2 citations · 4 across the 8 of their papers we have counts for
3 papers · 1 filter
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…
Scalar machine learning of tensorial quantities -- Born effective charges from monopole models
Bernhard Schmiedmayer, Angela Rittsteuer, Tobias Hilpert +1
Predicting tensorial properties with machine learning models typically requires carefully designed tensorial descriptors. In this work, we introduce an alternative strategy for lea…
Accurate Thermophysical Properties of Water using Machine-Learned Potentials
Tobias Hilpert, Georg Kresse
Simulating water from first principles remains a significant computational challenge due to the slow dynamics of the underlying system. Although machine-learned interatomic potenti…