Quantum Dynamics of Water from Møller-Plesset Perturbation Theory via a Neural Network Potential (Communications in Computational Chemistry, 2025)
We report the static and dynamical properties of liquid water at the level of second-order Møller-Plesset perturbationtheory (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 aswell as a lower diffusion coefficient at ambient conditions compared to experiments, which may be attributed to the incompletebasis set. A better agreement with experimental structural properties and the diffusion constant are observed at an elevatedtemperature of 340 K from our simulations. Although the high-level electronic structure calculations are expensive, training aneural network potential requires only a few thousand frames. This approach shows great potential, requiring modest humaneffort, and is straightforwardly extensible to other simple liquids.
Keywords: MP2; machine learning; quantum simulation; diffusion coefficient