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Flat-Histogram Extrapolation as a Useful Tool in the Age of Big Data

Published

Author(s)

Nathan Mahynski, Harold Hatch, Matthew Witman, David Sheen, Jeffrey R. Errington, Vincent K. Shen

Abstract

Here we review recent work by the authors to revisit the concept of extrapolating thermodynamic properties of classical systems using statistical mechanical principles. Specifically, we discuss how the combination of these principles with biased sampling techniques enables the prediction of free energy landscapes and other detailed information, such as structural properties, of the system in question. Remarkably accurat viour. While approximate, these extrapolations significantly amplify the amount of reasonably accurate information that can be extracted from simulations enabling a small set of them to feed data-intensive regression algorithms such as neural networks. Thus, this extrapolation methodology represents a useful tool for performing tasks such as high-throughput screening of physical properties, optimising force field parameters, exploring equilibrium phase behaviour, and enabling theory-guided data science for these systems.
Citation
The Journal of Chemical Physics

Keywords

Free energy, Fluid phase behavior, Molecular simulation, Thermodynamics

Citation

Mahynski, N. , Hatch, H. , Witman, M. , Sheen, D. , Errington, J. and Shen, V. (2020), Flat-Histogram Extrapolation as a Useful Tool in the Age of Big Data, The Journal of Chemical Physics, [online], https://doi.org/10.1080/08927022.2020.1747617, https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=929154 (Accessed April 23, 2024)
Created April 13, 2020, Updated February 23, 2022