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 electrostatics or dispersion interaction. 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 non-bonded interactions in the data-driven modeling of matter.

Scripts and example datasets that can be used to reproduce the results in the referenced preprint (Kevin K. Huguenin-Dumittan, Philip Loche, Ni Haoran, Michele Ceriotti, Arxiv (submitted 2023)).

{"references": ["Kevin K. Huguenin-Dumittan, Philip Loche, Ni Haoran, Michele Ceriotti, Arxiv (2023)"]}