Atomic permutationally invariant polynomials for fitting molecular force fields
Atomic permutationally invariant polynomials for fitting molecular force fields
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DOI：
10.1088/2632-2153/abd51e
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发表时间：
2021-06-01
影响因子：
6.8
通讯作者：
Csanyi, Gabor
中科院分区：
文献类型：
--
作者：
Allen, Alice E. A.;Dusson, Genevieve;Csanyi, Gabor
我们介绍并探索了一种用于构建小分子力场的方法，该方法将直观的低体阶经验力场项与最近机器学习潜力的数据驱动统计拟合的概念相结合。我们将这两个关键思想结合在一起，以弥合一方面具有高度可转移性的已建立经验力场与另一方面可系统改进并可收敛到非常高精度的机器学习潜力之间的差距。我们的框架扩展了在（2019年马赫）中为元素材料开发的原子置换不变多项式（aPIP）。学习.格：sci. 1015004）应用于分子系统。体序分解使我们能够保持每个项的维数较低，而使用迭代拟合方案以及正则化程序可以提高训练集外的外推。我们调查aPIP力场与广义4体条款，并检查一组小的有机分子的性能。在拟合单个分子时，我们实现了高水平的准确性，可与多体机器学习力场的准确性相媲美。拟合到短直链烷烃的组合训练集，aPIP力场的准确性仍然显著超过从经典经验力场可以预期的，同时保留到远离训练集和新分子的两种配置的合理可转移性。
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 (2019 Mach. Learn.: Sci. Technol. 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.