Unifying machine learning and quantum chemistry -- a deep neural network for molecular wavefunctions
This work addresses a bottleneck in chemistry and materials science by providing a method to capture electronic degrees of freedom efficiently, potentially enhancing reactive chemistry and chemical analysis.
The authors tackled the problem of machine learning models lacking explicit electronic structure information by developing a deep learning framework to predict quantum mechanical wavefunctions, enabling inverse design of molecular structures for electronic property optimization.
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.