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 | 80 | 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 | 8 | 2023 |
Stress and heat flux via automatic differentiation MF Langer, JT Frank, F Knoop The Journal of Chemical Physics 159 (17), 2023 | 4 | 2023 |
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 | | |