Issue 32, 2021

Physically inspired deep learning of molecular excitations and photoemission spectra

Abstract

Modern functional materials consist of large molecular building blocks with significant chemical complexity which limits spectroscopic property prediction with accurate first-principles methods. Consequently, a targeted design of materials with tailored optoelectronic properties by high-throughput screening is bound to fail without efficient methods to predict molecular excited-state properties across chemical space. In this work, we present a deep neural network that predicts charged quasiparticle excitations for large and complex organic molecules with a rich elemental diversity and a size well out of reach of accurate many body perturbation theory calculations. The model exploits the fundamental underlying physics of molecular resonances as eigenvalues of a latent Hamiltonian matrix and is thus able to accurately describe multiple resonances simultaneously. The performance of this model is demonstrated for a range of organic molecules across chemical composition space and configuration space. We further showcase the model capabilities by predicting photoemission spectra at the level of the GW approximation for previously unseen conjugated molecules.

Graphical abstract: Physically inspired deep learning of molecular excitations and photoemission spectra

Supplementary files

Article information

Article type
Edge Article
Submitted
17 3月 2021
Accepted
29 6月 2021
First published
30 6月 2021
This article is Open Access

All publication charges for this article have been paid for by the Royal Society of Chemistry
Creative Commons BY license

Chem. Sci., 2021,12, 10755-10764

Physically inspired deep learning of molecular excitations and photoemission spectra

J. Westermayr and R. J. Maurer, Chem. Sci., 2021, 12, 10755 DOI: 10.1039/D1SC01542G

This article is licensed under a Creative Commons Attribution 3.0 Unported Licence. You can use material from this article in other publications without requesting further permissions from the RSC, provided that the correct acknowledgement is given.

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