Escaping free-energy minima
Open accessAbstract
We introduce a powerful method for exploring the properties of the multidimensional free energy surfaces (FESs) of complex many-body systems by means of coarse-grained non-Markovian dynamics in the space defined by a few collective coordinates. A characteristic feature of these dynamics is the presence of a history-dependent potential term that, in time, fills the minima in the FES, allowing the efficient exploration and accurate determination of the FES as a function of the collective coordinates. We demonstrate the usefulness of this approach in the case of the dissociation of a NaCl molecule in water and in the study of the conformational changes of a dialanine in solution.
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The problem of obtaining a complete yet concise description of a molecular or condensed-phase structure is central to many modelling techniques.
“The problem of obtaining a complete yet concise description of a molecular or condensed-phase structure is central to many modelling techniques, including the sampling of rare events 15 and the search for stable structures 2,16,17 .”
From Unsupervised machine learning in atomistic simulations, between predictions and understanding (2019) · cites this paper as [15]
In order to observe a rare (activated) event without developing dedicated hardware, one needs to selectively increase the speed at which the pathways related to the transition are traversed, to modify the probability distribution that is sampled during the simulation so that configurations close to the transition state occur more often, or to generate adaptively ensembles of trajectories that覆盖the
“In order to observe a rare (activated) event without developing dedicated hardware 1 one needs to selectively increase the speed at which the pathways related to the transition are traversed 51,87,88 , to modify the probability distribution that is sampled during the simulation so that configurations that are close to the transition state occur more often 15,89 , or to generate adaptively ens”
From Unsupervised machine learning in atomistic simulations, between predictions and understanding (2019) · cites this paper as [15]
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Influential
- Authors
- Laio, A., & Parrinello, M.
- Affiliations
- ETH Zurich (CH)
- Venue
- Proceedings of the National Academy of Sciences
- Publisher
- National Academy of Sciences
- Published
- 2002-09-23
- Volume
- 99
- Issue
- 20
- Pages
- 12562-12566
- ISSN
- 0027-8424 (ISSN-L)
- 1091-6490
- PMC
- PMC130499
- OpenAlex
- W2149655632
- Cited by
- 5,626
- References
- 21
- Citations by year
- 2026: 305 · 2025: 382 · 2024: 338 · 2023: 399 · 2022: 359 · 2021: 367 · 2020: 385 · 2019: 330 · 2018: 358 · 2017: 335 · 2016: 323 · 2015: 292 · 2014: 267 · 2013: 211 · 2012: 202
- FWCI
- 18.71
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Proceedings of the National Academy of Sciences (publishedVersion)
- Europe PMC (PubMed Central) (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- SDL (submittedVersion)
- arXiv (Cornell University)
- Topics
- Physical Sciences › Chemistry › Physical and Theoretical Chemistry › Advanced Physical and Chemical Molecular Interactions
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
- Physical Sciences › Physics and Astronomy › Atomic and Molecular Physics, and Optics › Advanced Chemical Physics Studies
- Keywords
- free energy landscape, free energy minima, non-Markovian dynamics, coarse-grained dynamics, collective coordinates, many-body systems, conformational sampling
- MeSH terms
- Alanine, Chemistry, Sodium Chloride, Thermodynamics, Water, Models, Statistical, Normal Distribution
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