  Fast and accurate simulation of complex chemical systems in environments such
as solutions is a long standing challenge in theoretical chemistry. In recent
years, machine learning has extended the boundaries of quantum chemistry by
providing highly accurate and efficient surrogate models of electronic
structure theory, which previously have been out of reach for conventional
approaches. Those models have long been restricted to closed molecular systems
without accounting for environmental influences, such as external electric and
magnetic fields or solvent effects. Here, we introduce the deep neural network
FieldSchNet for modeling the interaction of molecules with arbitrary external
fields. FieldSchNet offers access to a wealth of molecular response properties,
enabling it to simulate a wide range of molecular spectra, such as infrared,
Raman and nuclear magnetic resonance. Beyond that, it is able to describe
implicit and explicit molecular environments, operating as a polarizable
continuum model for solvation or in a quantum mechanics / molecular mechanics
setup. We employ FieldSchNet to study the influence of solvent effects on
molecular spectra and a Claisen rearrangement reaction. Based on these results,
we use FieldSchNet to design an external environment capable of lowering the
activation barrier of the rearrangement reaction significantly, demonstrating
promising venues for inverse chemical design.
