9 citations · 16 across the 4 of their papers we have counts for
4 papers
Can DFT-trained neural network potentials reproduce structure, solvation, and water-exchange properties in aqueous magnesium solutions?
Sebastian Falkner, Pablo Montero de Hijes, Christoph Dellago +1
Magnesium ions play an essential role in many biological processes but remain challenging to model in biomolecular simulations. Despite considerable scientific effort, classical fo…
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…
Iterative charge equilibration for fourth-generation high-dimensional neural network potentials
Emir Kocer, Andreas Singraber, Jonas A. Finkler +4
Machine learning potentials (MLP) allow to perform large-scale molecular dynamics simulations with about the same accuracy as electronic structure calculations provided that the se…
Machine learning potentials for redox chemistry in solution
Emir Kocer, Redouan El Haouari, Christoph Dellago +1
Machine learning potentials (MLPs) represent atomic interactions with quantum mechanical accuracy offering an efficient tool for atomistic simulations in many fields of science. Ho…