arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 2004.07369physics.chem-phcond-mat.mtrl-sciphysics.comp-ph
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基于深层神经网络的液态水拉曼光谱和极化率研究

Raman Spectrum and Polarizability of Liquid Water from Deep Neural Networks

Grace M. Sommers, Marcos F. Calegari Andrade, Linfeng Zhang, Han Wang, Roberto Car

摘要:

我们介绍了一种基于机器学习和深度神经网络的方案,以模拟绝缘材料中电子极化率的环境依赖性。液态水的应用表明,训练网络与相对较少的分子配置是足够的预测任意液体配置的极化率与从头计算密度泛函理论计算密切一致。结合原子间势能面的神经网络表示,该方案使我们能够计算拉曼光谱沿2纳秒经典轨迹在不同温度下的H2O和D2O。相对于从头算方法,机器学习方法提供的效率的巨大收益使更长的轨迹和更大的系统尺寸成为可能,从而减少了统计误差并提高了低频拉曼光谱的分辨率。将光谱分解为分子内和分子间的贡献阐明了低频和拉伸模式的温度依赖性背后的机制。

英文摘要:

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 H2O and D2O. 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.

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