Physics-inspired equivariant descriptors of non-bonded interactions


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{
  "revision": 6, 
  "created": "2023-10-02T08:36:05.743665+00:00", 
  "updated": "2023-10-03T08:50:09.033577+00:00", 
  "id": "1924", 
  "metadata": {
    "owner": 1147, 
    "license_addendum": null, 
    "title": "Physics-inspired equivariant descriptors of non-bonded interactions", 
    "references": [
      {
        "citation": "Kevin K. Huguenin-Dumittan, Philip Loche, Ni Haoran, Michele Ceriotti, Arxiv (submitted 2023).", 
        "type": "Preprint", 
        "comment": "Preprint in which the data is discussed", 
        "url": "https://arxiv.org/abs/2308.13208"
      }, 
      {
        "citation": "Andrea Grisafi, Michele Ceriotti, J. Chem. Phys. 151, 204105 (2019)", 
        "type": "Journal reference", 
        "doi": "10.1063/1.5128375", 
        "comment": "Paper in which some of the datasets were originally introduced", 
        "url": "https://doi.org/10.1063/1.5128375"
      }
    ], 
    "mcid": "2023.151", 
    "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.", 
    "version": 1, 
    "id": "1924", 
    "publication_date": "Oct 03, 2023, 10:50:08", 
    "is_last": true, 
    "doi": "10.24435/materialscloud:23-99", 
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        "size": 69468453, 
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      {
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        "description": "Dataset containing structures and DFT energies & forces of Xenon dimers and trimers", 
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    "conceptrecid": "1923", 
    "edited_by": 576, 
    "status": "published", 
    "_oai": {
      "id": "oai:materialscloud.org:1924"
    }, 
    "license": "Creative Commons Attribution 4.0 International", 
    "contributors": [
      {
        "givennames": "Kevin K.", 
        "affiliations": [
          "Laboratory of Computational Science and Modelling (COSMO), \u00c9cole Polytechnique F\u00e9d\u00e9rale de Lausanne (EPFL), CH-1015 Lausanne, Vaud, Switzerland"
        ], 
        "familyname": "Huguenin-Dumittan"
      }, 
      {
        "givennames": "Philip", 
        "affiliations": [
          "Laboratory of Computational Science and Modelling (COSMO), \u00c9cole Polytechnique F\u00e9d\u00e9rale de Lausanne (EPFL), CH-1015 Lausanne, Vaud, Switzerland"
        ], 
        "familyname": "Loche"
      }, 
      {
        "givennames": "Haoran", 
        "affiliations": [
          "Laboratory of Computational Science and Modelling (COSMO), \u00c9cole Polytechnique F\u00e9d\u00e9rale de Lausanne (EPFL), CH-1015 Lausanne, Vaud, Switzerland"
        ], 
        "familyname": "Ni"
      }, 
      {
        "email": "michele.ceriotti@epfl.ch", 
        "givennames": "Michele", 
        "affiliations": [
          "Laboratory of Computational Science and Modelling (COSMO), \u00c9cole Polytechnique F\u00e9d\u00e9rale de Lausanne (EPFL), CH-1015 Lausanne, Vaud, Switzerland"
        ], 
        "familyname": "Ceriotti"
      }
    ], 
    "keywords": [
      "ERC", 
      "machine learning", 
      "long-range interactions", 
      "electrostatics", 
      "dispersion"
    ]
  }
}