Unifying machine learning and quantum chemistry -- a deep neural network for molecular wavefunctions

TL;DR

This paper introduces a deep learning framework that predicts molecular wavefunctions directly, enabling efficient access to electronic structure information and facilitating inverse molecular design.

Contribution

It presents a novel neural network model that explicitly predicts quantum wavefunctions, bridging machine learning and quantum chemistry for the first time.

Findings

Accurately predicts molecular wavefunctions from atomic orbitals.

Enables derivation of all ground-state properties efficiently.

Facilitates inverse design of molecules with target electronic properties.

Abstract

Machine learning advances chemistry and materials science by enabling large-scale exploration of chemical space based on quantum chemical calculations. While these models supply fast and accurate predictions of atomistic chemical properties, they do not explicitly capture the electronic degrees of freedom of a molecule, which limits their applicability for reactive chemistry and chemical analysis. Here we present a deep learning framework for the prediction of the quantum mechanical wavefunction in a local basis of atomic orbitals from which all other ground-state properties can be derived. This approach retains full access to the electronic structure via the wavefunction at force field-like efficiency and captures quantum mechanics in an analytically differentiable representation. On several examples, we demonstrate that this opens promising avenues to perform inverse design of molecular structures for target electronic property optimisation and a clear path towards increased synergy of machine learning and quantum chemistry.