通过路径规划生成的初始猜测,有效加快最小自由能路径的收敛速度

IF 3.4 3区 化学 Q2 CHEMISTRY, MULTIDISCIPLINARY
Yi Sun
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引用次数: 0

摘要

我们证明,将移位聚类算法与基于快速行进的算法相结合,可以生成给定自由能谱(FEL)的最小能量路径(MEP)的精确近似值。使用这种近似值作为 MEP 的初始猜测,然后用字符串方法进一步完善(称为快速行进树(FMT)-字符串组合方法),可以显著减少 MEP 收敛所需的迭代次数。与使用线性插值(LI)进行初始猜测相比,这种方法节省了大量时间。当有近似或收敛的 FEL 时,我们的方法为获得 MEP 的有效初始猜测提供了可行的解决方案。这项工作凸显了在化学反应中应用基于 FMT 的方法提取 MEP 的潜力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Efficient acceleration of the convergence of the minimum free energy path via a path-planning generated initial guess

Efficient acceleration of the convergence of the minimum free energy path via a path-planning generated initial guess

We demonstrate that combining a shifted clustering algorithm with a fast-marching-based algorithm can generate accurate approximations of the minimum energy path (MEP) given a free energy landscape (FEL). Using this approximation as the initial guess for the MEP, followed by further refinement with the string method (referred to as the fast marching tree (FMT)-string combined approach), significantly reduces the number of iterations required for MEP convergence. This approach saves substantial time compared to using linear interpolation (LI) for the initial guess. Our method offers a viable solution for obtaining an effective initial guess of the MEP when an approximate or converged FEL is available. This work highlights the potential of applying FMT-based approaches to extract the MEP in chemical reactions.

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来源期刊
CiteScore
6.60
自引率
3.30%
发文量
247
审稿时长
1.7 months
期刊介绍: This distinguished journal publishes articles concerned with all aspects of computational chemistry: analytical, biological, inorganic, organic, physical, and materials. The Journal of Computational Chemistry presents original research, contemporary developments in theory and methodology, and state-of-the-art applications. Computational areas that are featured in the journal include ab initio and semiempirical quantum mechanics, density functional theory, molecular mechanics, molecular dynamics, statistical mechanics, cheminformatics, biomolecular structure prediction, molecular design, and bioinformatics.
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