Publications of Matthias Rupp

Journal Article (10)

Journal Article
M.F. Langer, A. Goeßmann and M. Rupp: Representations of molecules and materials for interpolation of quantum-mechanical simulations via machine learning. npj Computational Materials 8, 41 (2022).
Journal Article
C.A. Sutton, M. Boley, L.M. Ghiringhelli, M. Rupp, J. Vreeken and M. Scheffler: Identifying domains of applicability of machine learning models for materials science. Nature Communications 11, 4428 (2020).
Journal Article
A. Stuke, M. Todorović, M. Rupp, C. Kunkel and K. Ghosh: Chemical diversity in molecular orbital energy predictions with kernel ridge regression. The Journal of Chemical Physics 150 (20), 204121 (2019).
Journal Article
C. Nyshadham, M. Rupp, B. Bekker, A.V. Shapeev, T. Mueller, C.W. Rosenbrock, G. Csányi, D.W. Wingate and G.L.W. Hart: Machine-learned multi-system surrogate models for materials prediction. npj Computational Materials 5, 51 (2019).
Journal Article
L. Li, J.C. Snyder, I.M. Pelaschier, J. Huang, U.N. Niranjan, P. Duncan, M. Rupp, K.-R. Müller and K. Burke: Understanding machine-learned density functionals. International Journal of Quantum Chemistry 116 (11), 819–833 (2016).
Journal Article
M. Rupp, R. Ramakrishnan and 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).
Journal Article
M. Rupp: Special issue on machine learning and quantum mechanics. International Journal of Quantum Chemistry 115 (16), 1003–1004 (2015).
Journal Article
M. Rupp: Machine learning for quantum mechanics in a nutshell. International Journal of Quantum Chemistry 115 (16), 1058–1073 (2015).
Journal Article
J.C. Snyder, M. Rupp, K.-R. Müller and K. Burke: Nonlinear gradient denoising: Finding accurate extrema from inaccurate functional derivatives. International Journal of Quantum Chemistry 115 (16), 1102–1114 (2015).
Journal Article
Z.D. Pozun, K. Hansen, D. Sheppard, M. Rupp, K.-R. Müller and G. Henkelman: Optimizing transition states via kernel-based machine learning. The Journal of Chemical Physics 136 (17), 174101 (2012).

Talk (36)

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