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首页> 外文期刊>The journal of physical chemistry, B. Condensed matter, materials, surfaces, interfaces & biophysical >Toward Automated Benchmarking of Atomistic Force Fields: Neat Liquid Densities and Static Dielectric Constants from the ThermoML Data Archive
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Toward Automated Benchmarking of Atomistic Force Fields: Neat Liquid Densities and Static Dielectric Constants from the ThermoML Data Archive

机译:走向原子力场的自动基准测试:ThermoML Data Archive的整洁液体密度和静态介电常数

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摘要

Atomistic molecular simulations are a powerful way to make quantitative predictions, but the accuracy of these predictions depends entirely on the quality of the force field employed. Although experimental measurements of fundamental physical 0 properties offer a straightforward approach for evaluating force field quality, the bulk of this information has been tied up in formats that are not machine-readable. Compiling benchmark data sets of physical properties from non-machine-readable sources requires substantial human effort and is prone to the accumulation of human errors, hindering the development of reproducible benchmarks of force-field accuracy. Here, we examine the feasibility of benchmarking atomistic force fields against the NIST ThermoML data archive of physicochemical measurements, which aggregates thousands of experimental measurements in a portable, machine-readable, self-annotating IUPAC-standard format. As a proof of concept, we present a detailed benchmark of the generalized Amber small-molecule force field (GAFF) using the AM1-BCC charge model against experimental measurements (specifically, bulk liquid densities and static dielectric constants at ambient pressure) automatically extracted from the archive and discuss the extent of data available for use in larger scale (or continuously performed) benchmarks. The results of even this limited initial benchmark highlight a general problem with fixed-charge force fields in the representation low-dielectric environments, such as those seen in binding cavities or biological membranes.
机译:原子分子模拟是进行定量预测的有力方法,但是这些预测的准确性完全取决于所用力场的质量。尽管基本物理0特性的实验测量提供了一种简单的方法来评估力场质量,但这些信息的大部分已被束缚在机器无法读取的格式中。从非机器可读的源中收集物理性能的基准数据集需要大量的人工,并且容易造成人为错误,从而阻碍了可重复性力场基准的发展。在这里,我们检查了根据NIST ThermoML物理化学测量数据存档基准对原子力场进行基准测试的可行性,该数据存档以便携式,机器可读,自注释IUPAC标准格式汇总了数千个实验测量。作为概念验证,我们提供了使用AM1-BCC电荷模型针对从中自动提取的实验测量值(具体而言,环境压力下的体液体密度和静态介电常数)的广义琥珀色小分子力场(GAFF)的详细基准。存档并讨论可用于大规模(或连续执行)基准测试的数据范围。即使是这种有限的初始基准,其结果也突出了代表性的低介电环境中固定电荷力场的普遍问题,例如在结合腔或生物膜中看到的那些。

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