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Material symmetry recognition and property prediction accomplished by crystal capsule representation

Author

Listed:
  • Chao Liang

    (Sun Yat-Sen University)

  • Yilimiranmu Rouzhahong

    (Sun Yat-Sen University)

  • Caiyuan Ye

    (Sun Yat-Sen University)

  • Chong Li

    (Sun Yat-Sen University)

  • Biao Wang

    (Sun Yat-Sen University)

  • Huashan Li

    (Sun Yat-Sen University
    Sun Yat-sen University
    Sun Yat-sen University)

Abstract

Learning the global crystal symmetry and interpreting the equivariant information is crucial for accurately predicting material properties, yet remains to be fully accomplished by existing algorithms based on convolution networks. To overcome this challenge, here we develop a machine learning (ML) model, named symmetry-enhanced equivariance network (SEN), to build material representation with joint structure-chemical patterns, to encode important clusters embedded in the crystal structure, and to learn pattern equivariance in different scales via capsule transformers. Quantitative analyses of the intermediate matrices demonstrate that the intrinsic crystal symmetries and interactions between clusters have been exactly perceived by the SEN model and critically affect the prediction performances by reducing effective feature space. The mean absolute errors (MAEs) of 0.181 eV and 0.0161 eV/atom are obtained for predicting bandgap and formation energy in the MatBench dataset. The general and interpretable SEN model reveals the potential to design ML models by implicitly encoding feature relationship based on physical mechanisms.

Suggested Citation

  • Chao Liang & Yilimiranmu Rouzhahong & Caiyuan Ye & Chong Li & Biao Wang & Huashan Li, 2023. "Material symmetry recognition and property prediction accomplished by crystal capsule representation," Nature Communications, Nature, vol. 14(1), pages 1-11, December.
  • Handle: RePEc:nat:natcom:v:14:y:2023:i:1:d:10.1038_s41467-023-40756-2
    DOI: 10.1038/s41467-023-40756-2
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    1. Simon Batzner & Albert Musaelian & Lixin Sun & Mario Geiger & Jonathan P. Mailoa & Mordechai Kornbluth & Nicola Molinari & Tess E. Smidt & Boris Kozinsky, 2022. "E(3)-equivariant graph neural networks for data-efficient and accurate interatomic potentials," Nature Communications, Nature, vol. 13(1), pages 1-11, December.
    2. K. T. Schütt & M. Gastegger & A. Tkatchenko & K.-R. Müller & R. J. Maurer, 2019. "Unifying machine learning and quantum chemistry with a deep neural network for molecular wavefunctions," Nature Communications, Nature, vol. 10(1), pages 1-10, December.
    3. Siwen Wang & Hemanth Somarajan Pillai & Hongliang Xin, 2020. "Bayesian learning of chemisorption for bridging the complexity of electronic descriptors," Nature Communications, Nature, vol. 11(1), pages 1-7, December.
    4. Andrij Vasylenko & Jacinthe Gamon & Benjamin B. Duff & Vladimir V. Gusev & Luke M. Daniels & Marco Zanella & J. Felix Shin & Paul M. Sharp & Alexandra Morscher & Ruiyong Chen & Alex R. Neale & Laurenc, 2021. "Element selection for crystalline inorganic solid discovery guided by unsupervised machine learning of experimentally explored chemistry," Nature Communications, Nature, vol. 12(1), pages 1-12, December.
    5. Keith T. Butler & Daniel W. Davies & Hugh Cartwright & Olexandr Isayev & Aron Walsh, 2018. "Machine learning for molecular and materials science," Nature, Nature, vol. 559(7715), pages 547-555, July.
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