The prediction of atomic kinetic energies from coordinates of surrounding atoms using kriging machine learning
The prediction of atomic kinetic energies from coordinates of surrounding atoms using kriging machine learning
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DOI：
10.1007/s00214-014-1499-0
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发表时间：
2014-05-21
影响因子：
1.7
通讯作者：
Popelier, Paul L. A.
中科院分区：
文献类型：
--
作者：
Fletcher, Timothy L.;Kandathil, Shaun M.;Popelier, Paul L. A.
新一代力场的设计不仅考虑了原子间电子能，而且考虑了原子内能。这种策略保证了力场和支撑力场的量子力学之间的忠实映射。量子化学拓扑提供了一种能量分配，其中原子具有明确定义的电子动能，我们感兴趣的是捕捉它们如何响应周围原子的位置变化。一种名为克里金的机器学习方法成功地从分子构型的训练集创建了模型，然后可以用来预测以前看不见的分子构型中发生的原子动能。我们提出了一个概念验证的基础上增加复杂性的四个分子（甲醇，N-甲基乙酰胺，甘氨酸和三甘氨酸）。我们测试如何以及原子动能可以模拟训练集的大小，分子大小和元素组成。对于所有测试的原子，平均原子动能误差低于1.5 kJ mol（-1），在大多数情况下远低于此。这表示误差都在0.5%以下，因此即使使用中等到小的训练集大小，使用克里金方法也可以很好地模拟动能。
A novel design of a next-generation force field considers not only the electronic inter-atomic energy but also intra-atomic energy. This strategy promises a faithful mapping between the force field and the quantum mechanics that underpins it. Quantum chemical topology provides an energy partitioning in which atoms have well-defined electronic kinetic energies, and we are interested in capturing how they respond to changes in the positions of surrounding atoms. A machine learning method called kriging successfully creates models from a training set of molecular configurations that can then be used to predict the atomic kinetic energies occurring in previously unseen molecular configurations. We present a proof-of-concept based on four molecules of increasing complexity ( methanol, N-methylacetamide, glycine and triglycine). We test how well the atomic kinetic energies can be modelled with respect to training set size, molecule size and elemental composition. For all atoms tested, the mean atomic kinetic energy errors fall below 1.5 kJ mol(-1), and far below this in most cases. This represents errors all under 0.5 % and thus the kinetic energies are well modelled using the kriging method, even when using modest-to-small training set sizes.