| Representations of molecules and materials for interpolation of quantum-mechanical simulations via machine learning MF Langer, A Goeßmann, M Rupp npj Computational Materials 8 (1), 41, 2022 | 114 | 2022 |
| Heat flux for semilocal machine-learning potentials MF Langer, F Knoop, C Carbogno, M Scheffler, M Rupp Physical Review B 108 (10), L100302, 2023 | 12 | 2023 |
| Stress and heat flux via automatic differentiation MF Langer, JT Frank, F Knoop The Journal of Chemical Physics 159 (17), 2023 | 10 | 2023 |
| Probing the effects of broken symmetries in machine learning MF Langer, SN Pozdnyakov, M Ceriotti Machine Learning: Science and Technology 5 (4), 04LT01, 2024 | 1 | 2024 |
| Fast and flexible range-separated models for atomistic machine learning P Loche, KK Huguenin-Dumittan, M Honarmand, Q Xu, E Rumiantsev, ... arXiv preprint arXiv:2412.03281, 2024 | | 2024 |
| Workflows for Artificial Intelligence J Behler, G Csányi, L Foppa, K Kang, MF Langer, JT Margraf, AS Nair, ... | | 2024 |
| Machine learning for atomistic modeling: representations and thermal transport MF Langer Technische Universität Berlin, 2023 | | 2023 |
| Thermal Transport with Message Passing Neural Networks via the Green-Kubo Method M Langer, F Knoop, C Carbogno, M Scheffler, M Rupp APS March Meeting Abstracts 2022, Z32. 010, 2022 | | 2022 |
| Green-Kubo Thermal Conductivities with Message-Passing Neural Networks M Langer, F Knoop, C Carbogno, M Scheffler, M Rupp Bulletin of the American Physical Society, 2021 | | 2021 |
| Exact representations of molecules and materials for accurate interpolation of ab initio simulations M Langer, A Goeßmann, M Rupp Report on the Workshop “Developing High-Dimensional Potential Energy …, 0 | | |
| Representing Molecules and Materials for Accurate Interpolation of Quantum-Mechanical Calculations MF Langer DPG Spring Meeting, 0 | | |
Matthias RuppLuxembourg Institute of Science and Technology (LIST)Verified email at list.lu
