Browsing Fakultät für Chemie (inkl. GAUSS) by Advisor "Behler, Jörg Prof. Dr."
Now showing items 1-5 of 5
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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 ... -
Reactivity at surfaces with high-dimensional neural network potentials
(2023-07-24)The surface of a material is critical for its properties, especially in heterogeneous catalysis. Surfaces properties can be probed, for example, by H atom scattering. However ab-inito theoretical investigations of H atom ... -
Vibrational Frequencies of Molecular Systems using High Dimensional Neural Network Potentials
(2023-05-08)Computational Chemistry is an important field in chemistry which looks for solu- tions for several questions such as reaction mechanisms, design of experiments and understanding fundamental properties of molecules. For ...