Structure-property maps with kernel principal covariates regression
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{
"metadata": {
"edited_by": 100,
"owner": 150,
"description": "Data analyses based on linear methods constitute the simplest, most robust, and transparent approaches to the automatic processing of large amounts of data for building supervised or unsupervised machine learning models. Principal covariates regression (PCovR) is an underappreciated method that interpolates between principal component analysis and linear regression, and can be used to conveniently reveal structure-property relations in terms of simple-to-interpret, low-dimensional maps. Here we introduce a kernelized version of PCovR and a sparsified extension, and demonstrate the performance of this approach in revealing and predicting structure-property relations in chemistry and materials science, showing a variety of examples including elemental carbon, porous silicate frameworks, organic molecules, amino acid conformers, and molecular materials.",
"keywords": [
"machine learning",
"materials science",
"dimensionality reduction",
"kernel methods",
"MaX",
"SNSF",
"ERC",
"EPFL",
"MARVEL/DD1"
],
"is_last": true,
"title": "Structure-property maps with kernel principal covariates regression",
"status": "published",
"license_addendum": null,
"doi": "10.24435/materialscloud:9e-3j",
"conceptrecid": "457",
"_files": [
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"checksum": "md5:433087121bd75a693da1c51bdd91a519",
"description": "Map created with KPCovR for global properties of DEEM zeolites at alpha=0.5 using the chemiscope.org visualizer JSON format",
"key": "DEEM-global-kpcovr-0.5-chemiscope.json.gz",
"size": 3159070
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"checksum": "md5:196238f8be2815f22257fe791eaa2199",
"description": "Map created with KPCovR for local properties of DEEM zeolites at alpha=0.5 using the chemiscope.org visualizer JSON format",
"key": "DEEM-local-kpcovr-0.5-chemiscope.json.gz",
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"description": "Map created with KPCovR for the QM9 dataset at alpha=0.5 using the chemiscope.org visualizer JSON format",
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"size": 3418447
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"description": "Map created with KPCovR for the QM9 dataset at alpha=1.0 using the chemiscope.org visualizer JSON format",
"key": "qm9-12PC-kpcovr-1.0-chemiscope.json.gz",
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],
"references": [
{
"citation": "B. A. Helfrecht, R. K. Cersonsky, G. Fraux, M. Ceriotti, arXiv:2002.05076 (2020)",
"url": "https://arxiv.org/abs/2002.05076",
"type": "Preprint"
}
],
"contributors": [
{
"givennames": "Benjamin A.",
"affiliations": [
"Laboratory of Computational Science and Modeling, IMX, \u00c9cole Polytechnique F\u00e9d\u00e9rale de Lausanne, 1015 Lausanne, Switzerland"
],
"familyname": "Helfrecht",
"email": "benjamin.helfrecht@epfl.ch"
},
{
"givennames": "Rose K.",
"affiliations": [
"Laboratory of Computational Science and Modeling, IMX, \u00c9cole Polytechnique F\u00e9d\u00e9rale de Lausanne, 1015 Lausanne, Switzerland"
],
"familyname": "Cersonsky",
"email": "rose.cersonsky@epfl.ch"
},
{
"givennames": "Guillaume",
"affiliations": [
"Laboratory of Computational Science and Modeling, IMX, \u00c9cole Polytechnique F\u00e9d\u00e9rale de Lausanne, 1015 Lausanne, Switzerland"
],
"familyname": "Fraux",
"email": "guillaume.fraux@epfl.ch"
},
{
"givennames": "Michele",
"affiliations": [
"Laboratory of Computational Science and Modeling, IMX, \u00c9cole Polytechnique F\u00e9d\u00e9rale de Lausanne, 1015 Lausanne, Switzerland"
],
"familyname": "Ceriotti",
"email": "michele.ceriotti@epfl.ch"
}
],
"_oai": {
"id": "oai:materialscloud.org:1185"
},
"publication_date": "Dec 20, 2021, 10:08:22",
"mcid": "2021.225",
"version": 2,
"id": "1185",
"license": "Creative Commons Attribution 4.0 International"
},
"revision": 2,
"created": "2021-12-17T11:10:59.044564+00:00",
"id": "1185",
"updated": "2021-12-20T09:08:23.032840+00:00"
}