6 citations · 9 across the 5 of their papers we have counts for
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physics.chem-ph2024★ 3 cited
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…
physics.chem-ph2024★ 6 cited
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…