Machine-learning-accelerated predictions of optical properties of condensed systems based on many-body perturbation theory

ORAL

Abstract

Accurate and efficient predictions of absorption spectra of molecules and solids are essential for the understanding and rational design of broad classes of materials, including photo-absorbers in solar and photo-electrochemical cells and defective insulators and semiconductors hosting optically addressable spin-defects. We present an approach to improve the efficiency of first principles calculations of absorption spectra of complex materials at finite temperature, based on the solution of the Bethe-Salpeter equation (BSE) [1]. We use machine learning techniques to predict the spectra of snapshots extracted from ab initio molecular dynamics simulations, and we use data generated by explicitly solving the BSE for a small subset of snapshots. We present results for nanoclusters, solids, liquids, including water, and semiconductor-water interfaces.

[1] N. L. Nguyen, H. Ma, M. Govoni, F. Gygi, and G. Galli, Phys. Rev. Lett. 122 (2019).

*The work was supported by Advanced Materials for Energy-Water Systems (AMEWS) Center, an Energy Frontier Research Center funded by the U.S. Department of Energy, Office of Science, Basic Energy Sciences (DOE-BES), and Midwest Integrated Center for Computational Materials (MICCoM) as part of the Computational Materials Science Program funded by DOE-BES.

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Presenters

  • Sijia Dong

    • Materials Science Division, Argonne National Laboratory

Authors

  • Sijia Dong

    • Materials Science Division, Argonne National Laboratory

  • Marco Govoni

    • Materials Science Division, Argonne National Laboratory
    • Materials Science Division and Center for Molecular Engineering, Argonne National Laboratory
    • Argonne National Laboratory
    • Argonne National Lab
    • Argonne Natl Lab

  • Giulia Galli

    • University of Chicago
    • Pritzker School of Molecular Engineering, University of Chicago
    • Pritzker School of Molecular Engineering, University of Chicago, Chicago, IL 60637, USA
    • University of Chicago and Argonne National Laboratory
    • Pritzker School of Molecular Engineering, The University of Chicago