Multi-scale approach for the prediction of atomic scale properties


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{
  "id": "699", 
  "metadata": {
    "title": "Multi-scale approach for the prediction of atomic scale properties", 
    "doi": "10.24435/materialscloud:tr-t9", 
    "license": "GNU General Public License v2.0 or later", 
    "keywords": [
      "multi-scale machine learning", 
      "long-range interactions", 
      "LODE", 
      "EPFL", 
      "ERC", 
      "MARVEL", 
      "CSCS"
    ], 
    "contributors": [
      {
        "affiliations": [
          "Laboratory of Computational Science and Modeling, IMX, \u00c9cole Polytechnique F\u00e9d\u00e9rale de Lausanne, 1015 Lausanne, Switzerland."
        ], 
        "familyname": "Grisafi", 
        "givennames": "Andrea"
      }, 
      {
        "affiliations": [
          "Laboratory of Computational Science and Modeling, IMX, \u00c9cole Polytechnique F\u00e9d\u00e9rale de Lausanne, 1015 Lausanne, Switzerland.", 
          "National Centre for Computational Design and Discovery of Novel Materials (MARVEL), \u00c9cole Polytechnique F\u00e9d\u00e9rale de Lausanne, 1015 Lausanne, Switzerland"
        ], 
        "familyname": "Nigam", 
        "givennames": "Jigyasa"
      }, 
      {
        "affiliations": [
          "Laboratory of Computational Science and Modeling, IMX, \u00c9cole Polytechnique F\u00e9d\u00e9rale de Lausanne, 1015 Lausanne, Switzerland.", 
          "National Centre for Computational Design and Discovery of Novel Materials (MARVEL), \u00c9cole Polytechnique F\u00e9d\u00e9rale de Lausanne, 1015 Lausanne, Switzerland"
        ], 
        "familyname": "Ceriotti", 
        "email": "michele.ceriotti@epfl.ch", 
        "givennames": "Michele"
      }
    ], 
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      {
        "description": "README file.", 
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    ], 
    "references": [
      {
        "type": "Journal reference", 
        "doi": "https://doi.org/10.1039/D0SC04934D", 
        "citation": "A. Grisafi, J. Nigam, M. Ceriotti, Chemical Science, accepted (2020)", 
        "comment": "Paper in which the method is described", 
        "url": "https://pubs.rsc.org/en/content/articlelanding/2021/SC/D0SC04934D#!divAbstract"
      }, 
      {
        "type": "Preprint", 
        "doi": "", 
        "citation": "A. Grisafi, J. Nigam, M. Ceriotti, arXiv:2008.12122 (2020)", 
        "comment": "Preprint in which the method is described", 
        "url": "https://arxiv.org/abs/2008.12122"
      }
    ], 
    "conceptrecid": "698", 
    "version": 1, 
    "edited_by": 100, 
    "id": "699", 
    "owner": 23, 
    "mcid": "2021.3", 
    "is_last": true, 
    "status": "published", 
    "description": "Electronic nearsightedness is one of the fundamental principles that governs the behavior of condensed matter and supports its description in terms of local entities such as chemical bonds. \nLocality also underlies the tremendous success of machine-learning schemes that predict quantum mechanical observables -- such as the cohesive energy, the electron density, or a variety of response properties -- as a sum of atom-centred contributions, based on a short-range representation of atomic environments.\nOne of the main shortcomings of these approaches is their inability to capture physical effects, ranging from electrostatic interactions to quantum delocalization, which have a long-range nature. \nHere we show how to build a multi-scale scheme that combines in the same framework local and non-local information, overcoming such limitations. We show that the simplest version of such features can be put in formal correspondence with a multipole expansion of permanent electrostatics. The data-driven nature of the model construction, however, makes this simple form suitable to tackle also different types of delocalized and collective effects. We present several examples that range from molecular physics to surface science and biophysics, demonstrating the ability of this multi-scale approach to model interactions driven by electrostatics, polarization and dispersion, as well as the cooperative behavior of dielectric response functions.", 
    "license_addendum": null, 
    "_oai": {
      "id": "oai:materialscloud.org:699"
    }, 
    "publication_date": "Jan 07, 2021, 13:37:10"
  }, 
  "revision": 16, 
  "updated": "2021-01-07T12:37:10.472278+00:00", 
  "created": "2021-01-05T10:24:02.198333+00:00"
}