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

SUBMITTER: Westermayr J 

PROVIDER: S-EPMC8372319 | biostudies-literature | 2021 Aug

REPOSITORIES: biostudies-literature