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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.

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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.

Keywords:
graph attention neural networksin silico screeninglong-range interatomic correlationsmachine learningmaterials representationmetal-organic frameworks

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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.