Combining Fast Exploration with Accurate Reweighting in the OPES-eABF Hybrid Sampling Method.

IF 5.7 1区化学 Q2 CHEMISTRY, PHYSICAL

Journal of Chemical Theory and Computation Pub Date : 2025-06-18 DOI:10.1021/acs.jctc.5c00395

Andreas Hulm,Robert P Schiller,Christian Ochsenfeld

引用次数: 0

Abstract

On-the-fly probability enhanced sampling (OPES) has recently been introduced [Invernizzi, M.; Parrinello, M. J. Chem. Theory Comput. 2022, 18, 3988-3996], with important improvements over the highly popular metadynamics methods. In our work, we introduce a new combination of OPES with the extended-system adaptive biasing force (eABF) method. We show that the resulting OPES-eABF hybrid is highly robust to the choice of input parameters, while ensuring faster exploration of configuration space than the original OPES. The only critical parameter of OPES-eABF is the coupling width to the extended-system, for which we introduce an automatic algorithm based on a short initial unbiased simulation, such that OPES-eABF requires minimal user intervention. Additionally, we show that due to the decoupling of the physical system from the time-dependent potential, unbiased probabilities of visited configurations are recovered highly accurately.

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OPES-eABF混合采样方法中快速勘探与精确加权的结合。

动态概率增强抽样（OPES）最近被引入[Invernizzi， M.；Parrinello, m.j. Chem。理论计算，2022,18,3988-3996]，对非常流行的元动力学方法进行了重要改进。在我们的工作中，我们引入了一种新的OPES与扩展系统自适应偏置力（eABF）方法的组合。结果表明，所得到的OPES- eabf混合算法对输入参数的选择具有高度鲁棒性，同时确保比原始OPES更快地探索配置空间。OPES-eABF的唯一关键参数是与扩展系统的耦合宽度，为此我们引入了一种基于短初始无偏模拟的自动算法，使得OPES-eABF需要最少的用户干预。此外，我们表明，由于物理系统与时间相关势的解耦，访问组态的无偏概率被高度准确地恢复。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Journal of Chemical Theory and Computation 化学-物理：原子、分子和化学物理

CiteScore

9.90

自引率

16.40%

发文量

568

审稿时长

1 months

期刊介绍： The Journal of Chemical Theory and Computation invites new and original contributions with the understanding that, if accepted, they will not be published elsewhere. Papers reporting new theories, methodology, and/or important applications in quantum electronic structure, molecular dynamics, and statistical mechanics are appropriate for submission to this Journal. Specific topics include advances in or applications of ab initio quantum mechanics, density functional theory, design and properties of new materials, surface science, Monte Carlo simulations, solvation models, QM/MM calculations, biomolecular structure prediction, and molecular dynamics in the broadest sense including gas-phase dynamics, ab initio dynamics, biomolecular dynamics, and protein folding. The Journal does not consider papers that are straightforward applications of known methods including DFT and molecular dynamics. The Journal favors submissions that include advances in theory or methodology with applications to compelling problems.