Browsing Fakultät für Chemie (inkl. GAUSS) by Advisor "Behler, Jörg Prof. Dr."
Now showing items 1-3 of 3
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Development of a Generally Applicable Machine Learning Potential with Accurate Long-Range Electrostatic Interactions
(2022-08-16)Machine learning potentials (MLPs) have become an indispensable tool for large-scale atomistic simulations, due to their accuracy comparable with ab-initio methods at considerably reduced computational cost. The development ... -
Investigation of Lithium Manganese Oxides Using High-Dimensional Neural Networks
(2022-03-03)Unraveling the atomic and electronic processes in modern energy materials is a key to advances in many important applications, from battery technology to heterogeneous catalysis. A prominent electrode material in lithium ... -
Neural Network Potential Simulations of Copper Supported on Zinc Oxide Surfaces
(2021-09-28)Heterogeneous catalysis is an area of active research, because many industrially relevant reactions involve gaseous reactants and are accelerated by solid phase catalysts. In recent years, activity in the field has become ...