Prediction rigidities for data-driven chemistry

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{
  "id": "2318", 
  "updated": "2024-08-28T13:05:10.910747+00:00", 
  "metadata": {
    "version": 1, 
    "contributors": [
      {
        "givennames": "Sanggyu", 
        "affiliations": [
          "Laboratory of Computational Science and Modeling (COSMO), IMX, \u00c9cole Polytechnique F\u00e9d\u00e9rale de Lausanne (EPFL), 1015 Lausanne, Switzerland"
        ], 
        "email": "sanggyu.chong@epfl.ch", 
        "familyname": "Chong"
      }, 
      {
        "givennames": "Filippo", 
        "affiliations": [
          "Laboratory of Computational Science and Modeling (COSMO), IMX, \u00c9cole Polytechnique F\u00e9d\u00e9rale de Lausanne (EPFL), 1015 Lausanne, Switzerland"
        ], 
        "email": "filippo.bigi@epfl.ch", 
        "familyname": "Bigi"
      }, 
      {
        "givennames": "Federico", 
        "affiliations": [
          "Laboratory of Computational Science and Modeling (COSMO), IMX, \u00c9cole Polytechnique F\u00e9d\u00e9rale de Lausanne (EPFL), 1015 Lausanne, Switzerland"
        ], 
        "email": "federico.grasselli@epfl.ch", 
        "familyname": "Grasselli"
      }, 
      {
        "givennames": "Philip", 
        "affiliations": [
          "Laboratory of Computational Science and Modeling (COSMO), IMX, \u00c9cole Polytechnique F\u00e9d\u00e9rale de Lausanne (EPFL), 1015 Lausanne, Switzerland"
        ], 
        "email": "philip.loche@epfl.ch", 
        "familyname": "Loche"
      }, 
      {
        "givennames": "Matthias", 
        "affiliations": [
          "Laboratory of Computational Science and Modeling (COSMO), IMX, \u00c9cole Polytechnique F\u00e9d\u00e9rale de Lausanne (EPFL), 1015 Lausanne, Switzerland"
        ], 
        "email": "matthias.kellner@epfl.ch", 
        "familyname": "Kellner"
      }, 
      {
        "givennames": "Michele", 
        "affiliations": [
          "Laboratory of Computational Science and Modeling (COSMO), IMX, \u00c9cole Polytechnique F\u00e9d\u00e9rale de Lausanne (EPFL), 1015 Lausanne, Switzerland"
        ], 
        "email": "michele.ceriotti@epfl.ch", 
        "familyname": "Ceriotti"
      }
    ], 
    "title": "Prediction rigidities for data-driven chemistry", 
    "_oai": {
      "id": "oai:materialscloud.org:2318"
    }, 
    "keywords": [
      "machine learning", 
      "prediction rigidity", 
      "uncertainty quantification", 
      "ML model robustness"
    ], 
    "publication_date": "Aug 28, 2024, 15:05:10", 
    "_files": [
      {
        "key": "PR_FD_materials_cloud.tar.gz", 
        "description": "compressed file containing the reference structures and their associated data, as well as trained NN models", 
        "checksum": "md5:4e5ab9528477908af3c68637ef0d1154", 
        "size": 4865433638
      }, 
      {
        "key": "README.txt", 
        "description": "README file with details about the data, how it has been organized, and where additional information about how to access the trained models can be found", 
        "checksum": "md5:5952c12112a7efa01dc6b6bf2bde36c6", 
        "size": 2008
      }
    ], 
    "references": [
      {
        "comment": "Paper in which the data and models were used for analysis", 
        "doi": "10.1039/D4FD00101J", 
        "citation": "S. Chong, F. Bigi, F. Grasselli, P. Loche, M. Kellner, M. Ceriotti, Faraday Discussions (2024)", 
        "type": "Journal reference"
      }
    ], 
    "description": "The widespread application of machine learning (ML) to the chemical sciences is making it very important to understand how the ML models learn to correlate chemical structures with their properties, and what can be done to improve the training efficiency whilst guaranteeing interpretability and transferability. In this work, we demonstrate the wide utility of prediction rigidities, a family of metrics derived from the loss function, in understanding the robustness of ML model predictions. We show that the prediction rigidities allow the assessment of the model not only at the global level, but also on the local or the component-wise level at which the intermediate (e.g. atomic, body-ordered, or range-separated) predictions are made. We leverage these metrics to understand the learning behavior of different ML models, and to guide efficient dataset construction for model training. We finally implement the formalism for a ML model targeting a coarse-grained system to demonstrate the applicability of the prediction rigidities to an even broader class of atomistic modeling problems.\nThis record contains all the data used for the analyses conducted in the associated work published in Faraday Discussions.", 
    "status": "published", 
    "license": "Creative Commons Attribution 4.0 International", 
    "conceptrecid": "2317", 
    "is_last": true, 
    "mcid": "2024.130", 
    "edited_by": 576, 
    "id": "2318", 
    "owner": 1063, 
    "license_addendum": null, 
    "doi": "10.24435/materialscloud:6x-gs"
  }, 
  "revision": 5, 
  "created": "2024-08-27T15:16:09.670407+00:00"
}