6 citations · 9 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…
Molecular Dynamics Simulations of -Belite(010)-Water Interfaces with High-Dimensional Neural Network Potentials
Bernadeta Prus, Jörg Behler
Belite -- dicalcium silicate CaSiO -- is a main constituent of low-carbon cement. In this work, we study several terminations of the (010) surface of -belite, its most s…
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…
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…
Properties of -Brass Nanoparticles I: Neural Network Potential Energy Surface
Jan Weinreich, Anton Römer, Martín Leandro Paleico +1
Binary metal clusters are of high interest for applications in heterogeneous catalysis and have received much attention in recent years. To gain insights into their structure and c…