论文标题
原子置换为拟合分子力场的多项式不变多项式
Atomic Permutationally Invariant Polynomials for Fitting Molecular Force Fields
论文作者
论文摘要
我们介绍并探索了一种用于构建小分子的力场的方法，该方法将直观的低身体经验力场术语与数据驱动的近期机器学到的统计拟合的概念结合在一起。我们将这两个关键想法汇总在一起，以弥合一方面具有高度可传递性的既定经验力场之间的差距，而机器学会了系统地改进并且可以融合到很高准确性的一方面。我们的框架扩展了针对[Mach中的元素材料开发的原子置换式多项式（APIP）。学习。技术。 2019 1 015004]到分子系统。车身秩序的分解使我们能够保持每个术语低的维度，而使用迭代拟合方案以及正则化程序可以改善训练集之外的外推。我们研究了具有通用4体术语的APIP力场，并检查一组小有机分子的性能。拟合单个分子时，我们可以达到高度的准确性，与多体机学到的力场相媲美。 APIP力场的准确性安装在一组短线性烷烃的组合训练集中，仍然显着超过经典的经验力场可以期望的，同时保留了远离训练集和新分子的合理可传递性。
We introduce and explore an approach for constructing force fields for small molecules, which combines intuitive low body order empirical force field terms with the concepts of data driven statistical fits of recent machine learned potentials. We bring these two key ideas together to bridge the gap between established empirical force fields that have a high degree of transferability on the one hand, and the machine learned potentials that are systematically improvable and can converge to very high accuracy, on the other. Our framework extends the atomic Permutationally Invariant Polynomials (aPIP) developed for elemental materials in [Mach. Learn.: Sci. Technol. 2019 1 015004] to molecular systems. The body order decomposition allows us to keep the dimensionality of each term low, while the use of an iterative fitting scheme as well as regularisation procedures improve the extrapolation outside the training set. We investigate aPIP force fields with up to generalised 4-body terms, and examine the performance on a set of small organic molecules. We achieve a high level of accuracy when fitting individual molecules, comparable to those of the many-body machine learned force fields. Fitted to a combined training set of short linear alkanes, the accuracy of the aPIP force field still significantly exceeds what can be expected from classical empirical force fields, while retaining reasonable transferability to both configurations far from the training set and to new molecules.
