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  • Letter
  • Open Access

Deep learning of spectra: Predicting the dielectric function of semiconductors

Malte Grunert*,†, Max Großmann*, and Erich Runge

  • *These authors contributed equally to this work.
  • †Contact author: malte.grunert@tu-ilmenau.de
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Phys. Rev. Materials 8, L122201 – Published 2 December, 2024

DOI: https://doi.org/10.1103/PhysRevMaterials.8.L122201

Abstract

Predicting spectra and related properties such as the dielectric function of crystalline materials based on machine learning has a huge, hitherto unexplored, technological potential. For this reason, we create an ab initio database of 9915 dielectric tensors of semiconductors and insulators calculated in the independent-particle approximation (IPA). In addition, we present the OptiMate family of machine learning models, a series of graph attention neural networks (GAT) trained to predict the dielectric function and refractive index. OptiMate yields accurate prediction of spectra of semiconductors using only their crystal structure. Smooth, artifact-free curves are obtained without these properties being enforced by penalties.

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Phys. Rev. Materials 8, L122201– Published 2 December, 2024
  • Received 13 June 2024
  • Accepted 25 October 2024
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Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article's title, journal citation, and DOI.

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