@Article{CiCC-7-88,
author = {Li , MengxuLan , JinggangWilkins , David M.Rybkin , Vladimir V.Iannuzzi , Marcella and Hutter , Jürg},
title = {Quantum Dynamics of Water from Møller-Plesset Perturbation Theory via a Neural Network Potential},
journal = {Communications in Computational Chemistry},
year = {2025},
volume = {7},
number = {2},
pages = {88--96},
abstract = {<p style="text-align: justify;">We report the static and dynamical properties of liquid water at the level of second-order Møller-Plesset perturbation
theory (MP2) with classical and quantum nuclear dynamics using a neural network potential. We examined the temperature-dependent radial distribution functions, diffusion, and vibrational dynamics. MP2 theory predicts over-structured liquid water as
well as a lower diffusion coefficient at ambient conditions compared to experiments, which may be attributed to the incomplete
basis set. A better agreement with experimental structural properties and the diffusion constant are observed at an elevated
temperature of 340 K from our simulations. Although the high-level electronic structure calculations are expensive, training a
neural network potential requires only a few thousand frames. This approach shows great potential, requiring modest human
effort, and is straightforwardly extensible to other simple liquids.</p>},
issn = {2617-8575},
doi = {https://doi.org/10.4208/cicc.2025.88.01},
url = {http://global-sci.org/intro/article_detail/cicc/24177.html}
}