title: Free Energy Surface Reconstruction from Umbrella Samples Using Gaussian Process Regression.
abstract: We demonstrate how the Gaussian process regression approach can be used to efficiently reconstruct free energy surfaces from umbrella sampling simulations. By making a prior assumption of smoothness and taking account of the sampling noise in a consistent fashion, we achieve a significant improvement in accuracy over the state of the art in two or more dimensions or, equivalently, a significant cost reduction to obtain the free energy surface within a prescribed tolerance in both regimes of spatially sparse data and short sampling trajectories. Stemming from its Bayesian interpretation the method provides meaningful error bars without significant additional computation. A software implementation is made available on www.libatoms.org .
authors.affiliation: Department of Engineering, University of Cambridge , Trumpington Street, Cambridge, CB2 1PZ, U.K.
authors.forename: Thomas
authors.initials: T
authors.lastname: Stecher
authors.affiliation: Naval Research Laboratory, Center for Computational Materials Science , Washington, D.C. 20375, United States.
authors.forename: Noam
authors.initials: N
authors.lastname: Bernstein
authors.affiliation: Department of Engineering, University of Cambridge , Trumpington Street, Cambridge, CB2 1PZ, U.K.
authors.forename: Gábor
authors.initials: G
authors.lastname: Csányi