| Surface Complexions Identified through Machine Learning and Surface Investigations J Timmermann, F Kraushofer, N Resch, P Li, Y Wang, Z Mao, M Riva, ... Physical review letters 125 (20), 206101, 2020 | 54 | 2020 |
| On the role of long-range electrostatics in machine-learned interatomic potentials for complex battery materials CG Staacke, HH Heenen, C Scheurer, G Csányi, K Reuter, JT Margraf ACS Applied Energy Materials 4 (11), 12562-12569, 2021 | 38 | 2021 |
| Data-efficient iterative training of Gaussian approximation potentials: application to surface structure determination of rutile IrO2 and RuO2 J Timmermann, Y Lee, CG Staacke, JT Margraf, C Scheurer, K Reuter The Journal of Chemical Physics 155 (24), 2021 | 32 | 2021 |
| Kernel charge equilibration: efficient and accurate prediction of molecular dipole moments with a machine-learning enhanced electron density model CG Staacke, S Wengert, C Kunkel, G Csányi, K Reuter, JT Margraf Machine Learning: Science and Technology 3 (1), 015032, 2022 | 30 | 2022 |
| Tackling Structural Complexity in Li2S-P2S5 Solid-State Electrolytes Using Machine Learning Potentials CG Staacke, T Huss, JT Margraf, K Reuter, C Scheurer Nanomaterials 12 (17), 2950, 2022 | 14 | 2022 |
| The Electrostatic Gap: Combining Electrostatic Models with Machine Learning Potentials CG Staacke Technische Universität München, 2022 | | 2022 |
| Investigations of the Polysulfide Conversion Mechanism via Gaussian Approximation Potentials X Han, CG Staacke, HH Heenen, X Xu, K Reuter | | |
Karsten ReuterDirector at Fritz-Haber-Institut der MPGVerified email at fhi-berlin.mpg.de