorporated herein by reference. 
     TECHNICAL FIELD 
     The present invention relates to a system for estimating a feature value of material. 
     BACKGROUND ART 
     As a method different from an evaluation method of material feature by tests, material feature evaluation by numerical simulation has been performed. In the numerical simulation of a material, a simulator is configured on the basis of physical laws, and a material descriptor is input to the numerical simulator, thereby obtaining a material feature value as a simulation result. Material informatics estimates a feature value of a material using a machine learning model that considers only a response relationship between a feature of the material and the feature value, and selects a target for test and numerical simulation. This makes it possible to optimize the number of times. 
     In such a situation, NPL 1 discloses a technique of performing material feature estimation by a machine learning model by using a numerical simulation result of a material for learning data of machine learning. In addition, PTL 1 discloses a technique of improving generalization performance of a machine learning model by creating a 3D model from image data, performing physical simulation of the model, and generating a large amount of new learning image data. 
     CITATION LIST 
     Patent Literature 
     PTL 1: JP 2017-182129 A 
     Non Patent Literature 
     NPL 1: G. R. Schleder et al., “From DFT to machine learning: recent approaches to materials science—a review”, J. Phys.: Mater. 2 (2019) 032001 
     SUMMARY OF INVENTION 
     Technical Problem 
     A numerical simulation of a material, particularly a numerical simulation by first-principle calculation such as density functional method, can give a good approximate value of a material feature value that is a target of estimation. However, the calculation cost of the numerical simulation is extremely high, and the number of material types for which the numerical simulation can be executed is limited. Therefore, there is a demand for a technique capable of replacing the numerical simulation with a machine learning model and estimating the material feature value with high accuracy with less calculation cost. 
     Solution to Problem 
     One aspect of the present invention is a system that estimates a feature value of a material, the system including one or more processors and one or more storage devices. The one or more storage devices store a material feature estimation model. The material feature estimation model includes a simulation estimation model that estimates a feature value of a simulation result of a material from a descriptor of the material, and a material feature value estimation model that estimates a feature value of the material from an estimation result of the simulation estimation model and a descriptor of the material. The one or more processors inputs a descriptor of a first material into the simulation estimation model to acquire a first simulation estimation result of a feature value of the first material, and inputs the first simulation estimation result and a descriptor of the first material into the material feature value estimation model to acquire a feature estimation value of the first material. 
     Advantageous Effects of Invention 
     According to one aspect of the present invention, a material feature value can be highly accurately and efficiently estimated by a machine learning model. 
    
    
     
       BRIEF DESCRIPTION OF DRAWINGS 
         FIG.  1    schematically illustrates a material feature estimation model that can substitute for a numerical simulator according to an example of the present description. 
         FIG.  2    schematically illustrates a logic configuration example of a material feature estimation device according to an example of the present description. 
         FIG.  3    illustrates a hardware configuration example of a material feature estimation device. 
         FIG.  4    illustrates a configuration example of a tested material database. 
         FIG.  5    illustrates a configuration example of an untested material database. 
         FIG.  6    illustrates a configuration example of a descriptor list output by a descriptor calculation unit. 
         FIG.  7    illustrates a flowchart of an example of overall processing of the material feature estimation device. 
         FIG.  8    schematically illustrates distribution of a material in a two-dimensional space. 
         FIG.  9    illustrates a flowchart of details of learning of a simulation estimation model. 
         FIG.  10    illustrates a flowchart of details of learning of a material feature value estimation model. 
         FIG.  11    illustrates an image example of a material feature estimation result displayed on a monitor by a material feature estimation result display unit. 
     
    
    
     DESCRIPTION OF EMBODIMENTS 
     In the following, when it is necessary for convenience, the description will be divided into a plurality of sections or examples, but unless otherwise specified, they are not unrelated to one another, and they are in a relationship where one is a modification, detail, supplementary explanation, and the like of some or all of the others. In the following, when referring to the number of elements and the like (including number of items, numerical value, amount, range, and the like), the number is not limited to a specific number unless otherwise stated or unless clearly limited to the specific number in principle, and the number may be equal to greater than or equal to or less than the specific number. 
     The present system may be a physical computer system (one or more physical computers) or a system constructed on a calculation resource group (a plurality of calculation resources) such as a cloud infrastructure. The computer system or the calculation resource group includes one or more interface devices (for example, including a communication device and an input/output device), one or more storage devices (for example, including a memory (main storage) and an auxiliary storage device), and one or more processors. 
     In a case where the function is implemented by executing a program by a processor, determined processing is appropriately performed using the storage device and/or the interface device, and thus, the function may be at least a part of the processor. The processing described with the function as the subject may be processing performed by a processor or a system including the processor. The program may be installed from a program source. The program source may be, for example, a program distribution computer or a computer-readable storage medium (for example, a computer-readable non-transitory storage medium). The description of each function is an example, and a plurality of functions may be put together into one function or one function may be divided into a plurality of functions. 
     [Outline] 
     Hereinafter, a technique capable of efficiently and highly accurately estimating a material feature value will be disclosed. Examples of the present description enable a numerical simulator of a material feature to be replaced by a machine learning model (material feature estimation model).  FIG.  1    schematically illustrates a material feature estimation model  20  that can substitute for a numerical simulator  11  in an example of the present description. 
     The numerical simulator  11  outputs a simulation result  13  of a predetermined feature value of a material from a chemical structural formula  12  of the material that has been input. In the example of  FIG.  1   , the numerical simulator  11  receives a chemical structural formula as input and outputs one type of material feature value, but in another example, the numerical simulator  11  may receive a descriptor of a chemical structural formula as input and may output a plurality of types of material feature values. 
     The material feature estimation model  20  includes a simulation estimation model  21  that estimates a simulation result of the numerical simulator  11  and a material feature value estimation model  25 . The simulation estimation model  21  receives a descriptor  22  (vector) of a material as input, and estimates a simulation result (material feature value) of the numerical simulator  11 . The descriptor is a vector representing a feature of a material in a multivariate manner. 
     The descriptor includes a plurality of elements (feature), and represents a feature corresponding to each element, for example, a molecular weight or an element mixing ratio. In the example of  FIG.  1   , the simulation estimation model  21  outputs one type of material feature value, but may output a plurality of types of material feature values included in the simulation result of the numerical simulator  11 . The simulation estimation model  21  is optimized (trained) based on an error between the simulation result  13  of the numerical simulator  11  and an estimation result  23  of the simulation estimation model  21 . 
     The material feature value estimation model  25  estimates one or a plurality of types of material feature values that are identical to the material feature value estimated by the simulation estimation model  21 . In the example of  FIG.  1   , the material feature value estimation model  25  estimates one specific type of material feature value. 
     The material feature value estimation model  25  receives, as input, a vector  26  in which a descriptor  24  of the material and the simulation result estimation value  23  of the material feature estimation model  20  are combined. The descriptor  24  may be identical to or different from the descriptor  22  input to the simulation estimation model  21 . The vector  26  is a descriptor in which the descriptor  22  of the material is extended. The material feature value estimation model  25  estimates a predetermined material feature value from an extension descriptor  26 , and outputs its material feature estimation value  27 . The material feature estimation value  27  is an estimation value of the material feature by the material feature estimation model  20 . 
     As described above, the material feature value estimation model  25  estimates the feature value of the material on the basis of the estimation result of the simulation estimation model  21  that estimates the simulation result of the numerical simulator  11  and the descriptor of the material. Due to this, the material feature value can be estimated with high accuracy by the machine learning model that can perform arithmetic operation more efficiently than the simulator. 
     Note that regression algorithms used by the simulation estimation model  21  and the material feature value estimation model  25  are discretionary, and these algorithms may be identical or different. For example, a discretionary algorithm can be selected from various regression algorithms including random forest, support vector machine, Gaussian process regression, and neural network. The material feature estimation model  20  is applicable to any of an organic-inorganic compound and an inorganic compound. The descriptor can be generated from a chemical formula, that is, any of a structural formula and a composition formula. Hereinafter, a more specific configuration of the example of the present description will be described. 
     EXAMPLE 1 
       FIG.  2    schematically illustrates a logic configuration example of the material feature estimation device according to the example of the present description. A material feature estimation device  100  stores a tested material database  102 , an untested material database  103 , and a simulation result database  110 . 
     The material feature estimation device  100  stores a descriptor calculation unit  104 , a simulation execution target selection unit  105 , a material feature value estimation model learning unit  106 , a simulation execution unit  107 , a simulation estimation unit  108 , a simulation estimation model learning unit  109 , a material feature value estimation unit  111 , and a material feature estimation result display unit  112 . These are programs, and one or more processors of the material feature estimation device  100  can operate as corresponding functional units by executing these programs. Note that a discretionary function of the material feature estimation device  100  can be implemented in a discretionary program. 
     The descriptor calculation unit  104  generates a descriptor from a chemical formula by a predetermined method. The descriptor represents a feature of the material indicated by the chemical formula. The descriptor is represented by a vector including a plurality of elements (feature). A feature corresponding to each element, for example, a molecular weight or an element mixing ratio is represented. Hereinafter, the organic compound material represented by the chemical structural formula will be described as an example of the estimation target material. Examples of the present description are also applicable to an inorganic compound material represented by a composition formula, for example. 
     The simulation execution target selection unit  105  selects a material for which simulation is to be executed by the numerical simulator  11  in order to generate learning data for learning (training) the material feature estimation model  20 . The simulation execution unit  107  executes simulation by the numerical simulator  11 . 
     The simulation estimation model learning unit  109  performs learning (training) of the simulation estimation model  21  that estimates a simulation result. The simulation estimation unit  108  calculates a simulation result estimation value of the material feature by the learned simulation estimation model  21 . 
     The material feature value estimation model learning unit  106  performs learning (training) of the material feature value estimation model  25  that estimates a material feature value. The material feature value estimation unit  111  calculates an estimation value of the material feature value by the learned material feature value estimation model  25 . The material feature estimation result display unit  112  presents the user a material feature estimation result by the material feature value estimation unit  111 . 
     The tested material database  102  stores test results of predetermined material feature values of various materials. The untested material database  103  stores data of materials for which tests on material feature values have not been executed. The simulation result database  110  stores a simulation result by the numerical simulator  11 . 
       FIG.  3    illustrates a hardware configuration example of the material feature estimation device  100 . The material feature estimation device  100  has a computer configuration and includes a processor  151  having calculation performance and a DRAM  152  that gives a volatile temporary storage area for storing programs to be executed by the processor  151  and data. The material feature estimation device  100  further includes a communication device  153  that performs data communication with another device, and an auxiliary storage device  154  that gives a permanent information storage area using a hard disk drive (HDD), a flash memory, and the like. 
     For example, the auxiliary storage device  154  stores programs such as the descriptor calculation unit  104 , the simulation execution target selection unit  105 , the material feature value estimation model learning unit  106 , the simulation execution unit  107 , the simulation estimation unit  108 , the simulation estimation model learning unit  109 , the material feature value estimation unit  111 , and the material feature estimation result display unit  112 . 
     The auxiliary storage device  154  further stores various data such as the tested material database  102 , the untested material database  103 , and the simulation result database  110 . The program to be executed by the processor  151  and processing target data are loaded from the auxiliary storage device  154  to the DRAM  152 . 
     The material feature estimation device  100  includes an input device  155  that receives operation from the user, and a monitor  156  (example of an output device) that presents the user an output result in each process. Note that the function of the material feature estimation device  100  may be separately implemented in a plurality of devices. Thus, the material feature estimation device  100  includes one or more storage devices and one or more processors. 
       FIG.  4    illustrates a configuration example of the tested material database  102 . The tested material database  102  associates a material with a test result of a feature value of the material. Specifically, the tested material database  102  includes a number column  301 , a structural formula (SMILES) column  302 , and a material feature measurement value column  303 . 
     The number column  301  identifies each record in the tested material database  102 . The structural formula (SMILES) column  302  indicates the chemical structural formula of the material. In the example of  FIG.  4   , the chemical structural formula is expressed according to simplified molecular input line entry system (SMILES) notation. It is possible to use a discretionary expression format of a chemical structural formula that can generate a descriptor. The material feature measurement value column  303  indicates a test result of a predetermined feature value of each chemical structural formula. 
       FIG.  5    illustrates a configuration example of the untested material database  103 . The untested material database  103  stores a chemical structural formula of a material for which a test of the material feature value has not been conducted. The feature value of the material selected from the untested material database  103  is estimated by the material feature estimation model  20 . 
     In the example shown in  FIG.  5   , the untested material database  103  includes a number column  401  and a structural formula (SMILES) column  402 . The number column  401  identifies each record in the untested material database  103 . The structural formula (SMILES) column  402  indicates SMILES expression of a chemical structural formula of a material. 
       FIG.  6    illustrates a configuration example of a descriptor list  500  output by the descriptor calculation unit  104 . The descriptor calculation unit  104  generates a descriptor from the chemical structural formula of the SMILES expression acquired from the tested material database  102  or the untested material database  103 , and generates the descriptor list  500 . 
     The descriptor list  500  includes a number column  501  and a column of each descriptor element. In the example of  FIG.  6   , the descriptor includes  1000  description elements, and columns of four descriptor elements are indicated by reference numerals  502  to  505  as an example. The value of the number column  501  corresponds to the value of the number column in the database from which the chemical structural formula for generating the descriptor list has been acquired. 
       FIG.  7    illustrates a flowchart of an example of overall processing of the material feature estimation device  100 . In step S 101 , the descriptor calculation unit  104  acquires a chemical structural formula of a material from the tested material database  102  and the untested material database  103 , and calculates a descriptor of each material. The descriptor calculation unit  104  generates a descriptor list of each of the tested material database  102  and the untested material database  103 . 
     In step S 102 , the simulation execution target selection unit  105  receives the descriptor of the material of each of the two databases  102  and  103  from the descriptor calculation unit  104 , and selects the materials for which simulation is executed on the basis of the descriptors. The simulation result is used for learning of the material feature estimation model  20 . 
     The numerical simulation requires many calculation resources. From the viewpoint of efficient and effective learning of the material feature estimation model  20 , it is important to select a material for which simulation is to be executed by the numerical simulator  11 . 
     From the viewpoint of learning of the simulation estimation model  21 , it is possible to improve generality of the simulation estimation model  21  by preparing simulation results of various types of qualitatively different materials (request  1 ). For the purpose of learning of the material feature value estimation model  25 , it is necessary to execute numerical simulation on a tested material (request  2 ). 
     The simulation execution target selection unit  105  determines the priority order of numerical simulation candidates so as to satisfy the requests  1  and  2 , and selects a higher-order material as a simulation target. 
     From the viewpoint of the request  1 , the simulation execution target selection unit  105  determines the simulation execution target on the basis of the similarity between materials. The similarity between materials can be calculated from a distance between, for example, descriptors or vectors obtained from descriptors. 
     For example, the simulation execution target selection unit  105  reduces the dimension of the descriptor of a candidate material, and analyzes the distribution of the materials in a low-dimensional space. For dimension reduction, for example, a dimension reduction algorithm such as t-distributed stochastic neighbor embedding (t-SNE) can be used. A predetermined element of the descriptor may be extracted to constitute a low-dimensional space. The subsequent calculation amount is reduced by the dimension reduction. 
       FIG.  8    schematically illustrates distribution of a material in a two-dimensional space. The circles indicate untested materials and the stars indicate tested materials. The simulation execution target selection unit  105  performs clustering of materials by similarity in the material space. Each cluster is configured of a similar material. In the example of  FIG.  8   , three clusters  601  to  603  are configured. 
     In order to satisfy the above request  1 , it is preferable not to select many materials from a biased cluster but to unbiasedly select materials from different clusters. In order to satisfy the above request  2 , it is preferable to preferentially select a tested material. 
     Therefore, the simulation execution target selection unit  105  selects a material that is a simulation execution target, for example, in accordance with the following priority order. (1) Tested material near the cluster center, (2) material in the cluster not containing any tested materials, (3) untested material near the cluster center, (4) tested material deviating from the above conditions, and (5) untested material deviating from the above conditions. 
     The simulation execution target selection unit 105 searches for a material that satisfies the conditions in the order of the above conditions (1) to (5), for example. The material near the cluster center is, for example, a material within a predetermined distance from the cluster center. For example, when the total number of found materials or the number of tested materials reaches a predetermined number, the simulation execution target selection unit  105  ends the search. Thus, the found material is determined as a simulation execution target and included in the material list. 
     Returning to  FIG.  7   , in step S 103 , the simulation execution unit  107  receives the material list from the simulation execution target selection unit  105 , and executes simulation of the material in the material list to calculate the material feature value. The material list may indicate, for example, a database identifier, the number in the database, and a descriptor. 
     The simulation execution unit  107  acquires the chemical structural formula of the material indicated by the material list from the tested material database  102  and the untested material database  103 , and executes these simulations. When a descriptor is necessary for the simulation, the simulation execution unit  107  requests the descriptor calculation unit  104  to calculate the descriptor. 
     In step S 104 , the simulation execution unit  107  stores the simulation result into the simulation result database  110 . The simulation result database  110  includes, for example, a number column, a structural formula (SMILES) column, and a column of a simulation result of a material feature value. The number column identifies a record in the simulation result database  110 , for example. The simulation result database  110  may indicate the presence or absence of the test result of the material. 
     In step S 105 , the simulation estimation model learning unit  109  performs learning of the simulation estimation model  21  that estimates a simulation result from a descriptor.  FIG.  9    illustrates a flowchart of details of learning (S 105 ) of the simulation estimation model  21 . 
     In step S 201 , the simulation estimation model learning unit  109  acquires a simulation result from the simulation result database  110 . In step S 202 , the simulation estimation model learning unit  109  receives a calculated descriptor from the descriptor calculation unit  104 . Specifically, the simulation estimation model learning unit  109  passes the chemical structural formula of the simulation to the descriptor calculation unit  104  and acquires the descriptors. 
     In step S 203 , the simulation estimation model learning unit  109  performs learning of the simulation estimation model based on the acquired descriptor and the material feature value indicated by the simulation result. The simulation estimation model learning unit  109  retains information on an initial configuration of the simulation estimation model  21  in advance, and configures the simulation estimation model in accordance with the information. A discretionary type of machine learning model can be used as the simulation estimation model  21 . 
     The simulation estimation model learning unit  109  sequentially inputs descriptors into the simulation estimation model  21  and acquires an output simulation result estimation value (material feature value). The simulation estimation model learning unit  109  optimizes the simulation estimation model  21  by updating parameters of the simulation estimation model  21  on the basis of an error between the simulation result estimation value and the material feature value of the acquired simulation result. Finally, in step S 204 , the simulation estimation model learning unit  109  passes the learned simulation estimation model  21  to the simulation estimation unit  108 . 
     Returning to  FIG.  7   , in step S 106 , the simulation estimation unit  108  receives the learned simulation estimation model  21  from the simulation estimation model learning unit  109 . 
     The simulation estimation unit  108  further receives a descriptor of a material for which simulation has not been executed from the descriptor calculation unit  104 . Specifically, the simulation estimation unit  108  selects the chemical structural formula of a material that is stored in the untested material database  103  and not stored in the simulation result database  110 , and requests the descriptor calculation unit  104  to calculate the descriptor. 
     Furthermore, the simulation estimation unit  108  sequentially inputs the descriptors acquired from the descriptor calculation unit  104  to the learned simulation estimation model  21  to calculate an estimation value of the simulation result. 
     Next, in step S 107 , the material feature value estimation model learning unit  106  performs learning of the material feature value estimation model  25 .  FIG.  10    illustrates a flowchart of details of learning (S 107 ) of the material feature value estimation model  25 . 
     In step S 301 , the material feature value estimation model learning unit  106  acquires a simulation result of the tested material from the simulation result database  110 . The material feature value estimation model learning unit  106  can identify a tested material by referring to the tested material database  102 , for example. The simulation result database  110  may indicate the presence or absence of the test. 
     In step S 302 , the material feature value estimation model learning unit  106  receives the calculated descriptor from the descriptor calculation unit  104 . Specifically, the material feature value estimation model learning unit  106  passes the chemical structural formula of the simulation result acquired in step S 301  to the descriptor calculation unit  104 , and acquires the descriptors. 
     In step S 303 , the material feature value estimation model learning unit  106  acquires a test result of the material feature value from the tested material database  102 . Specifically, the material feature value estimation model learning unit  106  acquires, from the tested material database  102 , the material feature value of the simulation result acquired in step S 301 . 
     In step S 304 , the material feature value estimation model learning unit  106  performs learning of the material feature value estimation model  25  based on the acquired simulation result, the acquired descriptor, and the test result of the material feature value. The simulation estimation model learning unit  109  retains information on an initial configuration of the material feature value estimation model  25  in advance, and configures the material feature value estimation model  25  in accordance with the information. A discretionary type of machine learning model can be used as the material feature value estimation model  25 . 
     The material feature value estimation model learning unit  106  sequentially inputs, into the material feature value estimation model  25 , extension descriptors (vectors) in which the descriptor and the simulation result of the material feature value are combined, and acquires the output material feature estimation value. The material feature value estimation model learning unit  106  optimizes the material feature value estimation model  25  by updating parameters of the material feature value estimation model  25  on the basis of an error between the material feature estimation value and the material feature value of the acquired test result. Finally, in step S 304 , the material feature value estimation model learning unit  106  passes the learned material feature value estimation model  25  to the material feature value estimation unit  111 . 
     As described above, the learning of the material feature value estimation model  25  uses the simulation result by the numerical simulator. This makes it possible to configure the material feature value estimation model  25  that is more appropriate. In another example, the learning of the material feature value estimation model  25  may use the estimation result of the learned simulation estimation model  21 . 
     Returning to  FIG.  7   , in step S 108 , the material feature value estimation unit  111  calculates an estimation value of the material feature value of an untested material by the learned material feature value estimation model  25 . Specifically, the material feature value estimation unit  111  receives the learned material feature value estimation model  25  from the material feature value estimation model learning unit  106 . 
     The material feature value estimation unit  111  receives a descriptor of an untested material from the descriptor calculation unit  104 . For example, the material feature value estimation unit  111  acquires a chemical structural formula from the untested material database  103 , and requests the descriptor calculation unit  104  to generate a descriptor together with them. 
     The material feature value estimation unit  111  receives the simulation result estimation value of the untested material calculated in step S 106  from the simulation estimation unit  108 . The material feature value estimation unit  111  acquires a simulation result of an untested material from the simulation result database  110 . 
     The material feature value estimation unit  111  combines and inputs, to the material feature value estimation model  25 , the descriptor with the simulation result estimation value (material feature value) or the simulation result (material feature value). The material feature value estimation model  25  calculates an estimation value of the feature value of the untested material represented by the input descriptor. 
     Finally, in step S 109 , the material feature estimation result display unit  112  receives the chemical structural formula of the untested material and the material feature estimation result from the material feature value estimation unit  111 . The material feature estimation result display unit  112  presents the user the chemical structural formula and the material feature estimation result. 
       FIG.  11    illustrates an image example of the material feature estimation result displayed on the monitor  156  by the material feature estimation result display unit  112 . In the example of  FIG.  11   , the image indicates the chemical structural formulae of the selected materials and the estimation values of the material feature values corresponding to them. With reference to the displayed chemical structural formulae and material feature values, the user can determine the chemical structural formula for actually executing a test or simulation. The estimation result is saved by a save button. 
     The present invention is not limited to the example described above, and includes various modifications. For example, the above-described example has been described in detail for easy understanding of the present invention, and is not necessarily limited to those having all the described configurations. A part of the configuration of a certain example can be replaced by the configuration of another example, and the configuration of another example can be added to the configuration of a certain example. A part of the configuration of each example can be added to, deleted from, or replaced by another configuration. 
     Some or all of the above-described configurations, functions, processing units, and the like may be implemented by hardware, for example, by designing with an integrated circuit. The above configurations, functions, and the like may be implemented by software by a processor interpreting and executing a program that implements each function. Information such as a program, a table, and a file for implementing each function can be stored in a memory, a recording device such as a hard disk and a solid state drive (SSD), or a recording medium such as an IC card and an SD card. 
     The control lines and the information lines indicate what is considered to be necessary for the description, and do not necessarily indicate all the control lines and the information lines on the product. In practice, almost all the configurations may be considered to be connected to one another.