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.
Cite
@article{arxiv.1312.4419,
title = {Free energy surface reconstruction from umbrella samples using Gaussian process regression},
author = {Thomas Stecher and Noam Bernstein and Gábor Csányi},
journal= {arXiv preprint arXiv:1312.4419},
year = {2014}
}
Comments
34 pages, 10 figures. Combines previous version part I (arxiv 1312.4419) and part II (arxiv 1312.4420)