基于机器学习的极化率模型模拟烷烃拉曼光谱的研究
A study of simulating Raman spectra for alkanes with a machine learning-based polarizability model
摘要：
极化率与分子体系的许多基本性质密切相关，在拉曼光谱模拟中起着不可或缺的作用。然而，量子力学（QM）方法在计算大体系极化率时，仍然受到处理能力的限制。本工作评估和比较了键极化率模型（BPM）和基于ML的原子极化率模型（AlphaML）在预测烷烃极化率方面的准确性，并考察了其模拟拉曼光谱的能力。我们发现AlphaML在学习训练数据集的极化率和预测与训练结构不同的分子的极化率方面具有明显的优势。此外，由于存在估算键各向异性的不确定性较大、模型构造中忽略了非对角参数等因素，BPM在预测极化率各向异性方面存在固有的缺陷。因此，BPM在各向异性拉曼散射的模拟中具有比AlphaML更大的误差。最后，我们证明了BPM和AlphaML都遭受了比训练数据集中使用的更大的烷烃迁移，但AlphaML的问题可以通过探索更合适的训练结构来规避。
英文摘要：
Polarizability is closely related to many fundamental characteristics of molecular systems and plays an indispensable role in simulating the Raman spectra. However, the calculations of polarizability for large systems still suffers from the limitations of processing ability of the quantum mechanical (QM) methods. This work assessed and compared the accuracy of the bond polarizability model (BPM) and a ML-based atomic polarizability model (AlphaML) in predicting polarizability of alkanes and then also investigated the ability of simulating Raman spectra. We found that the AlphaML has appreciable advantages over the BPM in learning the polarizability in the training data set and predicting polarizability of molecules that configurational differently from training structures. In addition, the BPM has inherent disadvantages in predicting polarizability anisotropy due to many factors including large uncertainties of estimating bond anisotropy, omitting of off-diagonal parameters in the construction of the model. As a result, the BPM has larger errors than the AlphaML in the simulation of anisotropic Raman scattering. Finally, we demonstrated that both the BPM and AlphaML suffer from transference to alkanes larger than those used in the training data sets, but the problem for the AlphaML can be circumvented by exploring more proper training structures.
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