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Free energy surface reconstruction from umbrella samples using Gaussian process regression II: Multiple collective variables

Bernstein, N and Stecher, T and Csányi, G Free energy surface reconstruction from umbrella samples using Gaussian process regression II: Multiple collective variables. (Unpublished)

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Abstract

We demonstrate how a prior assumption of smoothness can be used to enhance the reconstruction of free energy profiles from multiple umbrella sampling simulations using the Bayesian Gaussian process regression approach. The method we derive allows the concurrent use of histograms and free energy gradients and can easily be extended to include further data. In Part I we review the necessary theory and test the method for one collective variable. We demonstrate improved performance with respect to the weighted histogram analysis method and obtain meaningful error bars without any significant additional computation. In Part II we consider the case of multiple collective variables and compare to a reconstruction using least squares fitting of radial basis functions. We find substantial improvements in the regimes of spatially sparse data or short sampling trajectories. A software implementation is made available on www.libatoms.org.

Item Type: Article
Uncontrolled Keywords: cond-mat.stat-mech cond-mat.stat-mech
Subjects: UNSPECIFIED
Divisions: Div C > Applied Mechanics
Depositing User: Cron Job
Date Deposited: 17 Jul 2017 20:11
Last Modified: 18 Jul 2017 09:31
DOI: