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• W2885128148 abstract "A robust importance-sampling algorithm for mapping free-energy surfaces over geometrical variables, coined meta-eABF, is introduced. This algorithm shaves the free-energy barriers and floods valleys by incorporating a history-dependent potential term in the extended adaptive biasing force (eABF) framework. Numerical applications on both toy models and nontrivial examples indicate that meta-eABF explores the free-energy surface significantly faster than either eABF or metadynamics (MtD) alone, without the need to stratify the reaction pathway. In some favorable cases, meta-eABF can be as much as five times faster than other importance-sampling algorithms. Many of the shortcomings inherent to eABF and MtD, like kinetic trapping in regions of configurational space already adequately sampled, the requirement of prior knowledge of the free-energy landscape to set up the simulation, are readily eliminated in meta-eABF. Meta-eABF, therefore, represents an appealing solution for a broad range of applications, especially when both eABF and MtD fail to achieve the desired result." @default.
• W2885128148 created "2018-08-22" @default.
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• W2885128148 date "2018-08-03" @default.
• W2885128148 modified "2023-10-16" @default.
• W2885128148 title "Zooming across the Free-Energy Landscape: Shaving Barriers, and Flooding Valleys" @default.
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• W2885128148 doi "https://doi.org/10.1021/acs.jpclett.8b01994" @default.
• W2885128148 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/30074802" @default.
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