Department of Physics, Princeton University, Princeton, NJ 08544, USA.
Phys Chem Chem Phys. 2020 May 21;22(19):10592-10602. doi: 10.1039/d0cp01893g. Epub 2020 May 7.
We introduce a scheme based on machine learning and deep neural networks to model the environmental dependence of the electronic polarizability in insulating materials. Application to liquid water shows that training the network with a relatively small number of molecular configurations is sufficient to predict the polarizability of arbitrary liquid configurations in close agreement with ab initio density functional theory calculations. In combination with a neural network representation of the interatomic potential energy surface, the scheme allows us to calculate the Raman spectra along 2-nanosecond classical trajectories at different temperatures for HO and DO. The vast gains in efficiency provided by the machine learning approach enable longer trajectories and larger system sizes relative to ab initio methods, reducing the statistical error and improving the resolution of the low-frequency Raman spectra. Decomposing the spectra into intramolecular and intermolecular contributions elucidates the mechanisms behind the temperature dependence of the low-frequency and stretch modes.
我们提出了一种基于机器学习和深度神经网络的方案，用于模拟绝缘材料中电子极化率的环境依赖性。将该方案应用于液态水，结果表明，使用相对较少的分子构形对网络进行训练足以预测任意液态构形的极化率，与从头算密度泛函理论计算结果非常吻合。结合原子间势能表面的神经网络表示，该方案允许我们沿着不同温度的 2 纳秒经典轨迹计算 HO 和 DO 的拉曼光谱。与从头算方法相比，机器学习方法提供了巨大的效率提升，使得可以进行更长的轨迹和更大的体系尺寸模拟，从而减少统计误差并提高低频拉曼光谱的分辨率。将光谱分解为分子内和分子间贡献，阐明了低频和伸缩模式随温度变化的机制。
