利用物理信息深度学习模型加速分子振动光谱模拟
Accelerating molecular vibrational spectra simulations with a physically informed deep learning model
AI总结：
本文提出一种物理信息的高维神经网络势架构，通过机器学习分子动力学模拟准确预测红外和拉曼光谱，在吡嗪上验证了其高精度和效率。
摘要：
近年来，机器学习（ML）代理模型已成为加速物理和化学过程模拟不可或缺的工具。然而，目前仍缺乏能够准确预测分子振动光谱的机器学习模型。在此，我们提出了一种高效的高维神经网络势（HD-NNP）架构，基于通过机器学习-分子动力学（MD）模拟实时获得的偶极矩和极化率，来准确计算红外（IR）和拉曼光谱。该方法应用于吡嗪，一种典型的多原子发色团。HD-NNP预测的能量完全在化学精度（1 kcal/mol）范围内，且HD-NNP预测的力的误差仅为一种流行的性能优异的机器学习模型所获误差的一半。与从头算参考相比，HD-NNP预测的红外和拉曼光谱频率差异小于8.3 cm^(-1)，红外光谱强度和拉曼光谱的退偏振比均得到了很好的重现。本工作开发的HD-NNP架构凸显了构建高精度NNP以预测分子振动光谱的重要性。
英文摘要：
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 high-dimensional neural network potentials (HD-NNP) architecture to accurately calculate infrared (IR) and Raman spectra based on dipole moments and polarizabilities obtained on-the-fly via ML-molecular dynamics (MD) simulations. The methodology is applied to pyrazine, a prototypical polyatomic chromophore. The HD-NNP predicted energies are well within the chemical accuracy (1 kcal/mol), and the errors for HD-NNP predicted forces are only one-half of those obtained from a popular high-performance ML model. Compared to the ab initio reference, the HD-NNP predicted frequencies of IR and Raman spectra differ only by less than 8.3 cm^(-1), and the intensities of IR spectra and the depolarizaiton ratios of Raman spectra are well reproduced. The HD-NNP architecture developed in this work highlights importance of constructing highly accurate NNPs for predicting molecular vibrational spectra.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
