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Physically inspired deep learning of molecular excitations and photoemission spectra
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Westermayr, Julia and Maurer, Reinhard J. (2021) Physically inspired deep learning of molecular excitations and photoemission spectra. Chemical Science, 12 (32). pp. 10755-10764. doi:10.1039/D1SC01542G ISSN 2041-6520.
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Official URL: https://doi.org/10.1039/D1SC01542G
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 quasi-particle excitations for large and complex organic molecules with a rich elemental diversity and asize 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.
Item Type: | Journal Article | ||||||||||||
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Subjects: | Q Science > Q Science (General) Q Science > QA Mathematics Q Science > QC Physics Q Science > QD Chemistry T Technology > TA Engineering (General). Civil engineering (General) |
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Divisions: | Faculty of Science, Engineering and Medicine > Science > Chemistry Faculty of Science, Engineering and Medicine > Science > Chemistry > Computational and Theoretical Chemistry Centre |
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Library of Congress Subject Headings (LCSH): | Machine learning, Photoemission , Optoelectronics, Organic compounds , Hamiltonian systems | ||||||||||||
Journal or Publication Title: | Chemical Science | ||||||||||||
Publisher: | Royal Society of Chemistry | ||||||||||||
ISSN: | 2041-6520 | ||||||||||||
Official Date: | 28 August 2021 | ||||||||||||
Dates: |
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Volume: | 12 | ||||||||||||
Number: | 32 | ||||||||||||
Page Range: | pp. 10755-10764 | ||||||||||||
DOI: | 10.1039/D1SC01542G | ||||||||||||
Status: | Peer Reviewed | ||||||||||||
Publication Status: | Published | ||||||||||||
Access rights to Published version: | Open Access (Creative Commons) | ||||||||||||
Date of first compliant deposit: | 13 July 2021 | ||||||||||||
Date of first compliant Open Access: | 13 July 2021 | ||||||||||||
Grant number: | FWF [, MR/S016023/1 | ||||||||||||
RIOXX Funder/Project Grant: |
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Open Access Version: |
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