论文标题
同位素对基于扫描功能的深电位分子动力学对液态水的分子结构和电子特性的影响
Isotope effects on molecular structures and electronic properties of liquid water via deep potential molecular dynamics based on SCAN functional
论文作者
论文摘要
Feynman Path-Contegral Deep势分子动力学（PI-DPMD）计算已被用于研究光（H $ _2 $ O）和重水（D $ _2 $ O）的光（H $ _2 $ O）。特别是，根据从严格约束且适当的规范（扫描）交换相关功能获得的基础数据基于从头算数据进行训练。由于氢的质量比Deuteron较轻，因此浅水的特性比核量子效应的影响更大。观察并根据与实验性观察物紧密相关的水的氢键结构和电子性质观察并分析了清晰的同位素效应。液体H $ _2 $ O和D $ _2 $ o的分子结构与从散射实验中提取的数据非常吻合。微妙的同位素对径向分布函数和角度分布函数也得到很好的再现。我们的方法表明，深层神经网络与基于扫描功能的AB始于分子动力学相结合，为对水及其同位素效应进行建模提供了准确的理论工具。
Feynman path-integral deep potential molecular dynamics (PI-DPMD) calculations have been employed to study both light (H$_2$O) and heavy water (D$_2$O) within the isothermal-isobaric ensemble. In particular, the deep neural network is trained based on ab initio data obtained from the strongly constrained and appropriately normed (SCAN) exchange-correlation functional. Because of the lighter mass of hydrogen than deuteron, the properties of light water is more influenced by nuclear quantum effect than those of heavy water. Clear isotope effects are observed and analyzed in terms of hydrogen-bond structure and electronic properties of water that are closely associated with experimental observables. The molecular structures of both liquid H$_2$O and D$_2$O agree well with the data extracted from scattering experiments. The delicate isotope effects on radial distribution functions and angular distribution functions are well reproduced as well. Our approach demonstrates that deep neural network combined with SCAN functional based ab initio molecular dynamics provides an accurate theoretical tool for modeling water and its isotope effects.
