Publikationen von Matthias Rupp

Langer, M.F., F. Knoop, C. Carbogno, M. Scheffler und M. Rupp: Heat flux for semilocal machine-learning potentials. Physical Review B 108 (10), L100302 (2023).

Huo, H. und M. Rupp: Unified representation of molecules and crystals for machine learning. Machine Learning: Science and Technology 3 (4), 045017 (2022).

Langer, M.F., A. Goeßmann und M. Rupp: Representations of molecules and materials for interpolation of quantum-mechanical simulations via machine learning. npj Computational Materials 8, 41 (2022).

Sutton, C.A., M. Boley, L.M. Ghiringhelli, M. Rupp, J. Vreeken und M. Scheffler: Identifying domains of applicability of machine learning models for materials science. Nature Communications 11, 4428 (2020).

Nyshadham, C., M. Rupp, B. Bekker, A.V. Shapeev, T. Mueller, C.W. Rosenbrock, G. Csányi, D.W. Wingate und G.L.W. Hart: Machine-learned multi-system surrogate models for materials prediction. npj Computational Materials 5, 51 (2019).

Stuke, A., M. Todorović, M. Rupp, C. Kunkel und K. Ghosh: Chemical diversity in molecular orbital energy predictions with kernel ridge regression. The Journal of Chemical Physics 150 (20), 204121 (2019).

Li, L., J.C. Snyder, I.M. Pelaschier, J. Huang, U.N. Niranjan, P. Duncan, M. Rupp, K.-R. Müller und K. Burke: Understanding machine-learned density functionals. International Journal of Quantum Chemistry 116 (11), 819–833 (2016).

Rupp, M.: Special issue on machine learning and quantum mechanics. International Journal of Quantum Chemistry 115 (16), 1003–1004 (2015).

Rupp, M.: Machine learning for quantum mechanics in a nutshell. International Journal of Quantum Chemistry 115 (16), 1058–1073 (2015).

Rupp, M., R. Ramakrishnan und O.A. von Lilienfeld: Machine Learning for Quantum Mechanical Properties of Atoms in Molecules. The Journal of Physical Chemistry Letters 6 (16), 3309–3313 (2015).

Snyder, J.C., M. Rupp, K.-R. Müller und K. Burke: Nonlinear gradient denoising: Finding accurate extrema from inaccurate functional derivatives. International Journal of Quantum Chemistry 115 (16), 1102–1114 (2015).

Pozun, Z.D., K. Hansen, D. Sheppard, M. Rupp, K.-R. Müller und G. Henkelman: Optimizing transition states via kernel-based machine learning. The Journal of Chemical Physics 136 (17), 174101 (2012).

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