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cond-mat.soft2026
Dynamical properties of ab initio water from machine-learning potentials
P. Montero de Hijes, L. Neubeck, G. Kresse +1
We assess the dynamical properties of liquid water predicted by several density functionals using machine-learning interatomic potentials. MACE models were trained for SCAN, RPBE-D…
cond-mat.soft2023★ 1 cited
Comparing machine learning potentials for water: Kernel-based regression and Behler-Parrinello neural networks
Pablo Montero de Hijes, Christoph Dellago, Ryosuke Jinnouchi +2
In this paper we investigate the performance of different machine learning potentials (MLPs) in predicting key thermodynamic properties of water using RPBE+D3. Specifically, we scr…