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Best Practices for Quantification of Uncertainty and Sampling Quality in Molecular Simulations

Published

Author(s)

Alan Grossfield, Paul Patrone, Daniel R. Roe, Andrew J. Schultz, Daniel Siderius, Daniel M. Zuckerman

Abstract

The quantitative assessment of uncertainty and sampling quality is essential in molecular simulation. Many systems of interest are highly complex, often at the edge of current computational capabilities. Modelers must therefore analyze and communicate statistical uncertainties so that ''consumers'' of simulated data understand its significance and limitations. This article covers key analyses appropriate for trajectory data generated by conventional simulation methods such as molecular dynamics and (single Markov chain) Monte Carlo. It also provides guidance for analyzing some 'enhanced' sampling approaches. We do not discuss \emphsystematic} errors arising, e.g.\ from inaccuracy in the chosen model or force field.
Citation
Living Journal of Computational Molecular Science
Volume
1
Issue
1

Keywords

molecular simulation , uncertainty analysis , statistical analysis

Citation

Grossfield, A. , Patrone, P. , Roe, D. , Schultz, A. , Siderius, D. and Zuckerman, D. (2018), Best Practices for Quantification of Uncertainty and Sampling Quality in Molecular Simulations, Living Journal of Computational Molecular Science, [online], https://doi.org/10.33011/livecoms.1.1.5067, https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=925674 (Accessed May 19, 2024)

Issues

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Created October 26, 2018, Updated October 12, 2021