Physics-Inspired Equivariant Descriptors of Nonbonded Interactions

One essential ingredient in many machine learning (ML)
based methods
for atomistic modeling of materials and molecules is the use of locality.
While allowing better system-size scaling, this systematically neglects
long-range (LR) effects such as electrostatic or dispersion interactions.
We present an extension of the long distance equivariant (LODE) framework
that can handle diverse LR interactions in a consistent way and seamlessly
integrates with preexisting methods by building new sets of atom centered
features. We provide a direct physical interpretation of these using
the multipole expansion, which allows for simpler and more efficient
implementations. The framework is applied to simple toy systems as
proof of concept and a heterogeneous set of molecular dimers to push
the method to its limits. By generalizing LODE to arbitrary asymptotic
behaviors, we provide a coherent approach to treat arbitrary two-
and many-body nonbonded interactions in the data-driven modeling of
matter.