Machine Learning Approach for Describing Water OH Stretch Vibrations,Journal of Chemical Theory and Computation

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Machine Learning Approach for Describing Water OH Stretch Vibrations
Journal of Chemical Theory and Computation ( IF 5.7 ) Pub Date : 2021-09-09 , DOI: 10.1021/acs.jctc.1c00540
Kijeong Kwac ₁ , Holly Freedman ₁ , Minhaeng Cho _{1,

2}

Affiliation

A machine learning approach employing neural networks is developed to calculate the vibrational frequency shifts and transition dipole moments of the symmetric and antisymmetric OH stretch vibrations of a water molecule surrounded by water molecules. We employed the atom-centered symmetry functions (ACSFs), polynomial functions, and Gaussian-type orbital-based density vectors as descriptor functions and compared their performances in predicting vibrational frequency shifts using the trained neural networks. The ACSFs perform best in modeling the frequency shifts of the OH stretch vibration of water among the types of descriptor functions considered in this paper. However, the differences in performance among these three descriptors are not significant. We also tried a feature selection method called CUR matrix decomposition to assess the importance and leverage of the individual functions in the set of selected descriptor functions. We found that a significant number of those functions included in the set of descriptor functions give redundant information in describing the configuration of the water system. We here show that the predicted vibrational frequency shifts by trained neural networks successfully describe the solvent–solute interaction-induced fluctuations of OH stretch frequencies.

中文翻译：

描述水 OH 拉伸振动的机器学习方法

开发了一种采用神经网络的机器学习方法来计算被水分子包围的水分子的对称和反对称 OH 拉伸振动的振动频移和跃迁偶极矩。我们采用以原子为中心的对称函数 (ACSF)、多项式函数和基于高斯型轨道的密度向量作为描述符函数，并比较了它们在使用训练有素的神经网络预测振动频移方面的性能。在本文考虑的描述符函数类型中，ACSF 在模拟水的 OH 伸缩振动的频移方面表现最佳。然而，这三个描述符之间的性能差异并不显着。我们还尝试了一种称为 CUR 矩阵分解的特征选择方法，以评估所选描述符函数集中各个函数的重要性和影响力。我们发现，描述符函数集中包含的大量函数在描述水系统配置时提供了冗余信息。我们在这里表明，训练有素的神经网络预测的振动频移成功地描述了溶剂-溶质相互作用引起的 OH 拉伸频率的波动。

更新日期：2021-10-12

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