Publication date: Oct 03, 2023
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.
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|Dataset containing the structures and energies of "NaCl" atoms interacting via a pure Coulomb potential
|Dataset containing the structures and energies of atoms interacting via a pure dispersion (1/r^6) potential
|Dataset containing structures and DFT energies & forces of dimers taken from sidechain-sidechain fragments in biomolecules
|The structures and DFT energies & forces of the individual monomers contained in the "bio_dimers.xyz" dataset
|The FHI-AIMS input file to compute the energies of the "bio_dimers.xyz" and "bio_dimers_monomers.xyz" structures
|Dataset containing structures and DFT energies & forces of Xenon dimers and trimers
|The FHI-AIMS input file to compute the energies and forces of the structures in "xenon.xyz"