Training sets based on uncertainty estimates in the cluster-expansion method


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{
  "metadata": {
    "is_last": true, 
    "publication_date": "Feb 03, 2022, 17:42:15", 
    "edited_by": 576, 
    "version": 1, 
    "license": "Creative Commons Attribution 4.0 International", 
    "license_addendum": null, 
    "_files": [
      {
        "checksum": "md5:b894ad3a6916a4f3ca6b2da0d4b5fd49", 
        "key": "buss.db", 
        "size": 995328, 
        "description": "ASE database containing the training structures generated using BUSS scheme"
      }, 
      {
        "checksum": "md5:4bbbc675bdfcb53813ca300e9a68da2a", 
        "key": "rgs.db", 
        "size": 557056, 
        "description": "ASE database containing the training structures generated using RGS scheme"
      }
    ], 
    "mcid": "2022.21", 
    "keywords": [
      "BIG-MAP", 
      "cluster expansion", 
      "Monte Carlo", 
      "phase transition", 
      "bootstrapping", 
      "machine learning", 
      "energy materials"
    ], 
    "contributors": [
      {
        "givennames": "David", 
        "email": "david.kleiven@ntnu.no", 
        "familyname": "Kleiven", 
        "affiliations": [
          "Department of Physics, Norwegian University of Science and Technology, NO-7491 Trondheim, Norway"
        ]
      }, 
      {
        "givennames": "Jaakko", 
        "familyname": "Akola", 
        "affiliations": [
          "Department of Physics, Norwegian University of Science and Technology, NO-7491 Trondheim, Norway", 
          "Computational Physics Laboratory, Tampere University, P.O. Box 692, FI-33014 Tampere, Finland"
        ]
      }, 
      {
        "givennames": "Andrew", 
        "familyname": "Peterson", 
        "affiliations": [
          "School of Engineering, Brown University, Providence, RI 02912, United States of America", 
          "Department of Energy Conversion and Storage, Technical University of Denmark, DK-2800 Kgs. Lyngby, Denmark"
        ]
      }, 
      {
        "givennames": "Tejs", 
        "familyname": "Vegge", 
        "affiliations": [
          "Department of Energy Conversion and Storage, Technical University of Denmark, DK-2800 Kgs. Lyngby, Denmark"
        ]
      }, 
      {
        "givennames": "Jin Hyun", 
        "email": "jchang@dtu.dk", 
        "familyname": "Chang", 
        "affiliations": [
          "Department of Energy Conversion and Storage, Technical University of Denmark, DK-2800 Kgs. Lyngby, Denmark"
        ]
      }
    ], 
    "status": "published", 
    "doi": "10.24435/materialscloud:ha-ca", 
    "title": "Training sets based on uncertainty estimates in the cluster-expansion method", 
    "id": "1240", 
    "description": "Cluster expansion (CE) has gained an increasing level of popularity in recent years, and many strategies have been proposed for training and fitting the CE models to first-principles calculation results. The paper reports a new strategy for constructing a training set based on their relevance in Monte Carlo sampling for statistical analysis and reduction of the expected error. We call the new strategy a \"bootstrapping uncertainty structure selection\" (BUSS) scheme and compared its performance against a popular scheme where one uses a combination of random structure and ground-state search (referred to as RGS). The provided dataset contains the training sets generated using BUSS and RGS for constructing a CE model for disordered Cu2ZnSnS4 material. The files are in the format of the Atomic Simulation Environment (ASE) database (please refer to ASE documentation for more information https://wiki.fysik.dtu.dk/ase/index.html). Each `.db` file contains 100 DFT calculations, which were generated using iteration cycles. Each iteration cycle is referred to as a generation (marked with `gen` key in the database) and each database contains 10 generations where each generation consists of 10 training structures. See more details in the paper.", 
    "owner": 665, 
    "_oai": {
      "id": "oai:materialscloud.org:1240"
    }, 
    "conceptrecid": "1239", 
    "references": [
      {
        "doi": "10.1088/2515-7655/abf9ef", 
        "url": "https://iopscience.iop.org/article/10.1088/2515-7655/abf9ef/meta", 
        "comment": "Paper in which the method is described", 
        "citation": "D. Kleiven, J. Akola, A. Peterson, T. Vegge, J.H. Chang,  J. Phys. Energy 3 034012 (2021)", 
        "type": "Journal reference"
      }
    ]
  }, 
  "updated": "2022-02-03T16:42:15.908447+00:00", 
  "revision": 4, 
  "id": "1240", 
  "created": "2022-02-01T11:34:43.314804+00:00"
}