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

Abstract

Machine learning models can accurately predict atomistic chemical properties but do not provide access to the molecular electronic structure. Here the authors use a deep learning approach to predict the quantum mechanical wavefunction at high efficiency from which other ground-state properties can be derived.