Machine-Learning X-Ray Absorption Spectra to Quantitative Accuracy

Matthew R. Carbone, Mehmet Topsakal, Deyu Lu, and Shinjae Yoo
Phys. Rev. Lett. 124, 156401 – Published 16 April 2020
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Abstract

Simulations of excited state properties, such as spectral functions, are often computationally expensive and therefore not suitable for high-throughput modeling. As a proof of principle, we demonstrate that graph-based neural networks can be used to predict the x-ray absorption near-edge structure spectra of molecules to quantitative accuracy. Specifically, the predicted spectra reproduce nearly all prominent peaks, with 90% of the predicted peak locations within 1 eV of the ground truth. Besides its own utility in spectral analysis and structure inference, our method can be combined with structure search algorithms to enable high-throughput spectrum sampling of the vast material configuration space, which opens up new pathways to material design and discovery.

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  • Received 20 December 2019
  • Accepted 30 March 2020

DOI:https://doi.org/10.1103/PhysRevLett.124.156401

© 2020 American Physical Society

Physics Subject Headings (PhySH)

Condensed Matter, Materials & Applied Physics

Authors & Affiliations

Matthew R. Carbone1, Mehmet Topsakal2,*, Deyu Lu3,†, and Shinjae Yoo4,‡

  • 1Department of Chemistry, Columbia University, New York, New York 10027, USA
  • 2Nuclear Science and Technology Department, Brookhaven National Laboratory, Upton, New York 11973, USA
  • 3Center for Functional Nanomaterials, Brookhaven National Laboratory, Upton, New York 11973, USA
  • 4Computational Science Initiative, Brookhaven National Laboratory, Upton, New York 11973, USA

  • *mtopsakal@bnl.gov
  • dlu@bnl.gov
  • sjyoo@bnl.gov

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Issue

Vol. 124, Iss. 15 — 17 April 2020

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