论文标题
$δ$ - 机器人的力场学习方法，由CCSD（T）4体校正向MB-Pol水势说明
A $Δ$-Machine Learning Approach for Force Fields, Illustrated by a CCSD(T) 4-body Correction to the MB-pol Water Potential
论文作者
论文摘要
$Δ$ -Machine学习（$δ$ -ML）已显示出有效，有效地将低水平的ML势能表面带到CCSD（T）质量。在这里，我们提出将这种方法扩展到一般力场，该方法隐含或明确包含多体效应。在描述了这种通用方法之后，我们将其说明了MB-POL水电位，其中包含CCSD（T）2体和3体相互作用，但依赖于TTM4-F 4体和更高的体型相互作用。四体MB-POL（TTM4-F）相互作用在很短的范围内失败，对于某些异构体的水六聚体误差最高为0.84 kcal/mol，主要是由于4体误差。我们使用最近的CCSD（T）4体能量数据集应用$δ$ -ML将其用于4体相互作用，我们用来开发新的水潜力，Q-Aqua。这种四体校正被证明可以提高MB-POL电位的准确性，以示为8个异构体的相对能量以及谐波频率的相对能量。新电位在非常短的范围内是强大的，因此在高压和/或高温下模拟应该是可靠的。
$Δ$-Machine Learning ($Δ$-ML) has been shown to effectively and efficiently bring a low-level ML potential energy surface to CCSD(T) quality. Here we propose extending this approach to general force fields, which implicitly or explicitly contain many-body effects. After describing this general approach, we illustrate it for the MB-pol water potential which contains CCSD(T) 2-body and 3-body interactions but relies on the TTM4-F 4-body and higher body interactions. The 4-body MB-pol (TTM4-F) interaction fails at a very short range and for the water hexamer errors up to 0.84 kcal/mol are seen for some isomers, owing mainly to 4-body errors. We apply $Δ$-ML for the 4-body interaction, using a recent dataset of CCSD(T) 4-body energies that we used to develop a new water potential, q-AQUA. This 4-body correction is shown to improve the accuracy of the MB-pol potential for the relative energies of 8 isomers of the water hexamer as well as the harmonic frequencies. The new potential is robust in the very short range and so should be reliable for simulations at high pressure and/or high temperature.
