Johannes T. Margraf
Affiliations: | Fritz-Haber-Institute of the Max-Planck-Society |
Area:
Quantum Chemistry, Machine LearningGoogle:
"Johannes Margraf"Mean distance: (not calculated yet)
Parents
Sign in to add mentor| Timothy Clark | grad student | University of Erlangen-Nuremberg, Erlangen, Germany | |
| Rodney J. Bartlett | post-doc | ||
| Karsten Reuter | research scientist | (Physics Tree) |
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Publications
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| Keller E, Blum V, Reuter K, et al. (2025) Exploring atom-pairwise and many-body dispersion corrections for the BEEF-vdW functional. The Journal of Chemical Physics. 162 |
| Keller E, Morgenstein J, Reuter K, et al. (2024) Small basis set density functional theory method for cost-efficient, large-scale condensed matter simulations. The Journal of Chemical Physics. 161 |
| Cui M, Reuter K, Margraf JT. (2024) Obtaining Robust Density Functional Tight-Binding Parameters for Solids across the Periodic Table. Journal of Chemical Theory and Computation. 20: 5276-5290 |
| Rein V, Gao H, Heenen HH, et al. (2024) Characterization and Molecular Simulations Reveal the Growth Kinetics of Graphene on Liquid Copper During Chemical Vapor Deposition. Acs Nano |
| Xu W, Diesen E, He T, et al. (2024) Discovering High Entropy Alloy Electrocatalysts in Vast Composition Spaces with Multiobjective Optimization. Journal of the American Chemical Society. 146: 7698-7707 |
| Stocker S, Jung H, Csányi G, et al. (2023) Estimating Free Energy Barriers for Heterogeneous Catalytic Reactions with Machine Learning Potentials and Umbrella Integration. Journal of Chemical Theory and Computation. 19: 6796-6804 |
| Vondrák M, Reuter K, Margraf JT. (2023) q-pac: A Python package for machine learned charge equilibration models. The Journal of Chemical Physics. 159 |
| Chen K, Kunkel C, Cheng B, et al. (2023) Physics-inspired machine learning of localized intensive properties. Chemical Science. 14: 4913-4922 |
| Kube P, Dong J, Bastardo NS, et al. (2022) Green synthesis of propylene oxide directly from propane. Nature Communications. 13: 7504 |
| Staacke CG, Huss T, Margraf JT, et al. (2022) Tackling Structural Complexity in LiS-PS Solid-State Electrolytes Using Machine Learning Potentials. Nanomaterials (Basel, Switzerland). 12 |