期刊
JOURNAL OF CHEMICAL THEORY AND COMPUTATION
卷 10, 期 9, 页码 4079-4097出版社
AMER CHEMICAL SOC
DOI: 10.1021/ct500438v
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资金
- Office of Naval Research (ONR) through the Naval Research Laboratory's basic research program
- Office of Naval Research [N000141010826]
- European Union FP7-NMP programme [229205 ADGLASS]
- EPSRC [EP/J010847/1] Funding Source: UKRI
- Engineering and Physical Sciences Research Council [EP/J010847/1] Funding Source: researchfish
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.
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