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dc.contributor.author
Bonati, Luigi
dc.date.accessioned
2021-10-27T17:32:55Z
dc.date.available
2021-10-18T03:58:33Z
dc.date.available
2021-10-27T17:32:55Z
dc.date.issued
2021-07
dc.identifier.issn
1124-1896
dc.identifier.issn
1826-9885
dc.identifier.issn
2037-4909
dc.identifier.other
10.1393/ncc/i2021-21125-3
en_US
dc.identifier.uri
http://hdl.handle.net/20.500.11850/510215
dc.identifier.doi
10.3929/ethz-b-000510215
dc.description.abstract
A popular way to accelerate the sampling of rare events in molecular dynamics simulations is to introduce a potential that increases the fluctuations of selected collective variables. For this strategy to be successful, it is critical to choose appropriate variables. Here we review some recent developments in the data-driven design of collective variables, which combine Fisher's discriminant analysis and neural networks. This approach allows to compress the fluctuations of metastable states into a low-dimensional representation. We illustrate through different applications the effectiveness of this method in accelerating the sampling, while also identifying the physical descriptors that undergo the most significant changes in the process.
en_US
dc.format
application/pdf
en_US
dc.language.iso
en
en_US
dc.publisher
Societa Italiana di Fisica
en_US
dc.rights.uri
http://creativecommons.org/licenses/by/4.0/
dc.title
Training collective variables for enhanced sampling via neural networks based discriminant analysis
en_US
dc.type
Journal Article
dc.rights.license
Creative Commons Attribution 4.0 International
dc.date.published
2021-08-19
ethz.journal.title
Il Nuovo Cimento C
ethz.journal.volume
44
en_US
ethz.journal.issue
4-5
en_US
ethz.journal.abbreviated
NCC
ethz.pages.start
125
en_US
ethz.size
4 p.
en_US
ethz.version.deposit
publishedVersion
en_US
ethz.identifier.wos
ethz.identifier.scopus
ethz.publication.place
Bologna
en_US
ethz.publication.status
published
en_US
ethz.date.deposited
2021-10-18T03:58:42Z
ethz.source
SCOPUS
ethz.eth
yes
en_US
ethz.availability
Open access
en_US
ethz.rosetta.installDate
2021-10-27T17:33:03Z
ethz.rosetta.lastUpdated
2022-03-29T14:58:31Z
ethz.rosetta.versionExported
true
ethz.COinS
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