Loading [MathJax]/jax/output/HTML-CSS/config.js
Volume 7, Issue 2
Quantum Dynamics of Water from Møller-Plesset Perturbation Theory via a Neural Network Potential

Mengxu Li, Jinggang Lan, David M. Wilkins, Vladimir V. Rybkin, Marcella Iannuzzi & Jürg Hutter

Commun. Comput. Chem., 7 (2025), pp. 88-96.

Published online: 2025-06

[An open-access article; the PDF is free to any online user.]

Export citation
  • 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.

  • AMS Subject Headings

  • Copyright

COPYRIGHT: © Global Science Press

  • Email address
  • BibTex
  • RIS
  • TXT
@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 = {

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.

}, issn = {2617-8575}, doi = {https://doi.org/10.4208/cicc.2025.88.01}, url = {http://global-sci.org/intro/article_detail/cicc/24177.html} }
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 -

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.

Li , MengxuLan , JinggangWilkins , David M.Rybkin , Vladimir V.Iannuzzi , Marcella and Hutter , Jürg. (2025). Quantum Dynamics of Water from Møller-Plesset Perturbation Theory via a Neural Network Potential. Communications in Computational Chemistry. 7 (2). 88-96. doi:10.4208/cicc.2025.88.01
Copy to clipboard
The citation has been copied to your clipboard