41 citations · 49 across the 5 of their papers we have counts for
5 papers
RuNNer 2.0: A Software Suite for High-Dimensional Neural Network Potentials
Alexander L. M. Knoll, Moritz R. Schäfer, K. Nikolas Lausch +11
We present RuNNer 2.0, the "Ruhr University Neural Network energy representation", a highly optimized software suite for training and evaluating high-dimensional neural network pot…
Atomistic Simulations of Oxide-Water Interfaces using Machine Learning Potentials
Jan Elsner, K Nikolas Lausch, Jörg Behler
Oxide-water interfaces govern a wide range of physical and chemical processes fundamental to many fields like catalysis, geochemistry, corrosion, electrochemistry, and sensor techn…
Impact of the damping function in dispersion-corrected density functional theory on the properties of liquid water
K. Nikolas Lausch, Redouan El Haouari, Daniel Trzewik +1
Accounting for dispersion interactions is essential in approximate density functional theory (DFT). Often, a correction potential based on the London formula is added, which is dam…
Machine Learning Potentials for Heterogeneous Catalysis
Amir Omranpour, Jan Elsner, K. Nikolas Lausch +1
The sustainable production of many bulk chemicals relies on heterogeneous catalysis. The rational design or improvement of the required catalysts critically depends on insights int…
Predicting Oxidation and Spin States by High-Dimensional Neural Networks: Applications to Lithium Manganese Oxide Spinels
Marco Eckhoff, Knut Nikolas Lausch, Peter E. Blöchl +1
Lithium ion batteries often contain transition metal oxides like LiMnO (). Depending on the Li content different ratios of Mn to Mn$^\text{…