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Graph attention neural networks for mapping materials and molecules beyond short-range interatomic correlations
Yuanbin Liu1,2, Xin Liu3,4, Bingyang Cao1
1Key Laboratory for Thermal Science and Power Engineering of Ministry of Education, Department of Engineering Mechanics, Tsinghua University, Beijing 100084, People's Republic of China.
Summary
This study introduces a graph attention neural network for materials science, improving machine learning models by incorporating both local and non-local atomic information. This approach accelerates materials discovery and accurately predicts properties like electronic structure and heat capacity.
Area of Science:
- Materials Science
- Computational Chemistry
- Artificial Intelligence
Background:
- Current machine learning models in chemical science often rely on localized atomic environments, limiting their ability to capture long-range interactions.
- This limitation hinders the reliability of models for complex systems and accurate prediction of physical effects.
Purpose of the Study:
- To develop a unified machine learning framework that integrates both local and non-local atomic information for materials and molecules.
- To create a generalizable and interpretable representation for chemical structures.
- To enhance the accuracy and scope of machine learning applications in materials discovery and simulation.
Main Methods:
- A graph attention neural network was developed to process atomic environments at multiple scales.
- The framework maps materials and molecules into a representation combining local and non-local atomic correlations.
- The model was applied to predict electronic structure properties of metal-organic frameworks (MOFs) and heat capacity of nanoporous materials.
Main Results:
- The graph attention neural network achieved state-of-the-art performance in predicting electronic structure properties of MOFs.
- Clustering analysis demonstrated the model's capability for high-level identification of MOFs, aiding in rational material design.
- The model accurately predicted the heat capacity of complex nanoporous materials, showcasing versatility beyond electronic properties.
Conclusions:
- The developed graph attention neural network effectively combines local and non-local atomic information, overcoming limitations of previous machine learning approaches.
- This unified framework enhances the prediction of diverse physical properties for various materials, including MOFs and nanoporous materials.
- The study facilitates accelerated materials discovery and rational design through accurate and interpretable machine learning predictions.
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