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使用分子模拟设计框架(MoSDeF)实现分子动力学和蒙特卡罗模拟的可重复性和可再现性。

Achieving Reproducibility and Replicability of Molecular Dynamics and Monte Carlo Simulations Using the Molecular Simulation Design Framework (MoSDeF).

作者信息

Craven Nicholas C, Singh Ramanish, Quach Co D, Gilmer Justin B, Crawford Brad, Marin-Rimoldi Eliseo, Smith Ryan, DeFever Ryan, Dyukov Maxim S, Fothergill Jenny W, Jones Chris, Moore Timothy C, Butler Brandon L, Anderson Joshua A, Iacovella Christopher R, Jankowski Eric, Maginn Edward J, Potoff Jeffrey J, Glotzer Sharon C, Cummings Peter T, McCabe Clare, Siepmann J Ilja

机构信息

Interdisciplinary Materials Science Program, Vanderbilt University, Nashville, Tennessee 37235-0106, United States.

Department of Chemical Engineering and Materials Science, University of Minnesota, Minneapolis, Minnesota 55455-0132, United States.

出版信息

J Chem Eng Data. 2025 May 27;70(6):2178-2199. doi: 10.1021/acs.jced.5c00010. eCollection 2025 Jun 12.

DOI:10.1021/acs.jced.5c00010
PMID:40528899
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC12169611/
Abstract

Molecular simulations are increasingly used to predict thermophysical properties and explore molecular-level phenomena beyond modern imaging techniques. To make these tools accessible to nonexperts, several open-source molecular dynamics (MD) and Monte Carlo (MC) codes have been developed. However, using these tools is challenging, and concerns about the validity and reproducibility of the simulation data persist. In 2017, Schappals et al. reported a benchmarking study involving several research groups independently performing MD and MC simulations using different software to predict densities of alkanes using common molecular mechanics force fields [J. Chem. Theory Comput.2017, 4270-4280]. Although the predicted densities were reasonably close (mostly within 1%), the data often fell outside of the combined statistical uncertainties of the different simulations. Schappals et al. concluded that there are unavoidable errors inherent to molecular simulations once a certain degree of complexity of the system is reached. The Molecular Simulation Design Framework (MoSDeF) is a workflow package designed to achieve TRUE (Transparent, Reproducible, Usable-by-others, and Extensible) simulation studies by standardizing the implementation of molecular models for various simulation engines. This work demonstrates that using MoSDeF to initialize a simulation workflow results in consistent predictions of system density, even while increasing model complexity.

摘要

分子模拟越来越多地用于预测热物理性质,并探索超越现代成像技术的分子水平现象。为了让非专业人士也能使用这些工具,已经开发了几个开源分子动力学(MD)和蒙特卡罗(MC)代码。然而,使用这些工具具有挑战性,并且对模拟数据的有效性和可重复性的担忧依然存在。2017年,沙帕尔斯等人报告了一项基准研究,该研究涉及几个研究小组,他们使用不同软件独立进行MD和MC模拟,以使用常见分子力学力场预测烷烃的密度[《化学理论计算杂志》2017年,4270 - 4280页]。尽管预测的密度相当接近(大多在1%以内),但数据常常超出不同模拟的综合统计不确定性范围。沙帕尔斯等人得出结论,一旦系统达到一定程度的复杂性,分子模拟中就存在不可避免的误差。分子模拟设计框架(MoSDeF)是一个工作流包,旨在通过规范各种模拟引擎的分子模型实现,来完成真实(透明、可重复、他人可用且可扩展)的模拟研究。这项工作表明,使用MoSDeF初始化模拟工作流会产生一致的系统密度预测,即使在增加模型复杂性的情况下也是如此。

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本文引用的文献

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利用元学习对多种纳米多孔材料进行指纹识别以实现最佳储氢条件
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MoSDeF Cassandra: A complete Python interface for the Cassandra Monte Carlo software.用于卡珊德拉蒙特卡洛软件的完整Python接口:MoSDeF卡珊德拉
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Mol Phys. 2020;118(9-10). doi: 10.1080/00268976.2020.1742938. Epub 2020 Apr 8.
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From Order to Disorder: Computational Design of Triblock Amphiphiles with 1 nm Domains.从有序到无序:具有 1nm 畴的嵌段两亲物的计算设计。
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MoSDeF, a Python Framework Enabling Large-Scale Computational Screening of Soft Matter: Application to Chemistry-Property Relationships in Lubricating Monolayer Films.MoSDeF,一个用于大规模软物质计算筛选的 Python 框架:在润滑单层膜中的化学-性质关系中的应用。
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Best Practices for Foundations in Molecular Simulations [Article v1.0].分子模拟基础的最佳实践 [文章v1.0]
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