Robustness of local predictions in atomistic machine learning models
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{
"revision": 9,
"id": "1805",
"created": "2023-06-21T14:52:35.877563+00:00",
"metadata": {
"doi": "10.24435/materialscloud:re-0d",
"status": "published",
"title": "Robustness of local predictions in atomistic machine learning models",
"mcid": "2023.102",
"license_addendum": null,
"_files": [
{
"description": "tar file containing datasets and notebooks",
"key": "LPR_supp_notebook_dataset.tar.gz",
"size": 13489463,
"checksum": "md5:68664f36b80f27317378e76fa62a6406"
},
{
"description": "README file containing instructions to run the notebooks correctly",
"key": "README.txt",
"size": 1856,
"checksum": "md5:a0331a12aa07fc5269de7ccb738c8a13"
}
],
"owner": 1063,
"_oai": {
"id": "oai:materialscloud.org:1805"
},
"keywords": [
"Marie Curie Fellowship",
"ERC",
"EPSRC"
],
"conceptrecid": "1804",
"is_last": true,
"references": [
{
"type": "Preprint",
"doi": "10.48550/arXiv.2306.15638",
"url": "https://arxiv.org/abs/2306.15638",
"comment": "Preprint where the data and workflow are discussed",
"citation": "S. Chong, F. Grasselli, C. Ben Mahmoud, J. D. Morrow, V. L. Deringer, M. Ceriotti, arXiv:2306.15638"
}
],
"publication_date": "Jun 30, 2023, 16:39:41",
"license": "Creative Commons Attribution 4.0 International",
"id": "1805",
"description": "Machine learning (ML) models for molecules and materials commonly rely on a decomposition of the global target quantity into local, atom-centered contributions. This approach is convenient from a computational perspective, enabling large-scale ML-driven simulations with a linear-scaling cost, and can also be used to deduce useful structure--property relations as they associate simple atomic motifs with complicated macroscopic properties. However, even though there exist practical justifications for these decompositions, only the global quantity is rigorously defined, and thus it is unclear to what extent the atomistic terms predicted by the model can be trusted. Here, we introduce a quantitative metric, which we call the local prediction rigidity (LPR), to assess how robust the locally decomposed predictions of ML models are. We investigate the dependence of LPR on the details of model training, e.g. composition of the dataset, for several different problems ranging from simple toy models to real chemical systems. We present strategies to systematically enhance the LPR, which can be used to improve the robustness, interpretability, and transferability of the resulting atomistic ML models. \nThis repository contains datasets and Jupyter notebooks employed to corroborate the results of the above study.",
"version": 1,
"contributors": [
{
"email": "sanggyu.chong@epfl.ch",
"affiliations": [
"Laboratory of Computational Science and Modeling (COSMO), IMX, \u00c9cole Polytechnique F\u00e9d\u00e9rale de Lausanne (EPFL), 1015 Lausanne, Switzerland"
],
"familyname": "Chong",
"givennames": "Sanggyu"
},
{
"affiliations": [
"Laboratory of Computational Science and Modeling (COSMO), IMX, \u00c9cole Polytechnique F\u00e9d\u00e9rale de Lausanne (EPFL), 1015 Lausanne, Switzerland"
],
"familyname": "Grasselli",
"givennames": "Federico"
},
{
"affiliations": [
"Laboratory of Computational Science and Modeling (COSMO), IMX, \u00c9cole Polytechnique F\u00e9d\u00e9rale de Lausanne (EPFL), 1015 Lausanne, Switzerland"
],
"familyname": "Ben Mahmoud",
"givennames": "Chiheb"
},
{
"affiliations": [
"Department of Chemistry, Inorganic Chemistry Laboratory, University of Oxford, Oxford OX1 3QR, United Kingdom"
],
"familyname": "Morrow",
"givennames": "Joe"
},
{
"affiliations": [
"Department of Chemistry, Inorganic Chemistry Laboratory, University of Oxford, Oxford OX1 3QR, United Kingdom"
],
"familyname": "Deringer",
"givennames": "Volker"
},
{
"affiliations": [
"Laboratory of Computational Science and Modeling (COSMO), IMX, \u00c9cole Polytechnique F\u00e9d\u00e9rale de Lausanne (EPFL), 1015 Lausanne, Switzerland"
],
"familyname": "Ceriotti",
"givennames": "Michele"
}
],
"edited_by": 576
},
"updated": "2023-06-30T14:39:41.995631+00:00"
}