Chen Yuzhuo, Pios Sebastian V, Gelin Maxim F, Chen Lipeng
Zhejiang Laboratory, Hangzhou 311100, China.
School of Science, Hangzhou Dianzi University, Hangzhou 310018, China.
J Chem Theory Comput. 2024 Jun 11;20(11):4703-4710. doi: 10.1021/acs.jctc.4c00173. Epub 2024 Jun 2.
In recent years, machine learning (ML) surrogate models have emerged as an indispensable tool to accelerate simulations of physical and chemical processes. However, there is still a lack of ML models that can accurately predict molecular vibrational spectra. Here, we present a highly efficient multitask ML surrogate model termed Vibrational Spectra Neural Network (VSpecNN), to accurately calculate infrared (IR) and Raman spectra based on dipole moments and polarizabilities obtained on-the-fly via ML-enhanced molecular dynamics simulations. The methodology is applied to pyrazine, a prototypical polyatomic chromophore. The VSpecNN-predicted energies are well within the chemical accuracy (1 kcal/mol), and the errors for VSpecNN-predicted forces are only half of those obtained from a popular high-performance ML model. Compared to the ab initio reference, the VSpecNN-predicted frequencies of IR and Raman spectra differ only by less than 5.87 cm, and the intensities of IR spectra and the depolarization ratios of Raman spectra are well reproduced. The VSpecNN model developed in this work highlights the importance of constructing highly accurate neural network potentials for predicting molecular vibrational spectra.
近年来，机器学习（ML）替代模型已成为加速物理和化学过程模拟的不可或缺的工具。然而，仍然缺乏能够准确预测分子振动光谱的ML模型。在此，我们提出了一种高效的多任务ML替代模型，称为振动光谱神经网络（VSpecNN），用于基于通过ML增强分子动力学模拟即时获得的偶极矩和极化率来准确计算红外（IR）和拉曼光谱。该方法应用于典型的多原子发色团吡嗪。VSpecNN预测的能量完全在化学精度（1千卡/摩尔）范围内，VSpecNN预测力的误差仅为从一个流行的高性能ML模型获得的误差的一半。与从头算参考相比，VSpecNN预测的IR和拉曼光谱频率差异仅小于5.87厘米，并且IR光谱强度和拉曼光谱的退偏比得到了很好的再现。这项工作中开发的VSpecNN模型突出了构建高精度神经网络势以预测分子振动光谱的重要性。
