US-20260127410-A1 - MATERIAL PROPERTY PREDICTION SYSTEM AND METHOD

US20260127410A1US 20260127410 A1US20260127410 A1US 20260127410A1US-20260127410-A1

Abstract

A system for predicting a property of a material of the present invention may extract a graph embedding by inputting material information into a first artificial intelligence (AI) model, and extract a text embedding by inputting a text description of a crystal structure of the material into a second AI model, classify the text embedding into a plurality of structure information embeddings, and concatenate the graph embedding with at least one of the plurality of structure information embeddings, wherein the structure information embeddings may be classified to include global information, semi-global information, and local information of the crystal structure. The provided system and method may be employed to predict aspects of a crystal structure, a molecular structure, a protein structure, a catalyst structure, or a metal-organic framework of a target material, or to predict physical properties of a target material.

Inventors

Changyoung PARK
Jaewan LEE
Munjal MRIGI
Hongjun Yang
Sehui HAN

Assignees

LG MANAGEMENT DEVELOPMENT INSTITUTE CO., LTD.

Dates

Publication Date: 20260507
Application Date: 20251230
Priority Date: 20240905

Claims (20)

1 . A system comprising: at least one processor; and at least one memory storing an instruction or information executed by the at least one processor, wherein an operation performed by the instruction or information executed by the at least one processor comprises: an operation of extracting a graph embedding by inputting information relating to a material into a first artificial intelligence (AI) model; an operation of extracting a text embedding by inputting a text description of a crystal structure of the material into a second AI model; an operation of classifying the text embedding into a plurality of structure information embeddings; and an operation of concatenating the graph embedding with at least one of the plurality of structure information embeddings, wherein the structure information embeddings are classified to include global information, semi-global information, and local information of the crystal structure; and an operation of determining details of at least one aspect of a crystal structure, a molecular structure, a protein structure, a catalyst structure, or a metal-organic framework (MOF) for the material, or a numerical representation for at least one physical property of the material, based on results of the concatenating operation.
2 . The system of claim 1 , wherein the first AI model comprises: an embedding layer encoding a graph node for an atom of the crystal structure in the material information and connection information between the atoms; and an interaction layer repeatedly refining a representation of the material based on the graph node and the connection information.
3 . The system of claim 1 , wherein the global information comprises comprehensive arrangement information of the crystal structure including a mineral type, a space group, a dimensionality, and a symmetry property, the semi-global information comprises atomic arrangement information in the crystal structure including geometry and connectivity in the crystal structure, and the local information comprises atomic-level detailed information in the crystal structure including a type of the atom and a bond length between the atoms in the crystal structure.
4 . The system of claim 1 , wherein the graph embedding is projected into a 128-dimensional vector through a first projection head of the first AI model, the text embedding is projected into a 128-dimensional vector through a second projection head of the second AI model, and the graph embedding and the text embedding are concatenated in a concatenate layer to generate a multimodal embedding.
5 . The system of claim 4 , wherein the multimodal embedding is input into a fully connected layer to predict a property of a target material.
6 . The system of claim 3 , wherein the graph embedding is concatenated with the text embedding including at least the semi-global information.
7 . The system of claim 3 , wherein the graph embedding is concatenated with the text embedding including the global information and the semi-global information.
8 . A method comprising: extracting a graph embedding by inputting information relating to a target material into a first artificial intelligence (AI) model executed by a processor; extracting a text embedding by inputting a text description of a crystal structure of the target material into a second AI model executed by the processor; classifying the text embedding into a plurality of structure information embeddings by the processor; and concatenating the graph embedding with at least one of the plurality of structure information embeddings by the processor, wherein the structure information embeddings are classified to include global information, semi-global information, and local information of the crystal structure; and predicting details of at least one aspect of a crystal structure, a molecular structure, a protein structure, a catalyst structure, or a metal-organic framework (MOF) for the target material, or a numerical representation for at least one physical property of the target material, based on results of the concatenating operation.
9 . The method of claim 8 , wherein, in the extracting of the graph embedding, the material information input into the first AI model is encoded in an embedding layer as a graph node for an atom of a crystal structure in the material information and connection information between the atoms, and a representation of the material is repeatedly refined in an interaction layer based on the graph node and the connection information.
10 . The method of claim 8 , wherein the global information comprises comprehensive arrangement information of the crystal structure including a mineral type, a space group, a dimensionality, and a symmetry property, the semi-global information comprises atomic arrangement information in the crystal structure including geometry and connectivity in the crystal structure, and the local information comprises atomic-level detailed information in the crystal structure including a type of the atom and a bond length between the atoms in the crystal structure.
11 . The method of claim 8 , wherein, before the concatenating, the graph embedding is projected into a 128-dimensional vector through a first projection head of the first AI model, and the text embedding is projected into a 128-dimensional vector through a second projection head of the second AI model, and in the concatenating, the projected graph embedding and the projected text embedding are concatenated in a concatenate layer to generate a multimodal embedding.
12 . The method of claim 11 , wherein the predicting step comprises: predicting a property of the target material, wherein the predicting of the property comprises inputting the multimodal embedding into a fully connected layer to predict the property of the target material.
13 . The method of claim 10 , wherein, in the concatenating, the graph embedding is concatenated with the text embedding including at least the semi-global information.
14 . The method of claim 10 , wherein, in the concatenating, the graph embedding is concatenated with the text embedding including the global information and the semi-global information.
15 . The method of claim 8 , wherein the target material is a cathode material for a secondary battery, and wherein the predicting step predicts a shear modulus, a bulk modulus, or a bandgap of the target material.
16 . The method of claim 15 , wherein the target material comprises at least 60% manganese by weight.
17 . An application specific integrated circuit (ASIC) comprising a functional block including a non-transitory memory storing information and an instruction and at least one processor requesting access to the memory, wherein the memory stores an instruction or information for extracting a graph embedding by inputting information relating to a target material into a first artificial intelligence (AI) model, extracting a text embedding by inputting a text description of a crystal structure of the target material into a second AI model, classifying the text embedding into a plurality of structure information embeddings, and concatenating the graph embedding with at least one of the plurality of structure information embeddings, wherein the structure information embeddings are classified to include global information, semi-global information, and local information of the crystal structure, and wherein the memory stores a further instruction or information for generating a predicted numerical value for at least one physical property for the target material based on a result of the concatenating.
18 . A system comprising: at least one processor; and at least one memory storing an instruction or information executed by the at least one processor, wherein an operation performed by the instruction or information executed by the at least one processor comprises: an operation of extracting a graph embedding by inputting information relating to a target material into a first artificial intelligence (AI) model; an operation of extracting a text embedding by inputting a text description of a crystal structure of the target material into a second AI model; an operation of performing cross-attention on the graph embedding and the text embedding; and an operation of predicting a numerical value for at least one physical property for the target material based on a result of the cross-attention operation.
19 . The system of claim 18 , wherein the first AI model comprises: an embedding layer encoding a graph node for an atom of the crystal structure in the target material information and connection information between the atoms; and an interaction layer repeatedly refining a representation of the target material based on the graph node and the connection information.
20 . The system of claim 18 , wherein the text embedding comprises: comprehensive arrangement information of the crystal structure of the target material; atomic arrangement geometry information in the crystal structure; and atomic-level detailed information in the crystal structure, wherein the comprehensive arrangement information comprises a mineral type, a space group, a dimensionality, and a symmetry property; the atomic arrangement geometry information comprises geometry and connectivity in the crystal structure, and the atomic-level detailed information comprises a type of the atom and a bond length between the atoms in the crystal structure.

Description

CROSS REFERENCE TO RELATED APPLICATIONS This application is a Bypass Continuation of International Patent Application No. PCT/KR2025/008235, filed on Jun. 16, 2025, which claims priority from and the benefit of Korean Patent Application No. 10-2024-0120761, filed on Sep. 5, 2024, Korean Patent Application No. 10-2025-0037485, filed on Mar. 24, 2025, and Korean Patent Application No. 10-2025-0037494, filed on Mar. 24, 2025, each of which is hereby incorporated by reference for all purposes as if fully set forth herein. BACKGROUND Field Embodiments of the invention relate generally to a material property prediction system and method, and, more particularly, to a material property prediction system and prediction method capable of predicting a property of a material by using a graph-based model and a text-based model. Discussion of the Background Recently, artificial intelligence (AI) technology has shown advanced development and is attracting attention across society. AI refers to the execution by a computer of intellectual abilities unique to humans at a high level of capability, such as “a computer brain that performs domains achievable by human intelligence,” “engineering and science for making intelligent machines,” “a set of algorithms designed to think, perceive, and act like a human,” and the like. AI is introduced as providing a highly integrated smart space in combination with augmented reality, Internet of Things, edge computing, and digital twin, and the like and is emphasized as a core new technology leading the era of the Fourth Industrial Revolution. In addition, AI is drawing attention as a next-generation growth engine that can evolve the industrial ecosystem beyond standardized problem-solving, and is actively applied not only to IT, medical care, agriculture, energy, automobiles, and robots, but also to knowledge service industries such as distribution, finance, law, education, real estate, advertising, and communication. That is, AI is preparing for a new era by being combined with all existing systems ranging from industries aimed at improving convenience or quality of life to overall cultural and artistic aspects of our society. As product development methods have recently diversified, the development of new materials usable in product manufacturing has been actively conducted. Such materials greatly affect characteristics of products, and properties of a material sometimes become a determining factor for characteristics of a manufactured product. Therefore, in order to more efficiently develop and mass-produce a material used in product manufacturing, predicting and analyzing the property of the material may be essential. Traditionally, to check characteristics of the manufactured product according to the characteristics of the material, a method in which various materials are developed, the characteristics of each material are checked, and then the material is test-applied to a final product to check characteristics of the final product has been used. However, the conventional method requires significant cost and time for the development of materials and the characteristics of the materials, and also has a problem that it is difficult to find a material having optimal characteristics. To improve such a traditional method, research on methods for predicting a property of a material using AI has been actively conducted. Related art includes Korean Patent Publication No. 10-2024-0011349 (Jan. 26, 2024). The above information disclosed in this Background section is only for understanding of the background of the inventive concepts, and, therefore, it may contain information that does not constitute prior art. SUMMARY One embodiment of the present invention is directed to providing a material property prediction system and prediction method capable of predicting a property of a material by combining a graph-based structural embedding related to the property of the material and a text embedding related to the property of the material derived from a language model. One embodiment of the present invention is directed to providing a material property prediction system and prediction method capable of predicting a property of a material by performing Cross-Attention between a graph-based structural embedding related to the property of the material and a text embedding related to the property of the material derived from a language model. Additional features of the inventive concepts will be set forth in the description which follows, and in part will be apparent from the description, or may be learned by practice of the inventive concepts. A system for predicting a property of a material of one embodiment of the present invention may include at least one processor, and at least one memory storing an instruction or information executed by the at least one processor, wherein an operation performed by the instruction or information executed by the at least one processor may comprise an operation of