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Density of states prediction for materials discovery via contrastive learning from probabilistic embeddings

Author

Listed:
  • Shufeng Kong

    (Cornell University)

  • Francesco Ricci

    (Lawrence Berkeley National Laboratory)

  • Dan Guevarra

    (California Institute of Technology)

  • Jeffrey B. Neaton

    (Lawrence Berkeley National Laboratory
    University of California, Berkeley
    Kavli Energy NanoSciences Institute at Berkeley)

  • Carla P. Gomes

    (Cornell University)

  • John M. Gregoire

    (California Institute of Technology)

Abstract

Machine learning for materials discovery has largely focused on predicting an individual scalar rather than multiple related properties, where spectral properties are an important example. Fundamental spectral properties include the phonon density of states (phDOS) and the electronic density of states (eDOS), which individually or collectively are the origins of a breadth of materials observables and functions. Building upon the success of graph attention networks for encoding crystalline materials, we introduce a probabilistic embedding generator specifically tailored to the prediction of spectral properties. Coupled with supervised contrastive learning, our materials-to-spectrum (Mat2Spec) model outperforms state-of-the-art methods for predicting ab initio phDOS and eDOS for crystalline materials. We demonstrate Mat2Spec’s ability to identify eDOS gaps below the Fermi energy, validating predictions with ab initio calculations and thereby discovering candidate thermoelectrics and transparent conductors. Mat2Spec is an exemplar framework for predicting spectral properties of materials via strategically incorporated machine learning techniques.

Suggested Citation

  • Shufeng Kong & Francesco Ricci & Dan Guevarra & Jeffrey B. Neaton & Carla P. Gomes & John M. Gregoire, 2022. "Density of states prediction for materials discovery via contrastive learning from probabilistic embeddings," Nature Communications, Nature, vol. 13(1), pages 1-12, December.
  • Handle: RePEc:nat:natcom:v:13:y:2022:i:1:d:10.1038_s41467-022-28543-x
    DOI: 10.1038/s41467-022-28543-x
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    References listed on IDEAS

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    2. Rhys E. A. Goodall & Alpha A. Lee, 2020. "Predicting materials properties without crystal structure: deep representation learning from stoichiometry," Nature Communications, Nature, vol. 11(1), pages 1-9, December.
    3. Morten N. Gjerding & Mohnish Pandey & Kristian S. Thygesen, 2017. "Band structure engineered layered metals for low-loss plasmonics," Nature Communications, Nature, vol. 8(1), pages 1-8, April.
    4. 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.
    5. Olexandr Isayev & Corey Oses & Cormac Toher & Eric Gossett & Stefano Curtarolo & Alexander Tropsha, 2017. "Universal fragment descriptors for predicting properties of inorganic crystals," Nature Communications, Nature, vol. 8(1), pages 1-12, August.
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