Open Force Field Evaluator: An Automated, Efficient, and Scalable Framework for the Estimation of Physical Properties from Molecular Simulation.
Open Force Field Evaluator: An Automated, Efficient, and Scalable Framework for the Estimation of Physical Properties from Molecular Simulation.
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DOI：
10.1021/acs.jctc.1c01111
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发表时间：
2022-06-14
影响因子：
5.5
通讯作者：
Shirts, Michael R.
中科院分区：
文献类型：
--
作者：
Boothroyd, Simon;Wang, Lee-Ping;Mobley, David L.;Chodera, John D.;Shirts, Michael R.
开发精确的分子经典力场表示是实现分子模拟全部潜力的关键，既可以作为获得广泛的化学和生物现象的基本见解的强大途径，也可以用于预测物质的物理化学和机械特性。Open Force Field Consortium是一个由业界资助的开放科学项目，致力于开发开源工具，以快速生成新的高质量小分子力场。其中一个不可或缺的方面是根据高质量的凝聚相物理性质数据对力场进行参数化和评估，这些数据来自NIST ThermoML Archive等开放数据源以及量子化学数据。仅开放数据档案中的此类实验数据的数量就需要大量的人力和计算资源来手动管理和估计，特别是当必须对多组力场参数进行估计时。在这里，我们提出了一个完全自动化的，高度可扩展的框架，用于评估力场参数的物理特性及其梯度。它是作为一个模块化和可扩展的Python框架编写的，它采用了智能多尺度估计方法，允许从模拟和缓存的模拟数据中自动估计属性，以及用于估计新属性的可插入API。在这项研究中，我们通过对OpenFF 1.0.0小分子力场，GAFF 1.8和GAFF 2.1力场进行基准测试来证明该框架的实用性，该框架针对使用框架实用程序策划的二元密度和混合焓测量的测试集。此外，我们证明了该框架的效用作为力场优化的一部分，通过使用它旁边的ForceBalance，系统力场优化的框架，重新训练一组非键合的货车德瓦尔斯参数对密度和汽化焓测量的训练集。
Developing accurate classical force field representations of molecules is key to realizing the full potential of molecular simulations, both as a powerful route to gaining fundamental insight into a broad spectrum of chemical and biological phenomena, and for predicting physicochemical and mechanical properties of substances. The Open Force Field Consortium is an industry-funded open science effort to this end, developing open source tools for rapidly generating new, high-quality small molecule force fields. An integral aspect of this is the parameterization and assessment of force fields against high-quality, condensed-phase physical property data, curated from open data sources such the NIST ThermoML Archive, alongside quantum chemical data. The quantity of such experimental data in open data archives alone would require an onerous amount of human and compute resources to both curate and estimate manually, especially when estimations must be made for numerous sets of force field parameters. Here we present an entirely automated, highly scalable framework for evaluating physical properties and their gradients in terms of force field parameters. It is written as a modular and extensible Python framework, which employs an intelligent multiscale estimation approach that allows for the automated estimation of properties from simulation and cached simulation data, and a pluggable API for estimation of new properties. In this study we demonstrate the utility of the framework by benchmarking the OpenFF 1.0.0 small molecule force field, GAFF 1.8 and GAFF 2.1 force fields against a test set of binary density and enthalpy of mixing measurements curated using the frameworks utilities. Further, we demonstrate the framework’s utility as part of force field optimization by using it alongside ForceBalance, a framework for systematic force field optimization, to retrain a set of non-bonded van der Waals parameters against a training set of density and enthalpy of vaporization measurements.
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影响因子：
14.9
作者：
Dodda LS;Cabeza de Vaca I;Tirado-Rives J;Jorgensen WL
通讯作者：
Jorgensen WL
影响因子：
4.4
作者：
Horn, HW;Swope, WC;Head-Gordon, T
通讯作者：
Head-Gordon, T
影响因子：
5.5
作者：
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通讯作者：
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影响因子：
4.3
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通讯作者：
Pande VS
影响因子：
3.5
作者：
Dauber-Osguthorpe, Pnina;Hagler, A. T.
通讯作者：
Hagler, A. T.