Data-Driven Collective Variables for Enhanced Sampling
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{
"updated": "2020-04-06T00:00:00+00:00",
"id": "359",
"metadata": {
"id": "359",
"status": "published",
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"contributors": [
{
"givennames": "Luigi",
"familyname": "Bonati",
"affiliations": [
"Department of Physics, ETH Zurich, 8092 Zurich, Switzerland and Facolt\u00e0 di Informatica, Instituto di Scienze Computazionali, Universit\u00e0 della Svizzera italiana, 6900 Lugano, Switzerland"
],
"email": "luigi.bonati@phys.chem.ethz.ch"
},
{
"givennames": "Valerio",
"familyname": "Rizzi",
"affiliations": [
"Department of Chemistry and Applied Biosciences, ETH Zurich, 8092 Zurich, Switzerland and Facolt\u00e0 di Informatica, Instituto di Scienze Computazionali, Universit\u00e0 della Svizzera italiana (USI), 6900 Lugano, Switzerland"
]
},
{
"givennames": "Michele",
"familyname": "Parrinello",
"affiliations": [
"Department of Chemistry and Applied Biosciences, ETH Zurich, 8092 Zurich, Switzerland and Facolt\u00e0 di Informatica, Instituto di Scienze Computazionali, Universit\u00e0 della Svizzera italiana (USI), 6900 Lugano, Switzerland"
]
}
],
"conceptrecid": "358",
"doi": "10.24435/materialscloud:2020.0035/v1",
"references": [
{
"url": "",
"citation": "L. Bonati, V. Rizzi, M. Parrinello, J. Phys. Chem. Lett., 11, 2998-3004 (2020)",
"comment": "Paper in which the method is described",
"type": "Journal reference",
"doi": "10.1021/acs.jpclett.0c00535"
},
{
"url": "https://github.com/luigibonati/data-driven-CVs",
"citation": "Github repository",
"comment": "The latest release of the code",
"type": "Software",
"doi": ""
},
{
"url": "https://colab.research.google.com/drive/1dG0ohT75R-UZAFMf_cbYPNQwBaOsVaAA",
"citation": "Google Colab notebook",
"comment": "Tutorial for the training of the Deep-LDA CV ",
"type": "Software",
"doi": ""
},
{
"url": "https://arxiv.org/abs/2002.06562",
"citation": "L. Bonati, V. Rizzi, M. Parrinello, arXiv:2002.06562",
"comment": "Open-access preprint of the method",
"type": "Preprint",
"doi": ""
}
],
"title": "Data-Driven Collective Variables for Enhanced Sampling",
"publication_date": "Apr 06, 2020, 00:00:00",
"description": "Designing an appropriate set of collective variables is crucial to the success of several enhanced sampling methods. Here we focus on how to obtain such variables from information limited to the metastable states. We characterize these states by a large set of descriptors and employ neural networks to compress this information in a lower-dimensional space, using Fisher's linear discriminant as an objective function to maximize the discriminative power of the network. We test this method on alanine dipeptide, using the non-linearly separable dataset composed by atomic distances. We then study an intermolecular aldol reaction characterized by a concerted mechanism. The resulting variables are able to promote sampling by drawing non-linear paths in the physical space connecting the fluctuations between metastable basins. Lastly, we interpret the behavior of the neural network by studying its relation to the physical variables. Through the identification of its most relevant features, we are able to gain chemical insight into the process.",
"mcid": "2020.0035/v1",
"edited_by": 98,
"version": 1,
"is_last": true,
"owner": 41,
"license_addendum": "",
"keywords": [
"ERC",
"MARVEL/DD1",
"ENHANCED SAMPLING",
"COLLECTIVE VARIABLES",
"PLUMED",
"OPES",
"METADYNAMICS",
"NEURAL NETWORKS"
],
"_oai": {
"id": "oai:materialscloud.org:359"
},
"license": "Creative Commons Attribution 4.0 International"
},
"revision": 1,
"created": "2020-05-12T13:53:53.226416+00:00"
}