  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.
