The DOI matches its Crossref record; 36 of the 36 references it cites were found in a registry or an index.
Rethinking Metadynamics: From Bias Potentials to Probability Distributions
Open accessAbstract
Metadynamics is an enhanced sampling method of great popularity, based on the on-the-fly construction of a bias potential that is a function of a selected number of collective variables. We propose here a change in perspective that shifts the focus from the bias to the probability distribution reconstruction while retaining some of the key characteristics of metadynamics, such as flexible on-the-fly adjustments to the free energy estimate. The result is an enhanced sampling method that presents a drastic improvement in convergence speed, especially when dealing with suboptimal and/or multidimensional sets of collective variables. The method is especially robust and easy to use and in fact requires only a few simple parameters to be set, and it has a straightforward reweighting scheme to recover the statistics of the unbiased ensemble. Furthermore, it gives more control of the desired exploration of the phase space since the deposited bias is not allowed to grow indefinitely and it does not push the simulation to uninteresting high free energy regions. We demonstrate the performance of the method in a number of representative examples.
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Cites
Density Estimation for Statistics and Data Analysis
Silverman, B. W. (2018) · Routledge eBooks · doi:10.1201/9781315140919 · Cited by 15,796
Nonphysical sampling distributions in Monte Carlo free-energy estimation: Umbrella sampling
Torrie, G. M., & Valleau, J. P. (1977) · Journal of Computational Physics · doi:10.1016/0021-9991(77)90121-8 · Cited by 6,091
Laio, A., & Parrinello, M. (2002) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.202427399 · Cited by 5,626
Self-citationDensity Estimation for Statistics and Data Analysis
Dehnad, K. (1987) · Technometrics · doi:10.1080/00401706.1987.10488295 · Cited by 5,302
PLUMED 2: New feathers for an old bird
Tribello, G. A., Bonomi, M., Branduardi, D., Camilloni, C., & Bussi, G. (2014) · Computer Physics Communications · doi:10.1016/j.cpc.2013.09.018 · Cited by 3,479
Well-Tempered Metadynamics: A Smoothly Converging and Tunable Free-Energy Method
Barducci, A., Bussi, G., & Parrinello, M. (2008) · Physical Review Letters · doi:10.1103/physrevlett.100.020603 · Cited by 3,184
Self-citationPromoting transparency and reproducibility in enhanced molecular simulations
The PLUMED consortium (2019) · Nature Methods · doi:10.1038/s41592-019-0506-8 · Cited by 1,194
Efficient Reconstruction of Complex Free Energy Landscapes by Multiple Walkers Metadynamics
Raiteri, P., Laio, A., Gervasio, F. L., Micheletti, C., & Parrinello, M. (2006) · The Journal of Physical Chemistry B · doi:10.1021/jp054359r · Cited by 615
Self-citationA Bias-Exchange Approach to Protein Folding
Piana, S., & Laio, A. (2007) · The Journal of Physical Chemistry B · doi:10.1021/jp067873l · Cited by 598
A Time-Independent Free Energy Estimator for Metadynamics
Tiwary, P., & Parrinello, M. (2015) · The Journal of Physical Chemistry B · doi:10.1021/jp504920s · Cited by 514
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Cited by
Ibrahim, E., Lysogorskiy, Y., Drautz, R., & Piaggi, P. M. (2026) · arXiv · doi:10.48550/arxiv.2609.03044
“To thoroughly sample ion aggregation and reactivity, we performed biased simulations using On-the-fly Probability Enhanced Sampling (OPES), 27,28 which is an evolution of the Metadynamics method.”
Explainable Machine Learning Guided Enhanced Sampling of Protein Conformational Transition in HSP90
Chatterjee, S., & Ray, D. (2026) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.6c00132
Metadata
- Authors
- Invernizzi, M., & Parrinello, M.
- Venue
- The Journal of Physical Chemistry Letters
- Publisher
- American Chemical Society (ACS)
- Published
- 2020-03-19
- Volume
- 11
- Issue
- 7
- Pages
- 2731-2736
- ISSN
- 1948-7185 (ISSN-L)
- OpenAlex
- W3010816669
- Cited by
- 359
- References
- 36
- FWCI
- 14.71
- Type
- journal article
- Language
- en
- Open access
- green
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