Spectral operator representations

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{
  "id": "2312", 
  "updated": "2024-08-26T08:15:31.290416+00:00", 
  "metadata": {
    "version": 1, 
    "contributors": [
      {
        "givennames": "Austin", 
        "affiliations": [
          "Theory and Simulation of Materials (THEOS), \u00c9cole Polytechnique F\u00e9d\u00e9rale de Lausanne, CH-1015 Lausanne, Switzerland"
        ], 
        "email": "austin.zadoks@epfl.ch", 
        "familyname": "Zadoks"
      }, 
      {
        "givennames": "Antimo", 
        "affiliations": [
          "Scuola Internazionale Superiore di Studi Avanzati (SISSA), I-34136 Trieste, Italy", 
          "Dipartimento di Fisica, Universit\u00e0 di Trieste, I-34151 Trieste, Italy"
        ], 
        "email": "amarrazz@sissa.it", 
        "familyname": "Marrazzo"
      }, 
      {
        "givennames": "Nicola", 
        "affiliations": [
          "Theory and Simulation of Materials (THEOS), \u00c9cole Polytechnique F\u00e9d\u00e9rale de Lausanne, CH-1015 Lausanne, Switzerland", 
          "National Centre for Computational Design and Discovery of Novel Materials (MARVEL), \u00c9cole Polytechnique F\u00e9d\u00e9rale de Lausanne, CH-1015 Lausanne, Switzerland", 
          "Laboratory for Materials Simulations (LMS), Paul Scherrer Institut, CH-5232 Villigen, Switzerland"
        ], 
        "email": "nicola.marzari@epfl.ch", 
        "familyname": "Marzari"
      }
    ], 
    "title": "Spectral operator representations", 
    "_oai": {
      "id": "oai:materialscloud.org:2312"
    }, 
    "keywords": [
      "machine learning", 
      "electronic structure", 
      "electronic bands", 
      "EPFL", 
      "MARVEL"
    ], 
    "publication_date": "Aug 26, 2024, 10:15:31", 
    "_files": [
      {
        "key": "README.md", 
        "description": "Readme file", 
        "checksum": "md5:2f5c03ed52fa5e7639d2b84e937478c1", 
        "size": 446
      }, 
      {
        "key": "sorep_code_data.tar.gz", 
        "description": "Code and data archive", 
        "checksum": "md5:b66184de43c241f077820c9e2b93b629", 
        "size": 358930120
      }
    ], 
    "references": [
      {
        "doi": "10.48550/arXiv.2403.01514", 
        "citation": "A. Zadoks, A. Marrazzo, N. Marzari, arXiv 2403.01514 (2024)", 
        "url": "https://arxiv.org/abs/2403.01514", 
        "type": "Preprint"
      }
    ], 
    "description": "Materials are often represented in machine learning applications by (chemical-)geometric descriptions of their atomic structure. In this work, we propose an alternative framework for representing materials using descriptions of their electronic structure called Spectral Operator Representations (SOREPs). This record contains the code and data used to study carbon nanotubes (CNTs), barium titanate polymorphs, and the accelerated screening of transparent conducting materials with SOREPs. A data set for each application is provided: p<sub>z</sub> tight binding band structures for the three CNT configurations studied; the structures, band dispersions, and SOREP features of 127 BaTiO\u2083 polymorphs; and the SOREP features and ML targets for the MC3D materials considered in the accelerated screening. Additionally, code including patch files for Quantum ESPRESSO, the \"sorep\" python package, and the set of scripts used to prepare these data, train ML models, and plot results is provided.", 
    "status": "published", 
    "license": "Creative Commons Attribution 4.0 International", 
    "conceptrecid": "2311", 
    "is_last": true, 
    "mcid": "2024.128", 
    "edited_by": 576, 
    "id": "2312", 
    "owner": 1456, 
    "license_addendum": null, 
    "doi": "10.24435/materialscloud:vm-5n"
  }, 
  "revision": 9, 
  "created": "2024-08-19T14:12:58.678623+00:00"
}