Computation of the heat capacity of water from first principles

Shiga, Motoyuki; Elsner, J.*; Behler, J.*; Thomsen, B.

Journal of Chemical Physics, 163(13), p.134119_1 - 134119_13, 2025/10

https://doi.org/10.1063/5.0285698

Water possesses unique properties such as a high heat capacity, playing a crucial role in biological and climatic processes. To understand the microscopic origin of its heat capacity from first principles, highly accurate path integral molecular dynamics (PIMD) simulations that include nuclear quantum effects are required; however, such simulations are computationally demanding. In this study, we address this challenge by employing high-dimensional neural network potentials (HDNNPs) constructed from density functional theory (DFT) calculations. Additionally, we introduce an efficient PIMD algorithm that improves computational performance. Using this approach, we successfully obtain converged data for the heat capacity. In particular, results based on the revPBE0-D3 functional show excellent agreement with experimental data, demonstrating that this method is effective for the quantitative understanding of the thermodynamic properties of water.