Best Practices for Quantification of Uncertainty and Sampling Quality in Molecular Simulations [Article v1.0]

Keywords: best practices, uncertainty analysis, statistical analysis, molecular dynamics, monte carlo, molecular simulation

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 systematic errors arising, e.g., from inaccuracy in the chosen model or force field.

workflow for extracting and using uncertainties
Published
2018-10-27
How to Cite
Grossfield, A., Patrone, P. N., Roe, D. R., Schultz, A. J., Siderius, D., & Zuckerman, D. M. (2018). Best Practices for Quantification of Uncertainty and Sampling Quality in Molecular Simulations [Article v1.0]. Living Journal of Computational Molecular Science, 1(1), 5067. https://doi.org/10.33011/livecoms.1.1.5067
Section
Articles