The DOI matches its Crossref record; 6 of the 6 references it cites were found in a registry or an index.
Enhanced sampling in molecular dynamics
Open accessAbstract
Although molecular dynamics simulations have become a useful tool in essentially all fields of chemistry, condensed matter physics, materials science, and biology, there is still a large gap between the time scale which can be reached in molecular dynamics simulations and that observed in experiments. To address the problem, many enhanced sampling methods were introduced, which effectively extend the time scale being approached in simulations. In this perspective, we review a variety of enhanced sampling methods. We first discuss collective-variables-based methods including metadynamics and variationally enhanced sampling. Then, collective variable free methods such as parallel tempering and integrated tempering methods are presented. At last, we conclude with a brief introduction of some newly developed combinatory methods. We summarize in this perspective not only the theoretical background and numerical implementation of these methods but also the new challenges and prospects in the field of the enhanced sampling.
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Cites
Laio, A., & Parrinello, M. (2002) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.202427399 · Cited by 5,626
Coarse Grained Model for Semiquantitative Lipid Simulations
Marrink, S. J., de Vries, A. H., & Mark, A. E. (2004) · The Journal of Physical Chemistry B · doi:10.1021/jp036508g · Cited by 2,257
T RANSITION P ATH S AMPLING : Throwing Ropes Over Rough Mountain Passes, in the Dark
Bolhuis, P. G., Chandler, D., Dellago, C., & Geissler, P. L. (2002) · Annual Review of Physical Chemistry · doi:10.1146/annurev.physchem.53.082301.113146 · Cited by 1,972
Identification of slow molecular order parameters for Markov model construction
Pérez-Hernández, G., Paul, F., Giorgino, T., De Fabritiis, G., & Noé, F. (2013) · The Journal of Chemical Physics · doi:10.1063/1.4811489 · Cited by 990
Schwantes, C. R., & Pande, V. S. (2013) · Journal of Chemical Theory and Computation · doi:10.1021/ct300878a · Cited by 597
Assessing the Accuracy of Metadynamics
Laio, A., Rodriguez-Fortea, A., Gervasio, F. L., Ceccarelli, M., & Parrinello, M. (2005) · The Journal of Physical Chemistry B · doi:10.1021/jp045424k · Cited by 517
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Machine learning in the analysis of biomolecular simulations
Kaptan, S., & Vattulainen, I. (2022) · Advances in Physics: X · doi:10.1080/23746149.2021.2006080 · Cited by 31
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Cao, X., & Tian, P. (2021) · International Journal of Molecular Sciences · doi:10.3390/ijms22095053 · Cited by 10
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Wang, B., Wang, C., Chen, J., Liu, D., Sun, C., Zhang, J., Zhang, K., & Li, H. (2026) · Nature Machine Intelligence · doi:10.1038/s42256-026-01198-9 · Cited by 5
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Metadata
- Authors
- Yang, Y. I., Shao, Q., Zhang, J., Yang, L., & Gao, Y. Q.
- Venue
- The Journal of Chemical Physics
- Publisher
- AIP Publishing
- Published
- 2019-08-16
- Volume
- 151
- Issue
- 7
- Pages
- 070902-070902
- ISSN
- 0021-9606 (ISSN-L)
- 1089-7690
- OpenAlex
- W2968426314
- Cited by
- 398
- References
- 6
- FWCI
- 23.97
- Type
- journal article
- Language
- en
- Open access
- hybrid
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