The DOI matches its Crossref record; 45 of the 45 references it cites were found in a registry or an index.
Obtaining reaction coordinates by likelihood maximization
Abstract
We present a new approach for calculating reaction coordinates in complex systems. The new method is based on transition path sampling and likelihood maximization. It requires fewer trajectories than a single iteration of existing procedures, and it applies to both low and high friction dynamics. The new method screens a set of candidate collective variables for a good reaction coordinate that depends on a few relevant variables. The Bayesian information criterion determines whether additional variables significantly improve the reaction coordinate. Additionally, we present an advantageous transition path sampling algorithm and an algorithm to generate the most likely transition path in the space of collective variables. The method is demonstrated on two systems: a bistable model potential energy surface and nucleation in the Ising model. For the Ising model of nucleation, we quantify for the first time the role of nuclei surface area in the nucleation reaction coordinate. Surprisingly, increased surface area increases the stability of nuclei in two dimensions but decreases nuclei stability in three dimensions.
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“It is then possible to automate the parameter optimization, so as to maximise the similarity with the committor 100-102 .”
From Unsupervised machine learning in atomistic simulations, between predictions and understanding (2019) · cites this paper as [101]
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Computer Simulation of Liquids
Allen, M. P., & Tildesley, D. J. (2017) · doi:10.1093/oso/9780198803195.001.0001 · Cited by 20,945
Laio, A., & Gervasio, F. L. (2008) · Reports on Progress in Physics · doi:10.1088/0034-4885/71/12/126601 · Cited by 1,619
PyEMMA 2: A Software Package for Estimation, Validation, and Analysis of Markov Models
Scherer, M. K., Trendelkamp-Schroer, B., Paul, F., Pérez‐Hernández, G., Hoffmann, M., Plattner, N., Wehmeyer, C., Prinz, J.-H., & Noé, F. (2015) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.5b00743 · Cited by 1,160
Statistical Mechanics: Theory and Molecular Simulation
Tuckerman, M. E. (2023) · doi:10.1093/oso/9780198825562.001.0001 · Cited by 1,032
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Metadata
- Authors
- Peters, B., & Trout, B. L.
- Affiliations
- Massachusetts Institute of Technology (US)
- Venue
- The Journal of Chemical Physics
- Publisher
- AIP Publishing
- Published
- 2006-08-04
- Volume
- 125
- Issue
- 5
- Pages
- 054108-054108
- ISSN
- 0021-9606 (ISSN-L)
- 1089-7690
- OpenAlex
- W2011242225
- Cited by
- 417
- References
- 45
- Citations by year
- 2026: 15 · 2025: 20 · 2024: 22 · 2023: 41 · 2022: 22 · 2021: 23 · 2020: 26 · 2019: 21 · 2018: 19 · 2017: 50 · 2016: 27 · 2015: 23 · 2014: 14 · 2013: 13 · 2012: 15
- FWCI
- 6.61
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- The Journal of Chemical Physics (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Physical Sciences › Chemistry › Physical and Theoretical Chemistry › Advanced Physical and Chemical Molecular Interactions
- Physical Sciences › Physics and Astronomy › Condensed Matter Physics › Theoretical and Computational Physics
- Physical Sciences › Mathematics › Mathematical Physics › Stochastic processes and statistical mechanics
- Keywords
- reaction coordinate, transition path sampling, likelihood maximization, collective variables, Bayesian information criterion, nucleation, Ising model, surface area effect
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