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Issue 13, 2021
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Machine learning dielectric screening for the simulation of excited state properties of molecules and materials

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Abstract

Accurate and efficient calculations of absorption spectra of molecules and materials are essential for the understanding and rational design of broad classes of systems. Solving the Bethe–Salpeter equation (BSE) for electron–hole pairs usually yields accurate predictions of absorption spectra, but it is computationally expensive, especially if thermal averages of spectra computed for multiple configurations are required. We present a method based on machine learning to evaluate a key quantity entering the definition of absorption spectra: the dielectric screening. We show that our approach yields a model for the screening that is transferable between multiple configurations sampled during first principles molecular dynamics simulations; hence it leads to a substantial improvement in the efficiency of calculations of finite temperature spectra. We obtained computational gains of one to two orders of magnitude for systems with 50 to 500 atoms, including liquids, solids, nanostructures, and solid/liquid interfaces. Importantly, the models of dielectric screening derived here may be used not only in the solution of the BSE but also in developing functionals for time-dependent density functional theory (TDDFT) calculations of homogeneous and heterogeneous systems. Overall, our work provides a strategy to combine machine learning with electronic structure calculations to accelerate first principles simulations of excited-state properties.

Graphical abstract: Machine learning dielectric screening for the simulation of excited state properties of molecules and materials

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Supplementary files

Article information


Submitted
27 Jan 2021
Accepted
12 Feb 2021
First published
02 Mar 2021

This article is Open Access
All publication charges for this article have been paid for by the Royal Society of Chemistry

Chem. Sci., 2021,12, 4970-4980
Article type
Edge Article

Machine learning dielectric screening for the simulation of excited state properties of molecules and materials

S. S. Dong, M. Govoni and G. Galli, Chem. Sci., 2021, 12, 4970
DOI: 10.1039/D1SC00503K

This article is licensed under a Creative Commons Attribution-NonCommercial 3.0 Unported Licence. Material from this article can be used in other publications provided that the correct acknowledgement is given with the reproduced material and it is not used for commercial purposes.

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    [Original citation] - Published by The Royal Society of Chemistry (RSC) on behalf of the European Society for Photobiology, the European Photochemistry Association, and RSC.
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    [Original citation] - Published by The Royal Society of Chemistry.

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