93 citations · 363 across the 16 of their papers we have counts for
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cond-mat.mtrl-sci2021★ 54 cited
Insights into lithium manganese oxide-water interfaces using machine learning potentials
Marco Eckhoff, Jörg Behler
Unraveling the atomistic and the electronic structure of solid-liquid interfaces is the key to the design of new materials for many important applications, from heterogeneous catal…
physics.comp-ph2021★ 93 cited
High-Dimensional Neural Network Potentials for Magnetic Systems Using Spin-Dependent Atom-Centered Symmetry Functions
Marco Eckhoff, Jörg Behler
Machine learning potentials have emerged as a powerful tool to extend the time and length scales of first principles-quality simulations. Still, most machine learning potentials ca…