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Data-mechanism driven material attribute prediction method of graph neural network

A neural network and material property technology, which is applied in the fields of material discovery and graph neural network, can solve the problems of ignoring material properties in computational models and low generalization performance of deep learning networks, and achieve the effect of improving accuracy

Active Publication Date: 2022-07-29
UNIV OF SCI & TECH BEIJING
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Problems solved by technology

[0004] The present invention provides a data-mechanism-driven material property prediction method of graph neural network to solve the problem of mechanism-driven calculation models ignoring material properties and deep learning network generalization. The problem of low chemical performance

Method used

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  • Data-mechanism driven material attribute prediction method of graph neural network
  • Data-mechanism driven material attribute prediction method of graph neural network
  • Data-mechanism driven material attribute prediction method of graph neural network

Examples

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Embodiment 1

[0079] In this example, a well-collected molecular dataset is used to validate the effectiveness of the graph neural network's data-mechanism-driven material property prediction method. Molecular dataset includes 4208 pieces of data, each molecule includes: graph structure, descriptor, experimental value and calculated value. The graph structure includes the characteristics of the atoms and the characteristics of the bonds in the graph. Descriptors include: relative molecular mass, relative mass of heavy atoms, number of amino and hydroxyl groups, number of nitro groups, number of hydrogen acceptors, number of hydrogen donors, number of rotatable bonds, number of valence electrons, relative molecular mass, relative number of heavy atoms Mass, number of amino and hydroxyl groups. Molecular datasets are collected using methods such as image 3 shown.

[0080] The batch size of this model training is 32, the parameters are optimized using the Adam optimizer and the initial lea...

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Abstract

The invention discloses a data-mechanism driven material attribute prediction method of a graph neural network. The method comprises the following steps: S1, obtaining descriptor features and a graph structure of a to-be-predicted material molecule; s2, a final feature descriptor is screened out through feature engineering; s3, using graph convolution and a graph attention network to extract different levels of molecular graph features; s4, fusing the molecular graph features with the descriptor features by using a feature fusion layer; s5, using a correction module to better fuse the calculated value and the experimental value; wherein the calculated value is a numerical value obtained through simulation calculation according to a first principle, and the experimental value is an actual material attribute measured through an experiment; and S6, fusing the calculated value of the mechanism-driven model and the deep learning data-driven model for model reasoning, and outputting a numerical value of a prediction attribute. According to the method, descriptor features of molecules and graph structure features are fused, and the problems that graph structure data information is incomplete and molecular attributes are ignored by the descriptor features are solved.

Description

technical field [0001] The invention relates to the technical field of material discovery and graph neural network, in particular to a data-mechanism driven material property prediction method of graph neural network. Background technique [0002] Molecular materials are widely used in medical and health, food, daily chemical and other fields. Therefore, accelerating the discovery of new molecular materials is of great significance for promoting the development of science and society. At present, the study of molecular materials is very time-consuming and requires a lot of effort to determine certain target properties and optimize the molecular synthesis conditions. Theoretical high-throughput computational methods are often used to predict the properties of molecules. Such mechanism-driven computational models with plausible explanations can effectively accelerate the discovery of new materials. However, the mechanism-driven computational model is a theoretical model wit...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/62G06N3/04
CPCG06N3/045G06F18/214
Inventor 张桃红陈赛安陈晗
Owner UNIV OF SCI & TECH BEIJING
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