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• W3174875816 endingPage "10764" @default.
• W3174875816 startingPage "10755" @default.
• W3174875816 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." @default.
• W3174875816 created "2021-07-05" @default.
• W3174875816 creator A5016855366 @default.
• W3174875816 creator A5039680751 @default.
• W3174875816 date "2021-01-01" @default.
• W3174875816 modified "2023-10-18" @default.
• W3174875816 title "Physically inspired deep learning of molecular excitations and photoemission spectra" @default.
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• W3174875816 doi "https://doi.org/10.1039/d1sc01542g" @default.
• W3174875816 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/8372319" @default.
• W3174875816 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/34447563" @default.
• W3174875816 hasPublicationYear "2021" @default.
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