A unified picture of the covalent bond within quantum-accurate force fields: From organic molecules to metallic...

Computational studies of chemical processes taking place over extended size and time scales are inaccessible by electronic structure theories and can be tackled only by atomistic models such as force fields. These have evolved over the years to describe the most diverse systems. However, as we improve the performance of a force field for a particular physical/chemical situation, we are also moving away from a unified description. Here, we demonstrate that a unified picture of the covalent bond is achievable within the framework of machine learning-based force fields. Ridge regression, together with a representation of the atomic environment in terms of bispectrum components, can be used to map a general potential energy surface for molecular systems at chemical accuracy. This protocol sets the ground for the generation of an accurate and universal class of potentials for both organic and organometallic compounds with no specific assumptions on the chemistry involved.