WO2023008172A1 - 探索方法、探索システム、プログラム、予測モデル構築方法、及び予測モデル構築装置 - Google Patents

探索方法、探索システム、プログラム、予測モデル構築方法、及び予測モデル構築装置 Download PDF

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WO2023008172A1
WO2023008172A1 PCT/JP2022/027343 JP2022027343W WO2023008172A1 WO 2023008172 A1 WO2023008172 A1 WO 2023008172A1 JP 2022027343 W JP2022027343 W JP 2022027343W WO 2023008172 A1 WO2023008172 A1 WO 2023008172A1
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energy
initial
atomic arrangement
structures
prediction model
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English (en)
French (fr)
Japanese (ja)
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圭 網井
昌樹 大越
幹也 藤井
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Panasonic Intellectual Property Management Co Ltd
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Panasonic Intellectual Property Management Co Ltd
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Priority to JP2023538411A priority Critical patent/JPWO2023008172A1/ja
Priority to CN202280051365.XA priority patent/CN117716431A/zh
Publication of WO2023008172A1 publication Critical patent/WO2023008172A1/ja
Priority to US18/408,653 priority patent/US20240144045A1/en
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    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N20/00—Machine learning
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N5/00—Computing arrangements using knowledge-based models
    • G06N5/02—Knowledge representation; Symbolic representation
    • G06N5/022—Knowledge engineering; Knowledge acquisition
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/04—Architecture, e.g. interconnection topology
    • G06N3/045—Combinations of networks
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N7/00—Computing arrangements based on specific mathematical models
    • G06N7/01—Probabilistic graphical models, e.g. probabilistic networks
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16C—COMPUTATIONAL CHEMISTRY; CHEMOINFORMATICS; COMPUTATIONAL MATERIALS SCIENCE
    • G16C20/00—Chemoinformatics, i.e. ICT specially adapted for the handling of physicochemical or structural data of chemical particles, elements, compounds or mixtures
    • G16C20/70—Machine learning, data mining or chemometrics
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16C—COMPUTATIONAL CHEMISTRY; CHEMOINFORMATICS; COMPUTATIONAL MATERIALS SCIENCE
    • G16C60/00—Computational materials science, i.e. ICT specially adapted for investigating the physical or chemical properties of materials or phenomena associated with their design, synthesis, processing, characterisation or utilisation
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
    • Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
    • Y02P90/30—Computing systems specially adapted for manufacturing

Definitions

  • the present disclosure relates to a search method and the like for searching for a stable structure of atomic arrangement for the composition of a material.
  • Non-Patent Document 1 Conventionally, structural optimization techniques have been developed to obtain stable atomic arrangement structures by first-principles calculations (see, for example, Non-Patent Document 1).
  • Non-Patent Document 2 discloses a method of estimating characteristic values such as energy using machine learning for input of atomic arrangement structure.
  • the present disclosure provides a search method and the like that can efficiently search for a stable structure of atomic arrangement for the composition of a material.
  • a search method is a search method for searching for a stable structure of atomic arrangement in a three-dimensional space for a composition of a material, wherein a computer performs the three-dimensional space that the composition of the material can take
  • a first step of obtaining a plurality of initial structures that are structures of the atomic arrangement in , and performing structural optimization on some initial structures of the plurality of initial structures, and performing structural optimization of the atomic arrangement A second step of calculating a first energy corresponding to a structure and using a predictive model for another initial structure of the plurality of initial structures to perform structural optimization on the other initial structure.
  • Computer-readable recording media include non-volatile recording media such as CD-ROMs (Compact Disc-Read Only Memory).
  • FIG. 1 is a block diagram showing an overall configuration including a search system according to Embodiment 1.
  • FIG. 2 is a diagram showing an example of data stored in a material database according to Embodiment 1.
  • FIG. 3 is a diagram illustrating an example of a process in which a generation unit according to Embodiment 1 generates an initial structure;
  • FIG. 4 is a diagram illustrating an example of a process of generating an initial structure by a generation unit according to Embodiment 1.
  • FIG. 5 is a diagram illustrating an example of a process of generating an initial structure by a generation unit according to Embodiment 1.
  • FIG. 6 is a diagram depicting an example of a plurality of initial structures generated by a generation unit according to Embodiment 1;
  • FIG. 1 is a block diagram showing an overall configuration including a search system according to Embodiment 1.
  • FIG. 2 is a diagram showing an example of data stored in a material database according to Embodiment 1.
  • FIG. 3 is a diagram
  • FIG. 7A is a diagram showing an example of a configuration of an initial structure generated by a generation unit according to Embodiment 1.
  • FIG. 7B is a diagram showing another example of the configuration of the initial structure generated by the generation unit according to Embodiment 1.
  • FIG. 8 is a diagram depicting an example of data stored in a structure storage unit according to Embodiment 1;
  • FIG. 9 is a diagram illustrating an example of a process of calculating first energy by a calculation unit according to Embodiment 1.
  • FIG. 10 is a diagram showing an example of data stored in a calculation result storage unit according to Embodiment 1.
  • FIG. 11 is a diagram illustrating an example of a process of machine-learning a prediction model by a learning unit according to Embodiment 1.
  • FIG. 12 is a diagram illustrating an example of a process of predicting the second energy by the prediction unit according to Embodiment 1.
  • FIG. 13 is a diagram illustrating an example of data generated by a comparison unit according to Embodiment 1;
  • FIG. 14 is a diagram illustrating an evaluation example of prediction accuracy of the prediction unit according to Embodiment 1.
  • FIG. 15 is a diagram showing a result of verifying the prediction accuracy of the prediction unit according to Embodiment 1.
  • FIG. 16 is a diagram showing a result of verifying the correlation between the prediction accuracy of the prediction unit and the learning data ratio according to Embodiment 1.
  • FIG. 17 is a flowchart showing an operation example of the search system according to Embodiment 1.
  • FIG. 18 is a block diagram showing an overall configuration including a search system according to Embodiment 2.
  • FIG. 19 is a diagram depicting an example of data stored in a calculation result storage unit according to Embodiment 2;
  • FIG. 20 is a diagram illustrating an example of a process of machine-learning a prediction model by a learning unit according to Embodiment 2.
  • FIG. 21 is a diagram showing a result of verifying the prediction accuracy of the prediction unit according to Embodiment 2.
  • FIG. FIG. 22 is a diagram showing the result of verifying the correlation between the prediction accuracy of the prediction unit and the learning data set ratio according to the second embodiment.
  • 23 is a flowchart illustrating an operation example of the search system according to Embodiment 2.
  • FIG. 19 is a diagram depicting an example of data stored in a calculation result storage unit according to Embodiment 2
  • FIG. 20 is a diagram illustrating an example of a process of machine-learning a prediction model by a learning unit according to Embodiment 2.
  • FIG. 24 is a block diagram showing an overall configuration including a search system according to Embodiment 3.
  • FIG. 25 is a diagram depicting an example of data generated by a comparison unit according to Embodiment 3;
  • FIG. 26 is a flowchart illustrating an operation example of the search system according to Embodiment 3.
  • FIG. 27 is a block diagram showing an overall configuration including a search system according to Embodiment 4.
  • FIG. 28 is a flowchart showing an operation example of the search system according to Embodiment 4.
  • Non-Patent Document 1 discloses a structure optimization method based on first-principles calculation.
  • thermodynamically stable atomic arrangement structure In order to find a thermodynamically stable atomic arrangement structure in an unknown new substance, structural optimization is performed for the candidate atomic arrangement structure that the new substance can take.
  • a candidate atomic arrangement structure is obtained by partially substituting atoms included in the atomic arrangement structure of a known substance. Therefore, a plurality of candidate structures can be obtained depending on which atoms are substituted. Then, one or more structural optimizations are performed for each of the plurality of candidate structures, and the energy of the structurally optimized candidate structure, that is, the total energy is calculated. Then, the atomic arrangement structure corresponding to the lowest energy among the calculated energies, that is, the structure-optimized candidate structure is determined to be the thermodynamically most stable atomic arrangement structure in the new substance.
  • Non-Patent Document 2 describes a graph neural network model that converts atoms into nodes and bonds into edges in the atomic arrangement structure of the material composition, and predicts characteristic values such as energy from the atomic arrangement structure. Proposed. It has been shown that this method can construct a model that predicts material properties such as energy with high accuracy from the atomic arrangement structure included in the public database.
  • Non-Patent Document 1 is a prior art document that discloses the basic technology of structural optimization, and does not disclose learning of a prediction model by machine learning.
  • Non-Patent Document 2 merely discloses a technique for predicting material properties from an atomic arrangement structure, and does not disclose a search for a stable atomic arrangement structure.
  • the inventors of the present application focused on the fact that the relationship between atomic arrangement structure and energy can be associated with a graph neural network. Further, according to the studies of the inventors of the present application, it is possible to efficiently search for an atomic arrangement structure that is thermodynamically stable compared to the conventional one from the atomic arrangement structures that are candidates for the composition of a material that exists in a plurality of ways. I've found a technique that makes it possible. As a result, it has become clear that the calculation cost can be reduced, and a stable atomic configuration can be searched with high accuracy.
  • a search method is a search method for searching for a stable structure of atomic arrangement in a three-dimensional space for a composition of a material, wherein a computer performs the three A first step of obtaining a plurality of initial structures that are structures of atomic arrangements in a dimensional space, and performing structural optimization on some of the initial structures among the plurality of initial structures, and optimizing the structure of the atoms a second step of calculating a first energy corresponding to a configuration structure; and using a predictive model for the other initial structure of the plurality of initial structures to optimize the structure for the other initial structure.
  • a third step of predicting a second energy corresponding to the structure of the atomic arrangement when is performed and a fourth step of extracting a third energy indicating a local minimum based on the first energy and the second energy and a fifth step of outputting the third energy, a first structure that is a structure of an atomic arrangement corresponding to the third energy, or the third energy and the first structure, and the prediction model is machine-learned so that a structure with an arbitrary atomic arrangement is input, and the energy corresponding to the structure when structural optimization is performed on the structure is output as the second energy.
  • the partial initial structures in the second step are m (m is 1 ⁇ m ⁇ n), and the other initial structures in the third step may be (n ⁇ m) initial structures.
  • the third energy may be the minimum value of the first energy and the second energy.
  • a known structure which is a structure of atomic arrangement in a three-dimensional space of a known material similar to the composition of the material, may be obtained, and a plurality of the initial structures may be generated based on the known structure.
  • the known material contains at least one element dissimilar to the element contained in the composition of the material, and the first step comprises adding the dissimilar element to the element contained in the composition of the material.
  • a process of substituting elements may be included.
  • the first step may include extending the known structure in at least one dimension.
  • the prediction model may be a model machine-learned using a first learning data set that includes the initial structure as input data and the first energy corresponding to the initial structure as correct data.
  • the prediction model is a model machine-learned using a second learning data set that further includes the structure of the optimized atomic arrangement as input data and the first energy corresponding to the structure as correct data. There may be.
  • the number of the partial initial structures in the second step may be 90% or less of the number of the plurality of initial structures.
  • a search system is a search system for searching for a stable structure of atomic arrangement in a three-dimensional space for a composition of a material, wherein the atomic arrangement in the three-dimensional space that the composition of the material can take and a generation unit that generates a plurality of initial structures that are structures of and performs structural optimization on a part of the initial structures among the plurality of initial structures, and corresponds to the structure of the optimized atomic arrangement
  • a calculation unit that calculates the first energy and a prediction model for the other initial structure of the plurality of initial structures
  • the atom when the structure optimization is performed for the other initial structure A prediction unit that predicts a second energy corresponding to an arrangement structure, and an output unit that outputs the first energy and the second energy, and the prediction model receives an arbitrary atomic arrangement structure as input, Machine learning is performed so as to output, as the second energy, the energy corresponding to the structure when the structure is optimized for the structure.
  • the output unit is extracted based on the first energy and the second energy, a third energy indicating a local minimum, a first structure that is an atomic arrangement corresponding to the third energy, or the third energy and the first structure may be output.
  • a program is a program for searching for a stable structure of atomic arrangement in a three-dimensional space for a composition of a material, wherein the atomic arrangement structure in the three-dimensional space that the composition of the material can take
  • Machine learning is performed so that the structure is input and the energy corresponding to the structure when the structure is optimized is output as the second energy.
  • the computer further executes a fourth step of extracting a third energy indicating a minimum value based on the first energy and the second energy, and in the sixth step, the third energy, the third
  • the first structure which is the structure of the atomic arrangement corresponding to the energy, or the third energy and the first structure may be further output.
  • a predictive model construction method includes a first step in which a computer acquires an initial structure that is an atomic arrangement structure in a three-dimensional space that a material composition can take, and the initial structure as input data, the Using a learning data set that contains the energy corresponding to the structure of the atomic arrangement obtained by performing structural optimization on the initial structure as correct data, and a seventh step of performing machine learning so as to output energy corresponding to the structure when the structure is optimized.
  • a predictive model construction device includes a generation unit that generates an initial structure that is a structure of atomic arrangements in a three-dimensional space that a material composition can take, and the initial structure as input data, and for the initial structure Using a learning data set containing the energy corresponding to the structure of the atomic arrangement obtained by performing structural optimization as correct data, the structure is optimized for the input of the structure with an arbitrary atomic arrangement. and a learning unit that performs machine learning so as to output energy corresponding to the structure in the case of .
  • a search method is a search for searching for a stable structure of atomic arrangement in the three-dimensional space for the composition of the material using a prediction model machine-learned by the prediction model construction device.
  • a method wherein a computer performs geometry optimization on the initial structures by first obtaining a plurality of the initial structures and using the predictive model for each of the plurality of initial structures.
  • the eighth step of predicting the energy corresponding to the structure of the atomic arrangement in the case of and the ninth step of extracting the energy indicating the minimum value from the plurality of predicted energies are performed.
  • a search method is a search for searching for a stable structure of atomic arrangement in the three-dimensional space for the composition of the material using a prediction model machine-learned by the prediction model construction device.
  • a method wherein a computer performs a first step of obtaining a plurality of said initial structures, performing structure optimization on some initial structures of said plurality of initial structures, and obtaining structure-optimized atoms a second step of calculating a first energy corresponding to a configuration structure; and using the prediction model for at least one initial structure among the partial initial structures to perform structural optimization for the initial structure a tenth step of predicting a second energy corresponding to the structure of the atomic arrangement when the transformation is performed; and a third step of verifying the prediction accuracy of the prediction model by comparing the first energy and the second energy.
  • the computer uses the predictive model for the other initial structure among the plurality of initial structures, thereby for the other initial structure.
  • a twelfth step of predicting the second energy corresponding to the structure of the atomic arrangement when the structure optimization is performed, and extracting the third energy indicating the minimum value based on the first energy and the second energy. and a thirteenth step may be further performed.
  • Such a computer program can also be realized as a computer program that causes a computer to execute the characteristic processing included in the search method or predictive model construction method of the present disclosure.
  • a computer program can be distributed via a computer-readable non-temporary recording medium such as a CD-ROM or a communication network such as the Internet.
  • the search system may be configured such that all components are included in one computer, or may be configured as a system in which multiple components are distributed to multiple computers. .
  • Search system 100 (search method or program) according to Embodiment 1 of the present disclosure will be described in detail using the drawings.
  • Search system 100 (search method or program) according to Embodiment 1 is a system (method or program) for searching for a stable structure of atomic arrangement in three-dimensional space for the composition of a material.
  • stable structure refers to a structure in which the force acting on each atom contained in the structure of the atomic arrangement (that is, the crystal structure) is below the threshold, and the energy corresponding to the structure (total energy) is the minimum.
  • the threshold can be appropriately set by the user, but may be a value close to zero. This is because the closer the force acting on each atom to zero, the more thermodynamically stable the structure.
  • the search system 100 searches for a stable structure as described above and outputs it to the user. It may include a mode of outputting data necessary for performing. In other words, the search system 100 (search method or program) does not have to complete the process of searching for stable structures.
  • FIG. 1 is a block diagram showing the overall configuration including a search system 100 according to Embodiment 1.
  • the search system 100 is configured as a computer such as a personal computer or a server, for example.
  • the search system 100 includes an acquisition unit 102, a generation unit 103, a calculation unit 104, a learning unit 105, a prediction unit 106, a comparison unit 107, and an output unit 108.
  • Peripheral configurations of the search system 100 include an input unit 101 , a material database (DB) 109 , a structure storage unit 110 , a calculation result storage unit 111 , and a prediction model storage unit 112 .
  • the peripheral configuration of the search system 100 may be included in the components of the search system 100 .
  • the generation unit 103 and the learning unit 105 in the search system 100 are also components of a prediction model construction device.
  • the input unit 101 is an input interface that receives a user's input, acquires information about the composition of the material to be searched by user's input, and outputs the information to the acquisition unit 102 .
  • the information about the composition is, for example, composition formula information expressed in character string format.
  • the compositional formula information can be expressed as “Li 12 Mn 6 Ni 6 O 24 ” as an example. That is, in this case, the composition of the material to be searched is composed of 12 Li (lithium) atoms, 6 Mn (manganese) atoms, 6 Ni (nickel) atoms, and 24 O (oxygen) atoms. indicates that
  • the input unit 101 includes, for example, a keyboard, touch sensor, touch pad, mouse, or the like.
  • the acquisition unit 102 acquires composition formula information from the input unit 101 and acquires from the material database 109 the structure of the atomic arrangement of a known material similar in composition to the material to be searched included in the composition formula information.
  • the term “similar” as used herein means, for example, that the elements included in the composition of the material to be searched and the composition of the known material are only partially different.
  • Similar means that the composition of the known material contains at least one element contained in the composition of the material being searched for.
  • Similar means that the composition of the material to be searched can be constructed by expanding and substituting the structure of the atomic arrangement of the known material.
  • the material database 109 stores in advance known material data including the composition and structure of each of one or more materials.
  • the material database 109 is composed of a recording medium such as a hard disk drive or a non-volatile semiconductor memory.
  • the material database 109 may be a public database such as MaterialsProject.
  • the known material data includes, for example, information described in the Crystallographic Information Common Data Format (Crystallographic Information File: CIF).
  • CIF Crystallographic Information Common Data Format
  • the description format of the information is not limited to the CIF data format, and any description format that enables structural optimization calculations by first-principles calculations such as the composition formula, crystal structure, and lattice vector can be used. can be written in any format.
  • FIG. 2 is a diagram showing an example of data stored in the material database 109 according to Embodiment 1.
  • the material database 109 of the present disclosure stores data described in a format called CIF.
  • the CIF describes the composition formula indicating the composition of the material, the length of the unit cell vector, the angle at which atoms intersect, the arrangement of atoms in the unit cell, and the like.
  • the atomic arrangement information regarding the material "Li 4 C 2 O 6 " and the material "Li 6 Ni 6 O 12 " is shown.
  • the left column represents the composition formula indicating the composition of the material
  • the right column represents the atomic arrangement of the material composition.
  • the acquisition unit 102 outputs the composition formula information acquired from the input unit 101 and the known material data acquired from the material database 109 to the generation unit 103 .
  • the generation unit 103 performs expansion processing and replacement processing on the atomic arrangement structure (known structure) of the known material acquired from the acquisition unit 102 .
  • the generation unit 103 generates a plurality of initial structures representing the composition of the search target material included in the composition formula information also acquired from the acquisition unit 102 . That is, the generation unit 103 (at the first step) acquires a plurality of initial structures, which are structures of atomic arrangements in a three-dimensional space that can be taken by the composition of the material.
  • the "initial structure" as used herein is an arbitrary structure having the same composition as the composition of the material to be searched for and the atomic arrangement of the composition of the material to be searched for.
  • the generation unit 103 acquires a known structure, which is a structure of atomic arrangements in a three-dimensional space of a known material similar to the composition of the material, and based on the known structure, a plurality of initial structures to generate
  • FIGS. 3 to 5 are diagrams each showing an example of the process of generating an initial structure by the generation unit 103 according to Embodiment 1.
  • FIG. In each of FIGS. 3 to 5, (a) represents the CIF of the initial structure, and (b) represents the unit cell of the crystal structure indicated by the CIF of the initial structure, that is, the atomic arrangement.
  • the composition formula of the known material is Li 6 Mn 6 O 12
  • the composition formula of the material to be searched is Li 12 Mn 6 Ni 6 O 24 .
  • the smallest spheres are O atoms
  • the unhatched spheres are Li atoms
  • the hatched spheres are similar in size to Li atoms.
  • Ni atoms, and black-filled spheres represent Mn atoms.
  • FIG. 3 represents the CIF and atomic arrangement for known structures.
  • the generation unit 103 first generates an arrangement structure of Li 12 Ni 12 O 24 by extending a known structure.
  • the term "expansion” as used herein refers to copying a structure to be expanded (here, a known structure) so as to be repeated in at least one of the three-dimensional directions (x-direction, y-direction, and z-direction). That is, the generator 103 (the first step) includes a process of expanding the known structure in at least one dimension.
  • Figure 4 shows the CIF and atomic arrangement of the expanded structure of the known structure.
  • the structure shown in FIG. 4(b) is obtained by repeating the structure shown in FIG. 3(b) twice.
  • the known structure is expanded so that the number of atoms is the same as the number of atoms in the composition of the material being searched for.
  • the number of atoms is the same as the number of atoms in the composition of the material being searched for.
  • there are a total of 6 "Li0" to "Li5" in the known structure but a total of 12 "Li0" to "Li11” in the expanded structure. , which is the same as the number of Li atoms in the composition of the material to be searched.
  • the generation unit 103 replaces 6 Ni atoms out of 12 Ni atoms in the expanded structure (Li 12 Ni 12 O 24 ) with Mn atoms. That is, the composition of the known material contains at least one element different from the element contained in the composition of the material to be searched. Then, the generation unit 103 (acquisition step) includes a process of substituting elements of the same type as the elements contained in the composition of the material to be searched for the different elements.
  • FIG. 5 shows the CIF and atomic arrangement of the structure after substitution.
  • 6 “Ni18” to “Ni23” are replaced with 6 “Mn18” to “Mn23”.
  • each element contained in the structure after substitution becomes the same as each element contained in the composition of the material to be searched. It's becoming
  • FIG. 6 is a diagram showing an example of a plurality of initial structures generated by the generation unit 103 according to the first embodiment.
  • 7A is a diagram showing an example of the configuration of the initial structure generated by the generation unit 103 according to Embodiment 1
  • FIG. 7B is a diagram showing the configuration of the initial structure generated by the generation unit 103 according to Embodiment 1.
  • FIG. It is a figure which shows another example of.
  • FIG. 6(a) shows the structure when "Ni18” to “Ni23” are replaced with “Mn18” to “Mn23”.
  • FIG. 6(b) shows the structure when “Ni12” to “Ni17” are replaced with “Mn12” to “Mn17”.
  • (c) of FIG. 6 "Ni13”, “Ni15”, “Ni17”, “Ni19”, “Ni21” and “Ni23” are changed to “Mn13”, “Mn15”, “Mn17”, “Mn19” and “Mn21”. ”, and “Mn23”.
  • the generation unit 103 selects 6 of the 12 Ni atoms that can be taken by Li 12 Mn 6 Ni 6 O 24 and replaces them with Mn atoms. Generate initial structures.
  • the generation unit 103 outputs the multiple generated initial structures to the structure storage unit 110 .
  • all the generated initial structures may be output to the structure storage unit 110, or equivalent structures from the viewpoint of symmetry are screened using an existing program or the like, Only the filtered initial structure may be output.
  • the structure storage unit 110 stores a plurality of initial structures generated by the generation unit 103.
  • the data of each initial structure is stored in a description format in which structure optimization calculations such as a composition formula, crystal structure, and lattice vector can be performed by first-principles calculation or the like, similarly to the material database 109 .
  • FIG. 8 is a diagram showing an example of data stored in the structure storage unit 110 according to the first embodiment.
  • the left column represents the initial structure ID (Identifier) assigned to distinguish each initial structure
  • the right column represents the atomic arrangement of the initial structure.
  • the calculation unit 104 acquires part of the initial structure from the structure storage unit 110, and performs structural optimization on the acquired initial structure.
  • the calculation unit 104 executes a process of calculating energy (first energy) corresponding to the final structure obtained by repeating the structure optimization.
  • FIG. 9 is a diagram showing an example of a process of calculating the first energy by calculation section 104 according to the first embodiment.
  • the calculation unit 104 (in the second step) performs structure optimization on some initial structures among the plurality of initial structures, and calculates the first energy corresponding to the structure of the optimized atomic arrangement.
  • the "first energy” here may indicate the energy corresponding to the final structure obtained by repeating the structure optimization, or may indicate the energy corresponding to the intermediate structure that has not yet reached the final structure. be.
  • the calculation unit 104 uses a first-principles calculation package such as VASP (Vienna Abinitio Simulation Package), for example, to perform a process of calculating the first energy corresponding to structural optimization and the final structure.
  • VASP Vehicle Abinitio Simulation Package
  • the "final structure” is a structure obtained by performing structural optimization on the initial structure, and is a structure in which the force acting on each atom contained in the structure is equal to or less than the threshold.
  • the “intermediate structure” is a structure obtained by performing structure optimization on the initial structure, and the force acting on at least one or more atoms contained in the structure exceeds the threshold value. It is a structure that has not reached the final structure.
  • the calculation unit 104 calculates the force F acting on each atom included in the structure to be processed, and the structure (that is, the final structure) in which the magnitude of the force F calculated for each atom is equal to or less than the threshold value. to explore.
  • the threshold may be a value close to zero. Specifically, when the magnitude of the force F acting on at least one atom exceeds a threshold in the structure obtained by performing the structure optimization, the calculation unit 104 determines that the force F is applied. Each atom is moved in a direction and the position of each atom is adjusted so that the force F becomes small.
  • the calculation unit 104 repeats the above-described process of calculating the force F of each atom and the process of adjusting the position of each atom as one cycle of structural optimization.
  • a structure that is, the final structure
  • the structure optimization is terminated.
  • the calculation unit 104 calculates the energy corresponding to the obtained final structure, that is, the final energy.
  • the calculation unit 104 outputs the initial structure, the final structure obtained by repeatedly performing structural optimization on the initial structure, and the final energy corresponding to the calculated final structure to the calculation result storage unit 111 .
  • the calculation result storage unit 111 stores a set of the final energy calculated by the calculation unit 104 and the corresponding initial structure.
  • FIG. 10 is a diagram showing an example of data stored in the calculation result storage unit 111 according to Embodiment 1. As shown in FIG. In FIG. 10, the left column indicates the initial structure ID, the middle column indicates the atomic arrangement of the initial structure, and the right column indicates the final energy corresponding to the final structure obtained by optimizing the initial structure. represents. Thus, the calculation result storage unit 111 may store at least a set of the initial structure and the final energy of the final structure. In Embodiment 1, the calculation result storage unit 111 further stores the atomic arrangement of the final structure.
  • the learning unit 105 acquires the initial structure and the final energy of the final structure from the calculation result storage unit 111, and makes the prediction model learn using the acquired initial structure and final energy.
  • the set of input and output learned by the prediction model is, for example, the input as the initial structure and the output as the final energy.
  • the learning unit 105 uses the learning data set to generate a structure obtained by optimizing the structure (here, the initial structure) with respect to the input structure of an arbitrary atomic arrangement.
  • Machine learning is performed on the prediction model so as to output the energy corresponding to (here, the final structure).
  • the learning data set includes the initial structure as input data and the energy corresponding to the atomic arrangement structure (here, the final structure) obtained by performing structural optimization on the initial structure as correct data.
  • the prediction model is composed of a graph neural network with graph structure as input.
  • the graph neural network is, for example, CGCNN (Crystal Graph Convolutional Neural Network) or MEGNet (Material Graph Network).
  • the prediction model is constructed by MEGNet.
  • MEGNet is a graph neural network that uses not only nodes (nodes/vertices) and edges (branches/sides) as feature quantities, but also global state quantities that represent the features of the entire target system as feature quantities.
  • FIG. 11 is a diagram showing an example of a process of machine learning a prediction model by the learning unit 105 according to the first embodiment.
  • the learning unit 105 first converts the atomic coordinates and type of each atom in the initial structure as shown in FIG. 11(a) into a graph structure as shown in FIG. 11(b). In the graph structure, a node corresponds to each atom of the initial structure and an edge corresponds to a bond between each atom of the initial structure.
  • the learning unit 105 inputs the converted graph structure to a graph neural network as shown in FIG. 11(c).
  • the learning unit 105 compares the predicted value of the final energy shown in (d) of FIG. 11 output from the graph neural network and the final energy as correct data.
  • the learning unit 105 updates the weight of the graph neural network. In this way, the learning unit 105 machine-learns the prediction model by supervised learning using a plurality of learning data sets.
  • the learning unit 105 outputs the prediction model for which machine learning has been completed, that is, the learned model to the prediction unit 106 and the prediction model storage unit 112 .
  • the prediction model for which this machine learning has been completed takes as input a structure with an arbitrary atomic arrangement (here, the initial structure), and the structure (here, the final structure) when the structure is optimized for the structure. Machine learning is performed so that the corresponding energy is output as second energy, which will be described later.
  • This prediction model is machine-learned using a first learning data set that includes an initial structure as input data and a first energy (here, final energy) corresponding to the initial structure as correct data.
  • the prediction model storage unit 112 stores the graph neural network structure and weights of the prediction model machine-learned by the learning unit 105 .
  • the prediction unit 106 acquires an initial structure whose final energy has not yet been calculated from the structure storage unit 110 . Then, the prediction unit 106 predicts the final energy of the initial structure by inputting the initial structure into the prediction model acquired from the learning unit 105, that is, the learned prediction model.
  • the “initial structure whose final energy has not been calculated” means a structure other than a part of the initial structure whose energy has been calculated by the calculation unit 104 among the plurality of initial structures, and other initial structures.
  • the prediction unit 106 uses the prediction model for other initial structures among the plurality of initial structures, so as to obtain Predict the second energy corresponding to the structure of the atomic arrangement.
  • the second energy is the predicted value of the final energy corresponding to the final structure when geometry optimization is performed on other initial structures.
  • FIG. 12 is a diagram showing an example of the process of predicting the second energy by the prediction unit 106 according to Embodiment 1.
  • the prediction unit 106 converts the initial structure into a graph structure and inputs the converted initial structure to the prediction model.
  • illustration of the process of converting the initial structure to the graph structure is omitted.
  • the predictive model outputs a predicted value of the final energy corresponding to the final structure when structural optimization is performed on the input initial structure, that is, the second energy.
  • the prediction model as disclosed in Non-Patent Document 2 outputs a prediction value of the energy corresponding to the input initial structure
  • the prediction model according to Embodiment 1 outputs the input initial structure
  • the predicted value of the energy output by the prediction model corresponds to the energy corresponding to the structure obtained by the calculation unit 104 actually performing structural optimization on the initial structure. do.
  • Embodiment 1 by using a prediction model, a structure optimized for a structure (for example, an intermediate structure or It is possible to obtain the energy corresponding to the final structure). Therefore, in Embodiment 1, calculations for structural optimization can be omitted to some extent, so that calculation costs can be reduced.
  • the prediction unit 106 outputs the initial structure and the final energy prediction value corresponding to the initial structure to the comparison unit 107 .
  • the comparison unit 107 obtains a set of predicted values of initial structure and final energy from the prediction unit 106 .
  • the comparison unit 107 acquires a set of final structure and final energy from the calculation result storage unit 111 . Then, the comparison unit 107 generates a list in which the pair of the initial structure and the predicted value of the final energy and the pair of the final structure and the final energy are arranged.
  • FIG. 13 is a diagram showing an example of data generated by the comparison unit 107 according to Embodiment 1.
  • the left column represents the atomic arrangement of the initial structure or the final structure
  • the middle column represents the final energy corresponding to the final structure
  • the right column represents the predicted value of the final energy corresponding to the initial structure.
  • the comparison unit 107 rearranges the final energies and the predicted values of the final energies in a predetermined order based on the list.
  • the comparison unit 107 rearranges the final energy and the predicted final energy in order from the lowest energy value.
  • Such rearrangement of the final energies and the predicted values of the final energies corresponds to the process of extracting the smallest value, in other words, the minimum value or the minimum value, from the final energies and the predicted values of the final energies.
  • the comparison unit 107 extracts the third energy indicating the minimum value based on the first energy and the second energy.
  • the first energy is the final energy obtained from the calculation result storage unit 111
  • the second energy is the predicted value of the final energy obtained from the prediction unit 106 .
  • the minimum value is the minimum value of the first energy and the second energy.
  • the third energy is the minimum value of the first energy and the second energy.
  • the comparison unit 107 outputs to the output unit 108 a list in which the final energy and the predicted value of the final energy are rearranged as described above.
  • the output unit 108 displays the predicted values of the initial structures and final energies, and the final structures and final energies included in the list output by the comparison unit 107, according to the above-described predetermined order, that is, in order from the structure with the lowest energy to the display. indicate. That is, the output unit 108 (in the fifth step) outputs the third energy, the first structure, which is the structure of the atomic arrangement corresponding to the third energy, or the third energy and the first structure.
  • the output unit 108 may display only the third energy and the atomic arrangement structure corresponding to the third energy on the display.
  • the output unit 108 may display the list before the final energy and the predicted value of the final energy are rearranged by the comparison unit 107 on the display. That is, the output unit 108 (in the sixth step) may output the first energy and the second energy. In this case, the above extraction processing (fourth step) by the comparison unit 107 is unnecessary.
  • the predicting unit 106 has a stable structure for each of substances having 21 types of composition composed of 48 atoms including Li atoms and Mn atoms and at least one element selected from Ni atoms and O atoms. The purpose is to confirm whether it is possible to predict
  • a total of 1086 sets of initial structures and final energies were prepared for the substances with the above 21 compositions. That is, for each of a total of 1086 initial structures, structure optimization was performed to obtain the final structure, and the final energy corresponding to the obtained final structure was calculated. Of the total 1086 pairs, 328 pairs, or 30% of the total, were used as verification data (test data), and the remaining 70%, or 758 pairs, were used as learning data (train data).
  • machine learning of the prediction model was performed by using the initial structure as the input data and the final energy as the correct data as the learning data set.
  • the machine-learned prediction model was then used to predict the final energy of the verification data. That is, by inputting the initial structure contained in the verification data into a machine-learned prediction model, a predicted value of the final energy corresponding to the initial structure output from the prediction model was obtained.
  • the structure with the most stable atomic arrangement among the multiple final structures obtained by actually performing structural optimization on each of the multiple initial structures is the prediction model. Then, we considered what the most stable structure is predicted to be. Thereby, it is possible to evaluate whether or not screening using the prediction model is possible.
  • FIG. 14 is a diagram showing an evaluation example of the prediction accuracy of the prediction unit 106 according to the first embodiment.
  • the “correct final energy value” referred to here is the final energy corresponding to the final structure obtained by actually performing structural optimization on the initial structure.
  • the “predicted value of final energy” referred to here is the predicted value of final energy output from the prediction model by inputting the initial structure into the prediction model.
  • the “ranking” referred to here is the ranking when the final structure with the smallest correct value of final energy or the smallest predicted value of final energy is ranked first.
  • the structure with the most stable atomic arrangement obtained by actually performing structural optimization is predicted by the prediction unit 106 to be the structure with the second most stable atomic arrangement. become.
  • FIG. 15 is a diagram showing the results of verifying the prediction accuracy of the prediction unit 106 according to Embodiment 1.
  • FIG. 15 the composition formula of the substance, the number of learning data for the substance, the number of verification data for the substance, and the ranking are shown in order from the leftmost column.
  • the “rank” here indicates the order of the most stable atomic arrangement structure predicted by the prediction unit 106 among the verification data for the substance.
  • the prediction accuracy of the prediction unit 106 may decrease.
  • the structure of the atomic arrangement that is actually considered to be the most stable among the 52 sets of verification data is predicted by the prediction unit 106 to be the third most stable structure.
  • rice field For example, for Li 15 Mn 5 Ni 4 O 24 as well, the prediction unit 106 predicted that the structure with the most stable atomic arrangement among the 78 sets of verification data was the tenth most stable structure.
  • the prediction unit 106 determines the structure of the atomic arrangement that is actually considered to be the most stable for a substance having any composition within 20% of the entire verification data for the substance. It can be seen that the order-stable structure can be predicted. That is, even if the number of verification data increases, the prediction accuracy of the prediction unit 106 hardly deteriorates.
  • the prediction unit 106 ranks the structure of the atomic arrangement that is actually thought to be the most stable for a substance having any composition within 17%, or even within 13% of the entire verification data for the substance. can be predicted to be a stable structure at
  • FIG. 16 is a diagram showing the result of verifying the correlation between the prediction accuracy of prediction section 106 and the ratio of learning data according to Embodiment 1.
  • FIG. 16 is a diagram showing the result of verifying the prediction accuracy of the prediction unit 106 while changing the learning data ratio for a substance having the composition Li 14 Mn 5 Ni 5 O 24 .
  • the "ratio of learning data” as used herein is the ratio of the number of learning data to the total number of learning data and verification data for a substance having the composition Li14Mn5Ni5O24 , expressed as a percentage. is represented.
  • the ratio of learning data for the substance the number of learning data for the substance, the number of verification data for the substance, the ranking, and the number of all substances It represents the total number of training data.
  • the “rank” here indicates the order of the most stable atomic arrangement structure predicted by the prediction unit 106 among the verification data for the substance.
  • FIG. 17 is a flowchart showing an operation example of the search system 100 according to Embodiment 1.
  • FIG. 17 is a flowchart showing an operation example of the search system 100 according to Embodiment 1.
  • Step S101 The input unit 101 acquires composition formula information through user input, and outputs the acquired composition formula information to the acquisition unit 102 .
  • Step S102 The acquisition unit 102 acquires the structure of the atomic arrangement of known materials similar to the composition of the material to be searched included in the composition formula information from the material database 109 and outputs the acquired similar known structures to the generation unit 103 .
  • Step S103 The generation unit 103 executes expansion processing and replacement processing on the structure of the atomic arrangement of the known structure acquired in step S102. As a result, the generation unit 103 generates a plurality of initial structures representing the composition of the material to be searched, which is included in the composition formula information, and outputs them to the structure storage unit 110 .
  • Step S104 The calculation unit 104 performs structure optimization on some of the initial structures generated in step S103, and calculates the final energy corresponding to the final structure obtained by performing the structure optimization. Calculate Calculation unit 104 then outputs the calculation result to calculation result storage unit 111 .
  • n is an integer equal to or greater than 2
  • m is an initial structure of 1 ⁇ m ⁇ n integer.
  • “m” is a number that is 90% or less of "n”.
  • “m” may be a number that is 1% or more and 90% or less of “n”. That is, the number of partial initial structures in the calculation unit 104 (second step) is 90% or less of the number of multiple initial structures.
  • Step S105 The learning unit 105 performs machine learning of a prediction model configured by a graph neural network using the combination of the final energy and initial structure calculated in step S104 as a learning data set.
  • the learning unit 105 then outputs the machine-learned prediction model to the prediction unit 106 and the prediction model storage unit 112 .
  • the number of training datasets is the same as the number of some initial structures, which is m.
  • Step S106 The prediction unit 106 acquires from the structure storage unit 110 an initial structure for which the final energy has not been calculated, that is, another initial structure among the plurality of initial structures. Then, the prediction unit 106 calculates predicted values of final energies corresponding to other initial structures using the prediction model machine-learned in step S105.
  • the number of other initial structures is the number obtained by excluding some initial structures from the plurality of initial structures. That is, the other initial structures in the prediction unit 106 (third step) are (nm) initial structures.
  • the prediction model is the prediction model machine-learned in step S105.
  • Step S107 The comparison unit 107 generates a list in which the final energy calculated in step S105 and the predicted value of the final energy calculated in step S106 are rearranged in descending order of energy, and outputs the generated list. Output to unit 108 . In other words, the comparison unit 107 extracts the energy indicating the minimum value from the final energy and the predicted value of the final energy.
  • Step S108 The output unit 108 outputs the predicted values of the initial structures and final energies and the final structures and final energies included in the list generated in step S107 by displaying them on the display in order from the structure with the lowest energy.
  • Embodiment 1 instead of performing structural optimization on all initial structures, structural optimization is performed only on some initial structures, and the rest of the initial structures are optimized. On the other hand, by using a predictive model, calculations for structural optimization are omitted. Therefore, in Embodiment 1, it is possible to search for a structure with the most thermodynamically stable atomic arrangement in the new substance, as in the case where structural optimization is performed for all initial structures. Moreover, it is possible to omit the computation required for the search to some extent. In other words, in Embodiment 1, compared to the case where structure optimization is performed for all initial structures, the calculation cost can be reduced, and the stable structure of the atomic arrangement for the composition of the material can be efficiently obtained. can be explored.
  • the search system 200 (search method or program) according to Embodiment 2 of the present disclosure will be described in detail below with reference to the drawings.
  • the search system 200 according to Embodiment 2 differs from the search system 100 according to Embodiment 1 in that it uses not only an initial structure but also an intermediate structure and a final structure when performing machine learning on a prediction model.
  • the same reference numerals are given to the same components as in the first embodiment, and the description thereof is omitted.
  • FIG. 18 is a block diagram showing the overall configuration including the search system 200 according to the second embodiment.
  • the search system 200 includes an acquisition unit 102, a generation unit 103, a calculation unit 204, a learning unit 205, a prediction unit 106, a comparison unit 107, and an output unit 108.
  • Peripheral configurations of the search system 200 include an input unit 101 , a material database (DB) 109 , a structure storage unit 110 , a calculation result storage unit 211 , and a prediction model storage unit 212 .
  • the peripheral configuration of the search system 200 may be included in the components of the search system 200 .
  • the generation unit 103 and the learning unit 205 in the search system 200 are also components of a prediction model construction device.
  • the calculation unit 204 acquires a part of the initial structure from the structure storage unit 110 and performs structural optimization on the acquired initial structure.
  • the calculation unit 104 executes a process of calculating energy (first energy) corresponding to the final structure obtained by repeating the structure optimization.
  • the calculation unit 204 outputs the initial structure, the final structure obtained by repeatedly executing structure optimization on the initial structure, and the final energy corresponding to the calculated final structure to the calculation result storage unit 211 .
  • the calculation unit 204 also outputs an intermediate structure obtained each time structure optimization is performed on the initial structure to the calculation result storage unit 211 .
  • the calculation result storage unit 211 stores a set of the final energy calculated by the calculation unit 204, the corresponding initial structure, the corresponding intermediate structure, and the corresponding final structure.
  • FIG. 19 is a diagram showing an example of data stored in the calculation result storage unit 211 according to the second embodiment.
  • the left column shows the initial structure ID
  • the middle column shows the atomic arrangement of the intermediate structure and the atomic arrangement of the final structure obtained each time the structure is implemented
  • the right column shows the final energy corresponding to the final structure. represents.
  • illustration of the atomic arrangement of the initial structure is omitted.
  • the learning unit 205 acquires the initial structure, the intermediate structure, the final structure, and the final energy of the final structure from the calculation result storage unit 211, and uses these to learn the prediction model.
  • FIG. 20 is a diagram showing an example of the process of machine learning a prediction model by the learning unit 205 according to the second embodiment.
  • the input data included in the learning data set includes not only the initial structure but also the intermediate structure and final structure obtained each time structure optimization is performed. .
  • the learning unit 205 not only includes the first learning data set including the initial structure as input data and the final energy as correct data, but also the intermediate structure or final structure as input data and the final energy as correct data.
  • the prediction model is machine-learned further using a second training data set including as .
  • the prediction model further includes, in addition to the first learning data set, the structure of the structure-optimized atomic arrangement, that is, the intermediate structure or the final structure as input data, corresponding to the structure
  • This model is machine-learned using a second learning data set that includes the first energy, that is, the final energy as correct data. Note that the details of the prediction model machine learning process by the learning unit 205 are the same as those in the first embodiment, and thus description thereof is omitted.
  • the learning unit 205 outputs the prediction model for which machine learning has been completed, that is, the learned model to the prediction unit 106 and the prediction model storage unit 212 .
  • the prediction model storage unit 212 stores the graph neural network structure and weights of the prediction model machine-learned by the learning unit 205 .
  • Embodiment 2 Verification of prediction accuracy
  • Verification of the prediction accuracy of the prediction unit 106 according to the second embodiment will be described below.
  • 21 types of compositions composed of 48 atoms containing Li atoms and Mn atoms and at least one element selected from Ni atoms and O atoms were selected.
  • the object is to confirm whether or not the prediction unit 106 can predict a stable structure for each of the substances possessed.
  • Verification in Embodiment 2 is that the learning data set used for machine learning of the prediction model further includes not only the above-described first learning data set but also the above-described second learning data set, This is different from the verification in the first embodiment.
  • FIG. 21 is a diagram showing the result of verifying the prediction accuracy of the prediction unit 106 according to the second embodiment.
  • what each column represents is the same as in FIG. 15 of Embodiment 1, so the description is omitted here.
  • the prediction accuracy of the prediction unit 106 may decrease.
  • the structure of the atomic arrangement that is actually considered to be the most stable among the 52 sets of verification data is predicted by the prediction unit 106 to be the fifth most stable structure.
  • rice field For example, for Li 15 Mn 5 Ni 4 O 24 as well, the prediction unit 106 predicted that the structure with the most stable atomic arrangement among the 78 sets of verification data was the tenth most stable structure.
  • the prediction unit 106 determines the structure of the atomic arrangement that is actually considered to be the most stable for a substance having any composition within 20% of the entire verification data for the substance. It can be seen that the order-stable structure can be predicted. That is, even if the number of verification data increases, the prediction accuracy of the prediction unit 106 hardly deteriorates.
  • the prediction unit 106 determines the structure of the atomic arrangement that is actually considered to be the most stable within 17% of the entire verification data for the substance, and further within 13%. It may be possible to predict a stable structure.
  • FIG. 22 is a diagram showing the result of verifying the correlation between the prediction accuracy of prediction section 106 and the ratio of learning data according to the second embodiment. Specifically, FIG. 22 is a diagram showing the result of verifying the prediction accuracy of the prediction unit 106 while changing the learning data ratio for a substance having the composition Li 14 Mn 5 Ni 5 O 24 . In FIG. 22, what each column represents is the same as in FIG. 16 of Embodiment 1, so the description is omitted here.
  • the structure of the atomic arrangement that is actually considered to be the most stable among the 147 sets of verification data is ranked third by the prediction unit 106. Predicted to be a stable structure.
  • the structure of the atomic arrangement that is actually considered to be the most stable was predicted by the prediction unit 106 to be the twelfth most stable. .
  • the prediction model is machine-learned by further using a learning data set containing the structure of the structurally optimized atomic arrangement, that is, the intermediate structure or the final structure as input data. Even if the ratio is low, it is considered possible to make highly accurate predictions.
  • FIG. 23 is a flow chart showing an operation example of the search system 200 according to the second embodiment. Since the processes of steps S201 to S204 and steps S206 to S208 are the same as the processes of steps S101 to S104 and steps S106 to S108 shown in FIG. 17 respectively, description thereof is omitted. That is, the overall flow of the processing of the search system 100 according to Embodiment 1 is the same as that of step S205.
  • Step S205 The learning unit 205 uses the set of the final energy and the initial structure calculated in step S204 and the set of the final energy and the optimized structure as learning data sets to create a prediction model configured by a graph neural network. machine learning.
  • a "structure optimized structure" as used herein is an intermediate structure or a final structure.
  • the learning unit 205 then outputs the machine-learned prediction model to the prediction unit 106 and the prediction model storage unit 212 .
  • the prediction model is machine-learned by further using a learning data set containing as input data the structure of the structurally optimized atomic arrangement, that is, the intermediate structure or the final structure. Therefore, in the second embodiment, compared with the first embodiment, it is easier to more accurately predict the energy corresponding to the structure when the structure optimization is performed on the input initial structure.
  • Embodiment 3 A search system 300 (search method or program) according to Embodiment 3 of the present disclosure will be described in detail below with reference to the drawings.
  • the search system 300 according to Embodiment 3 when predicting the second energy corresponding to the structure of the atomic arrangement when structural optimization is performed on the initial structure, the prediction of a known structure that has been machine-learned in advance is performed. It differs from the search system 100 according to the first embodiment or the search system 200 according to the second embodiment in that a model is used.
  • the same reference numerals are assigned to the same constituent elements as those in the first or second embodiment, and the description thereof is omitted.
  • FIG. 24 is a block diagram showing the overall configuration including search system 300 according to the third embodiment.
  • the search system 300 includes an acquisition unit 102, a generation unit 103, a prediction unit 306, a comparison unit 307, and an output unit . not prepared.
  • Peripheral configurations of the search system 300 include an input unit 101 , a material database (DB) 109 , a structure storage unit 110 , and a prediction model storage unit 312 .
  • the peripheral configuration of the search system 300 may be included in the components of the search system 300 .
  • the prediction model storage unit 312 stores the structure and weights of the graph neural network for a prediction model that has undergone machine learning in advance.
  • the prediction model employed here is, for example, a prediction model relating to the known structure of a known material similar to the composition of the material to be searched, or a general-purpose learned prediction model.
  • the prediction model is the former prediction model, that is, the prediction model for known structures.
  • This prediction model uses, for example, a known structure as input data and a learning data set that includes, as correct data, the final energy corresponding to the final structure obtained by performing structural optimization on the known structure. machine-learned.
  • the prediction unit 306 acquires the initial structure from the structure storage unit 110. FIG. Then, the prediction unit 306 inputs the initial structure into the trained prediction model acquired from the prediction model storage unit 312, thereby predicting the final energy of the initial structure. In Embodiment 3, the prediction unit 306 predicts the final energy for each of all initial structures using a prediction model. That is, the prediction unit 306 (in the eighth step) uses a prediction model for each of a plurality of initial structures to correspond to the structure of the atomic arrangement when the structure optimization is performed for the initial structure. Predict energy. The "energy” here is the predicted value of the final energy corresponding to the final structure when geometry optimization is performed on the initial structure.
  • the prediction unit 306 outputs the initial structure and the final energy prediction value corresponding to the initial structure to the comparison unit 307 .
  • the comparison unit 307 obtains a set of predicted values of initial structure and final energy from the prediction unit 306 . Then, the comparison unit 307 generates a list in which sets of predicted values of initial structures and final energies are arranged.
  • FIG. 25 is a diagram showing an example of data generated by the comparison unit 307 according to the third embodiment.
  • the left column represents the atomic arrangement of the initial structure
  • the right column represents the predicted value of the final energy corresponding to the initial structure.
  • the comparison unit 307 rearranges the predicted final energy values in a predetermined order based on the list.
  • the comparison unit 307 rearranges the predicted final energy values in descending order of energy. Such rearrangement of the final energy predicted values corresponds to a process of extracting the smallest value from the final energy predicted values, in other words, the minimum value or minimum value.
  • the comparison unit 307 extracts the energy indicating the minimum value from the plurality of predicted energies.
  • the "energy” is the predicted value of the final energy corresponding to the final structure when geometry optimization is performed on the initial structure.
  • the local minimum is the minimum of the energies.
  • the comparison unit 307 outputs to the output unit 108 a list in which the predicted final energy values are rearranged as described above.
  • FIG. 26 is a flow chart showing an operation example of processing of the search system 300 according to the third embodiment.
  • the processing of steps S301 to S303 is the same as the processing of steps S101 to S103 shown in FIG. 17, respectively, so the description thereof is omitted.
  • Step S304 The search system 300 obtains a prediction model related to a known structure of a known material that has undergone machine learning in advance and has a composition similar to that of the material to be searched, and outputs the prediction model to the prediction model storage unit 312 .
  • Step S305 The prediction unit 306 acquires the initial structure from the structure storage unit 110. FIG. Then, the prediction unit 306 calculates the predicted value of the final energy corresponding to the initial structure using the prediction model acquired in step S304.
  • Step S306 The comparison unit 307 generates a list in which the predicted final energy values calculated in step S305 are rearranged in descending order of energy, and outputs the generated list to the output unit 108 . In other words, the comparison unit 307 extracts the energy indicating the minimum value from the predicted values of the final energy.
  • Step S307 The output unit 108 outputs the predicted values of the initial structures and final energies included in the list generated in step S306 by displaying them on the display in order from the structure with the lowest energy.
  • Embodiment 3 prediction models that have undergone machine learning in advance are used for all initial structures, so there is no need to perform calculations for structure optimization. Therefore, in Embodiment 3, as in Embodiment 1 or 2, it is possible to search for the structure of the atomic arrangement that is considered to be thermodynamically most stable in the novel substance, and the search It is possible to greatly omit the calculation required for In other words, in Embodiment 3, compared to the case where structural optimization is performed for a part of the initial structure, the calculation cost can be reduced, and the stable structure of the atomic arrangement for the composition of the material can be efficiently obtained. can be explored.
  • Search system 400 (search method or program) according to Embodiment 4 of the present disclosure will be described in detail below with reference to the drawings.
  • the search system 400 according to Embodiment 4 uses a prediction model for a known structure that has been machine-learned in advance, and verifies whether or not to relearn the prediction model. It differs from system 300 .
  • the same reference numerals are given to the same constituent elements as in the first, second, or third embodiment, and the description thereof is omitted.
  • FIG. 27 is a block diagram showing the overall configuration including the search system 400 according to the fourth embodiment.
  • the search system 400 includes an acquisition unit 102, a generation unit 103, a calculation unit 104, a learning unit 405, a prediction unit 406, a comparison unit 107, and an output unit 108.
  • Peripheral configurations of the search system 400 include an input unit 101 , a material database (DB) 109 , a structure storage unit 110 , a calculation result storage unit 111 , and a prediction model storage unit 312 . Note that the configuration around the search system 400 may be included in the components of the search system 400 .
  • DB material database
  • the learning unit 405 re-learns the prediction model when the prediction unit 406 determines that the prediction accuracy of the prediction model does not satisfy the conditions. Specifically, the learning unit 405 acquires the final energies of the initial structure and the final structure from the calculation result storage unit 111 and re-learns the prediction model acquired from the prediction model storage unit 312 using these.
  • the learning data set used for re-learning the prediction model includes the initial structure as input data and the final energy as correct data.
  • the learning unit 405 outputs the re-learned prediction model to the prediction unit 406 and the prediction model storage unit 312 .
  • the prediction model storage unit 312 stores the graph neural network structure and weights for the prediction model re-learned by the learning unit 405 . That is, in the prediction model storage unit 312, the already stored prediction model is updated to the re-learned prediction model.
  • the prediction unit 406 acquires the final energy of the initial structure and final structure from the calculation result storage unit 111 .
  • the prediction unit 406 acquires prediction models from the prediction model storage unit 312 .
  • the prediction model acquired by the prediction unit 406 here is a prediction model before being re-learned by the learning unit 405 .
  • the prediction unit 406 predicts the final energy of the initial structure by inputting the initial structure into the obtained prediction model. Then, the prediction unit 406 verifies the prediction accuracy of the prediction model by comparing the predicted value of the final energy with the final energy acquired from the calculation result storage unit 111 .
  • the prediction unit 406 as an example, if the root mean square error (RMSE) between the final energy and the predicted value of the final energy is below a certain value, the prediction accuracy of the prediction model is sufficient, that is, it satisfies the conditions for prediction accuracy. On the other hand, if the above RMSE exceeds a certain value, the prediction unit 406 determines that the prediction accuracy of the prediction model is insufficient, that is, the prediction accuracy condition is not satisfied. For example, the prediction unit 406 may determine that a structure with an atomic arrangement that is actually considered to be the most stable satisfies the conditions for prediction accuracy by predicting it as a stable structure within a certain order. Note that the method for verifying the prediction accuracy of the prediction model is not limited to the above method, and other methods may be used.
  • RMSE root mean square error
  • the prediction unit 406 uses a prediction model for at least one initial structure out of some of the initial structures, so that when structure optimization is performed on the initial structure Predict the second energy corresponding to the structure of the atomic configuration of .
  • the second energy is the predicted value of the final energy corresponding to the final structure when geometry optimization is performed on at least one initial structure.
  • the prediction unit 406 verifies the prediction accuracy of the prediction model by comparing the first energy and the second energy.
  • the first energy is the final energy of the final structure corresponding to at least one initial structure.
  • the prediction unit 406 acquires an initial structure whose final energy has not yet been calculated from the structure storage unit 110.
  • the “initial structure for which the final energy has not yet been calculated” as used herein is a structure excluding some of the initial structures, that is, other initial structures. Then, the prediction unit 406 predicts the final energy of the initial structure by inputting the initial structure into the prediction model.
  • the prediction unit 406 performs other initial structures among the plurality of initial structures.
  • a prediction model is used for the structure to predict the second energy corresponding to the structure of the atomic arrangement if the geometry optimization were performed on the other initial structure.
  • the second energy is the predicted value of the final energy corresponding to the final structure when geometry optimization is performed on other initial structures.
  • the prediction unit 406 outputs the initial structure and the final energy prediction value corresponding to the initial structure to the comparison unit 107 .
  • FIG. 28 is a flow chart showing an operation example of processing of the search system 400 according to the fourth embodiment.
  • the processing of steps S401 to S404 is the same as the processing of steps S301 to S304 shown in FIG. 26, respectively, so description thereof will be omitted.
  • Step S405 The calculation unit 104 performs structure optimization on some of the initial structures generated in step S403, and calculates the final energy corresponding to the final structure obtained by performing the structure optimization. Calculate Calculation unit 104 then outputs the calculation result to calculation result storage unit 111 .
  • Step S406 The prediction unit 406 acquires an initial structure, that is, a partial initial structure, from the calculation result storage unit 111 . Then, the prediction unit 406 calculates a predicted final energy value corresponding to a part of the initial structures using the prediction model acquired in step S404.
  • Step S407 The prediction unit 406 verifies the prediction accuracy of the prediction model by comparing the predicted value of the final energy calculated in step S406 and the final energy calculated in step S405. If the prediction result satisfies the prediction accuracy condition (step S407: Yes), the process proceeds to step S409. On the other hand, if the prediction result does not satisfy the prediction accuracy condition (step S407: No), the process proceeds to step S408.
  • Step S408 The learning unit 405 re-learns the prediction model configured by the graph neural network using the set of the final energy and the initial structure calculated in step S405 as a learning data set.
  • the learning unit 405 then outputs the re-learned prediction model to the prediction unit 406 and the prediction model storage unit 312 .
  • a set of an initial structure and a final energy different from the partial initial structure may be used as a learning data set. In this case, the calculation unit 104 needs to separately calculate the final energy corresponding to the different initial structure.
  • Step S409 The prediction unit 406 acquires from the structure storage unit 110 an initial structure for which the final energy has not been calculated, that is, another initial structure among the plurality of initial structures. Then, the prediction unit 406 calculates predicted values of final energies corresponding to other initial structures using the prediction model.
  • the prediction model if the prediction result satisfies the prediction accuracy condition in step S407, the prediction model acquired in S404 is adopted. On the other hand, if the prediction result does not satisfy the prediction accuracy condition in step S407, the re-learned prediction model is adopted in step S408.
  • Step S410 The comparison unit 107 generates a list in which the final energy calculated in step S405 and the predicted value of the final energy calculated in step S409 are rearranged in order from the lowest energy value, and outputs the generated list. Output to unit 108 .
  • the comparison unit 107 extracts the energy indicating the minimum value from the final energy and the predicted value of the final energy.
  • the comparison unit 107 (at the thirteenth step) extracts the third energy indicating the minimum value based on the first energy and the second energy.
  • the first energy is the final energy obtained from the calculation result storage unit 111
  • the second energy is the predicted value of the final energy obtained from the prediction unit 406 .
  • the third energy is the minimum value of the first energy and the second energy.
  • Step S411 The output unit 108 outputs the predicted values of the initial structures and final energies included in the list generated in step S410 by displaying them on the display in order from the structure with the lowest energy.
  • Embodiment 4 the prediction accuracy of the prediction model is verified while using the prediction model that has undergone machine learning in advance. Therefore, in Embodiment 4, it becomes easier to realize a prediction model with sufficient prediction accuracy.
  • Embodiment 4 by using a prediction model that satisfies the condition of prediction accuracy, that is, a prediction model with relatively high prediction accuracy, it is easier to more efficiently search for a stable structure of atomic arrangement for the material composition.
  • the minimum value is the minimum value of the first energy and the second energy, but is not limited to this.
  • the first energy is the final energy calculated by the calculator 104
  • the second energy is the predicted value of the final energy predicted by the predictors 106 , 306 and 406 .
  • the smallest value of the first energy and the second energy is the minimum value of the second energy
  • the second smallest value is the minimum value of the first energy
  • these values are approximate.
  • the difference between the two values is within 1/10000 of the minimum value of the second energy.
  • the minimum value may be the minimum value of the first energy instead of the minimum value of the second energy. This is because the actually calculated value is considered to be more accurate than the predicted value.
  • the search systems 100 to 400 acquire a plurality of initial structures by generating a plurality of initial structures by the generation unit 103, but the present invention is not limited to this.
  • the search systems 100-400 may acquire, at the acquisition unit 102, multiple initial structures generated by other systems.
  • the generator 103 is unnecessary. That is, the obtaining step may obtain by generating a plurality of initial structures, or obtain a plurality of initial structures generated by another system.
  • each component may be configured with dedicated hardware or implemented by executing a software program suitable for each component.
  • Each component may be implemented by a program execution unit such as a CPU (Central Processing Unit) or processor reading and executing a software program recorded in a recording medium such as a hard disk or semiconductor memory.
  • a program execution unit such as a CPU (Central Processing Unit) or processor reading and executing a software program recorded in a recording medium such as a hard disk or semiconductor memory.
  • the at least one system described above is specifically a computer system composed of a microprocessor, ROM, RAM, hard disk unit, display unit, keyboard, mouse, and the like.
  • a computer program is stored in the RAM or hard disk unit.
  • At least one of the above systems achieves its functions by a microprocessor operating according to a computer program.
  • the computer program is constructed by combining a plurality of instruction codes indicating instructions to the computer in order to achieve a predetermined function.
  • a part or all of the components constituting at least one of the above systems may be composed of one system LSI (Large Scale Integration).
  • a system LSI is an ultra-multifunctional LSI manufactured by integrating multiple components on a single chip. Specifically, it is a computer system that includes a microprocessor, ROM, RAM, etc. . A computer program is stored in the RAM. The system LSI achieves its functions by the microprocessor operating according to the computer program.
  • a part or all of the components that make up at least one of the above systems may be made up of an IC card or a single module that can be attached to and removed from the device.
  • An IC card or module is a computer system composed of a microprocessor, ROM, RAM, and the like.
  • the IC card or module may include the super multifunctional LSI.
  • the IC card or module achieves its function by the microprocessor operating according to the computer program. This IC card or this module may have tamper resistance.
  • the present disclosure may be the method shown above. It may be a computer program for realizing these methods by a computer, or it may be a digital signal composed of a computer program.
  • the present disclosure is a computer-readable recording medium for computer programs or digital signals, such as flexible discs, hard disks, CD (Compact Disc)-ROM, DVD, DVD-ROM, DVD-RAM, BD (Blu-ray (registered trademark) ) Disc), or recorded in a semiconductor memory or the like. It may be a digital signal recorded on these recording media.
  • CD Compact Disc
  • DVD DVD-ROM
  • DVD-RAM DVD-RAM
  • BD Blu-ray (registered trademark) ) Disc
  • computer programs or digital signals may be transmitted via electric communication lines, wireless or wired communication lines, networks represented by the Internet, data broadcasting, and the like.
  • It may be implemented by another independent computer system by recording the program or digital signal on a recording medium and transferring it, or by transferring the program or digital signal via a network or the like.
  • the present disclosure makes it possible to search for stable atomic arrangement structures without performing calculations for all atomic arrangement structure candidates, and in situations where large-scale computational resources cannot be prepared, stable atomic arrangement of new materials Useful for searching structures.
  • 100, 200, 300, 400 search system 101 input unit 102 acquisition unit 103 generation unit 104, 204 calculation unit 105, 205, 405 learning unit 106, 306, 406 prediction unit 107, 307 comparison unit 108 output unit 109 material DB 110 structure storage unit 111, 211 calculation result storage unit 112, 212, 312 prediction model storage unit

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* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2024189928A1 (ja) * 2023-03-16 2024-09-19 富士通株式会社 情報処理プログラム、情報処理方法、および情報処理装置

Families Citing this family (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN118974837A (zh) * 2022-03-31 2024-11-15 松下知识产权经营株式会社 信息处理方法、信息处理系统以及程序
US12587274B2 (en) 2023-03-28 2026-03-24 Quantum Generative Materials Llc Satellite optimization management system based on natural language input and artificial intelligence
US12368503B2 (en) 2023-12-27 2025-07-22 Quantum Generative Materials Llc Intent-based satellite transmit management based on preexisting historical location and machine learning
US12603701B2 (en) 2023-12-27 2026-04-14 Quantum Generative Materials Llc Distributed satellite constellation management and control system

Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2020054841A1 (ja) * 2018-09-14 2020-03-19 富士フイルム株式会社 化合物探索方法、化合物探索プログラム、記録媒体、及び化合物探索装置
JP2020166706A (ja) * 2019-03-29 2020-10-08 株式会社クロスアビリティ 結晶形予測装置、結晶形予測方法、ニューラルネットワークの製造方法、及びプログラム

Patent Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2020054841A1 (ja) * 2018-09-14 2020-03-19 富士フイルム株式会社 化合物探索方法、化合物探索プログラム、記録媒体、及び化合物探索装置
JP2020166706A (ja) * 2019-03-29 2020-10-08 株式会社クロスアビリティ 結晶形予測装置、結晶形予測方法、ニューラルネットワークの製造方法、及びプログラム

Non-Patent Citations (2)

* Cited by examiner, † Cited by third party
Title
HIROYUKI MATSUI, OKADA TOMOHARU: "Machine-Learning-Assisted Design Tool for Semiconducting and Fluorescent Molecules", THE 67TH JSAP SPRING MEETING 2020, 12 March 2020 (2020-03-12), pages 10 - 115, XP055956660, Retrieved from the Internet <URL:https://confit.atlas.jp/guide/event-img/jsap2020s/12p-A405-15/public/pdf?type=in> [retrieved on 20220831] *
KIYOHARA, SHIN ET AL.: "Interface Structure Determination and Spectrum Analysis Using Machine Learning", JOURNAL OF JAPANESE SOCIETY FOR ARTIFICIAL INTELLIGENCE, vol. 34, no. 3, 1 May 2019 (2019-05-01), pages 345 - 350, XP009542998, ISSN: 2188-2266, DOI: 10.11517/jjsai.34.3_345 *

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2024189928A1 (ja) * 2023-03-16 2024-09-19 富士通株式会社 情報処理プログラム、情報処理方法、および情報処理装置

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