Year: 2025
Author: Mengxu Li, Jinggang Lan, David M. Wilkins, Vladimir V. Rybkin, Marcella Iannuzzi, Jürg Hutter
Communications in Computational Chemistry, Vol. 7 (2025), Iss. 2 : pp. 88–96
Abstract
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.
Journal Article Details
Publisher Name: Global Science Press
Language: English
DOI: https://doi.org/10.4208/cicc.2025.88.01
Communications in Computational Chemistry, Vol. 7 (2025), Iss. 2 : pp. 88–96
Published online: 2025-01
AMS Subject Headings: Global Science Press
Copyright: COPYRIGHT: © Global Science Press
Pages: 9
Keywords: MP2 machine learning quantum simulation diffusion coefficient.