Machine learning of solvent effects on molecular spectra and reactions

TL;DR

This paper introduces FieldSchNet, a deep neural network model that accurately simulates solvent effects on molecular spectra and reactions, enabling advanced studies and design of chemical processes in complex environments.

Contribution

The paper presents FieldSchNet, a novel neural network model that incorporates environmental effects, allowing for accurate simulation of solvent influences on molecular properties and reactions.

Findings

FieldSchNet accurately models solvent effects on spectra.

It can simulate a wide range of molecular response properties.

It enables design of environments to modify reaction barriers.

Abstract

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.