Sensitivity benchmarks of structural representations for atomic-scale machine learning
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{
"metadata": {
"is_last": true,
"version": 1,
"title": "Sensitivity benchmarks of structural representations for atomic-scale machine learning",
"keywords": [
"DFT",
"methane",
"sensitivity",
"representations",
"machine learning",
"MARVEL",
"SNSF",
"ERC"
],
"description": "This dataset contains three sets of CH4 geometries that are distorted along special directions, to reveal the sensitivity to atomic displacements of structural descriptors used in machine-learning applications. The structures are stored in a format that can be visualized on http://chemiscope.org, and contain also DFT-computed energies, as well as the sensitivity analysis of four different kinds of features.",
"license": "Creative Commons Attribution Non Commercial 4.0 International",
"references": [
{
"citation": "Sergey N. Pozdnyakov, Liwei Zhang, Christoph Ortner, Gabor Csanyi, and Michele Ceriotti, Open Research Europe (submission in preparation)",
"type": "Preprint"
}
],
"doi": "10.24435/materialscloud:7z-g6",
"conceptrecid": "1023",
"publication_date": "Sep 17, 2021, 13:48:21",
"edited_by": 100,
"_oai": {
"id": "oai:materialscloud.org:1024"
},
"contributors": [
{
"affiliations": [
"Laboratory of Computational Science and Modelling, Institute of Materials, Ecole Polytechnique Federale de Lausanne, Lausanne 1015, Switzerland"
],
"email": "sergey.pozdnyakov@epfl.ch",
"familyname": "Pozdnyakov",
"givennames": "Sergey"
},
{
"affiliations": [
"Laboratory of Computational Science and Modelling, Institute of Materials, Ecole Polytechnique Federale de Lausanne, Lausanne 1015, Switzerland"
],
"email": "michele.ceriotti@epfl.ch",
"familyname": "Ceriotti",
"givennames": "Michele"
}
],
"owner": 190,
"license_addendum": null,
"mcid": "2021.149",
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"description": "2d manifold around random asymmetric structure",
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"description": "readme",
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"id": "1024",
"status": "published"
},
"revision": 7,
"updated": "2021-12-06T12:36:19.575143+00:00",
"created": "2021-09-16T23:18:08.480668+00:00",
"id": "1024"
}