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



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


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 \emph{systematic} errors arising, e.g.\ from inaccuracy in the chosen model or force field.
Living Journal of Computational Molecular Science


molecular simulation , uncertainty analysis , statistical analysis
Created October 27, 2018, Updated February 6, 2019