TY - JOUR
T1 - Quantum Dynamics of Water from Møller-Plesset Perturbation Theory via a Neural Network Potential
AU - Li , Mengxu
AU - Lan , Jinggang
AU - Wilkins , David M.
AU - Rybkin , Vladimir V.
AU - Iannuzzi , Marcella
AU - Hutter , Jürg
JO - Communications in Computational Chemistry
VL - 2
SP - 88
EP - 96
PY - 2025
DA - 2025/06
SN - 7
DO - http://doi.org/10.4208/cicc.2025.88.01
UR - https://global-sci.org/intro/article_detail/cicc/24177.html
KW - MP2, machine learning, quantum simulation, diffusion coefficient.
AB - <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>