<?xml version='1.0' encoding='utf-8'?> <oai_dc:dc xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/ http://www.openarchives.org/OAI/2.0/oai_dc.xsd"> <dc:creator>Huguenin-Dumittan, Kevin K.</dc:creator> <dc:creator>Loche, Philip</dc:creator> <dc:creator>Ni, Haoran</dc:creator> <dc:creator>Ceriotti, Michele</dc:creator> <dc:date>2023-10-03</dc:date> <dc:description>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.</dc:description> <dc:identifier>https://archive.materialscloud.org/record/2023.151</dc:identifier> <dc:identifier>doi:10.24435/materialscloud:23-99</dc:identifier> <dc:identifier>mcid:2023.151</dc:identifier> <dc:identifier>oai:materialscloud.org:1924</dc:identifier> <dc:language>en</dc:language> <dc:publisher>Materials Cloud</dc:publisher> <dc:rights>info:eu-repo/semantics/openAccess</dc:rights> <dc:rights>Creative Commons Attribution 4.0 International https://creativecommons.org/licenses/by/4.0/legalcode</dc:rights> <dc:subject>ERC</dc:subject> <dc:subject>machine learning</dc:subject> <dc:subject>long-range interactions</dc:subject> <dc:subject>electrostatics</dc:subject> <dc:subject>dispersion</dc:subject> <dc:title>Physics-inspired equivariant descriptors of non-bonded interactions</dc:title> <dc:type>Dataset</dc:type> </oai_dc:dc>