| From molecular fragments to the bulk: development of a neural network potential for MOF-5 M Eckhoff, J Behler Journal of chemical theory and computation 15 (6), 3793-3809, 2019 | 107 | 2019 |
| High-dimensional neural network potentials for magnetic systems using spin-dependent atom-centered symmetry functions M Eckhoff, J Behler npj Computational Materials 7 (1), 170, 2021 | 53 | 2021 |
| Predicting oxidation and spin states by high-dimensional neural networks: Applications to lithium manganese oxide spinels M Eckhoff, KN Lausch, PE Blöchl, J Behler The Journal of Chemical Physics 153 (16), 2020 | 34 | 2020 |
| Closing the gap between theory and experiment for lithium manganese oxide spinels using a high-dimensional neural network potential M Eckhoff, F Schönewald, M Risch, CA Volkert, PE Blöchl, J Behler Physical Review B 102 (17), 174102, 2020 | 31 | 2020 |
| Insights into lithium manganese oxide-water interfaces using machine learning potentials M Eckhoff, J Behler The Journal of Chemical Physics 155 (24), 244703, 2021 | 25 | 2021 |
| Strained hydrogen bonding in imidazole trimer: A combined infrared, Raman, and theory study T Forsting, J Zischang, MA Suhm, M Eckhoff, B Schröder, RA Mata Physical Chemistry Chemical Physics 21 (11), 5989-5998, 2019 | 19 | 2019 |
| Lifelong machine learning potentials M Eckhoff, M Reiher Journal of Chemical Theory and Computation 19 (12), 3509-3525, 2023 | 18 | 2023 |
| Hybrid density functional theory benchmark study on lithium manganese oxides M Eckhoff, PE Blöchl, J Behler Physical Review B 101 (20), 205113, 2020 | 18 | 2020 |
| The Guinness molecules for the carbohydrate formula J Altnöder, K Krüger, D Borodin, L Reuter, D Rohleder, F Hecker, ... The Chemical Record 14 (6), 1116-1133, 2014 | 10 | 2014 |
| Structure and thermodynamics of metal clusters on atomically smooth substrates M Eckhoff, D Schebarchov, DJ Wales The Journal of Physical Chemistry Letters 8 (21), 5402-5407, 2017 | 8 | 2017 |
| A full additive QM/MM scheme for the computation of molecular crystals with extension to many-body expansions TL Teuteberg, M Eckhoff, RA Mata The Journal of Chemical Physics 150 (15), 2019 | 7 | 2019 |
| SCINE—Software for chemical interaction networks T Weymuth, JP Unsleber, PL Türtscher, M Steiner, JG Sobez, CH Müller, ... The Journal of Chemical Physics 160 (22), 2024 | 3 | 2024 |
| A criticial view on e occupancy as a descriptor for oxygen evolution catalytic activity in LiMnO nanoparticles F Schönewald, M Eckhoff, M Baumung, M Risch, PE Blöchl, J Behler, ... arXiv preprint arXiv:2007.04217, 2020 | 3 | 2020 |
| CoRe optimizer: an all-in-one solution for machine learning M Eckhoff, M Reiher Machine Learning: Science and Technology 5 (1), 015018, 2024 | 2 | 2024 |
| ReiherGroup/CoRe_optimizer: Release 1.0. 0 M Eckhoff, M Reiher | 1 | 2024 |
| NEAR: A Training-Free Pre-Estimator of Machine Learning Model Performance RT Husistein, M Reiher, M Eckhoff arXiv preprint arXiv:2408.08776, 2024 | | 2024 |
| ReiherGroup/NEAR: Release 1.0. 0 RT Husistein, M Reiher, M Eckhoff | | 2024 |
| Investigation of Lithium Manganese Oxides Using High-Dimensional Neural Networks M Eckhoff Georg-August-Universität Göttingen, 2022 | | 2022 |
Jörg BehlerRuhr-Universität BochumVerified email at ruhr-uni-bochum.de
