论文标题
RPBE-D3水/蒸气界面的Ab-Initio结构和热力学通过神经网络分子动力学
Ab-initio Structure and Thermodynamics of the RPBE-D3 Water/Vapor Interface by Neural-Network Molecular Dynamics
论文作者
论文摘要
在RPBE近似校正以易于分散的RPBE近似中的密度功能理论（DFT）势能景观的神经网络表示的帮助下，我们计算了其液体/蒸气界面的几种结构和热力学特性。神经网络速度使我们能够以前所未有的精度沿其液体/蒸气共存线采样水的特性所需的大小和时间尺度间隙。
Aided by a neural network representation of the density functional theory (DFT) potential energy landscape of water in the RPBE approximation corrected for dispersion, we calculate several structural and thermodynamic properties of its liquid/vapor interface. The neural network speed allows us to bridge the size and time scale gaps required to sample the properties of water along its liquid/vapor coexistence line with unprecedented precision.
