Neural networks-based variationally enhanced sampling
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{
"updated": "2019-10-22T00:00:00+00:00",
"id": "231",
"metadata": {
"id": "231",
"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": "Yue-Yu",
"familyname": "Zhang",
"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": "230",
"doi": "10.24435/materialscloud:2019.0065/v1",
"references": [
{
"url": "",
"citation": "L. Bonati, Y.-Y. Zhang, M. Parrinello, Proceedings of the National Academy of Sciences, 116(36), 17641\u201317647 (2019)",
"comment": "",
"type": "Journal reference",
"doi": "10.1073/pnas.1907975116"
}
],
"title": "Neural networks-based variationally enhanced sampling",
"publication_date": "Oct 22, 2019, 00:00:00",
"description": "Sampling complex free-energy surfaces is one of the main challenges of modern atomistic simulation methods. The presence of kinetic bottlenecks in such surfaces often renders a direct approach useless. A popular strategy is to identify a small number of key collective variables and to introduce a bias potential that is able to favor their fluctuations in order to accelerate sampling. Here, we propose to use machine-learning techniques in conjunction with the recent variationally enhanced sampling method [O. Valsson, M. Parrinello, Phys. Rev. Lett. 113, 090601 (2014)] in order to determine such potential. This is achieved by expressing the bias as a neural network. The parameters are determined in a variational learning scheme aimed at minimizing an appropriate functional. This required the development of a more efficient minimization technique. The expressivity of neural networks allows representing rapidly varying free-energy surfaces, removes boundary effects artifacts, and allows several collective variables to be handled.",
"mcid": "2019.0065/v1",
"edited_by": 98,
"version": 1,
"is_last": true,
"owner": 41,
"license_addendum": "",
"keywords": [
"MARVEL",
"MARVEL/DD1",
"enhanced-sampling",
"deep-learning",
"PLUMED",
"biomolecules",
"silicon",
"crystallization",
"rare-events"
],
"_oai": {
"id": "oai:materialscloud.org:231"
},
"license": "Creative Commons Attribution 4.0 International"
},
"revision": 1,
"created": "2020-05-12T13:53:18.577849+00:00"
}