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Physics-Guided Dual Self-Supervised Learning for Structure-Based Material Property Prediction.

Nihang Fu1, Lai Wei1, Jianjun Hu1

  • 1Department of Computer Science and Engineering, University of South Carolina, Columbia, South Carolina 29201, United States.

The Journal of Physical Chemistry Letters
|March 5, 2024
PubMed
Summary
This summary is machine-generated.

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This study introduces DSSL, a novel physics-guided self-supervised learning (SSL) framework for material property prediction. DSSL enhances graph neural network performance by integrating generative and contrastive SSL strategies, improving data-scarce predictions.

Area of Science:

  • Materials Science
  • Artificial Intelligence
  • Computational Chemistry

Background:

  • Deep learning excels at material property prediction but requires extensive labeled data.
  • Data scarcity is a major limitation for training high-performance deep learning models in materials science.

Purpose of the Study:

  • To develop a physics-guided dual self-supervised learning (SSL) framework (DSSL) for graph neural network (GNN)-based material property prediction.
  • To address the challenge of limited labeled data in materials science by leveraging SSL techniques.

Main Methods:

  • DSSL combines node masking-based generative SSL and atomic coordinate perturbation-based contrastive SSL to capture crystal structure information.
  • A physics-guided pretraining strategy is employed, using atomic stiffness prediction as a pretext task related to macroscopic properties.

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  • The DSSL model is pretrained on the Materials Project database and fine-tuned on 10 diverse material property datasets.

    • Main Results:

    • The DSSL framework demonstrates significant performance improvements in material property prediction compared to baseline models.
    • Experimental results show up to a 26.89% performance enhancement by incorporating physics-guided SSL.
    • The study validates the effectiveness of integrating physical principles into SSL for enhanced neural network training.

    Conclusions:

    • Physics-guided dual SSL offers a powerful approach to overcome data limitations in material property prediction.
    • DSSL enhances the predictive accuracy of GNNs by effectively learning from limited data through combined generative and contrastive strategies.
    • This work highlights the potential of integrating domain knowledge (physics) into AI models for scientific discovery.