Publications of Matthias Rupp

Journal Article (10)

2022
Journal Article
Langer, M.F., 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).
2020
Journal Article
Sutton, C.A., 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).
2019
Journal Article
Nyshadham, C., 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
Stuke, A., 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).
2016
Journal Article
Li, L., 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).
2015
Journal Article
Rupp, M.: Special issue on machine learning and quantum mechanics. International Journal of Quantum Chemistry 115 (16), 1003–1004 (2015).
Journal Article
Rupp, M.: Machine learning for quantum mechanics in a nutshell. International Journal of Quantum Chemistry 115 (16), 1058–1073 (2015).
Journal Article
Rupp, M., 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
Snyder, J.C., 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).
2012
Journal Article
Pozun, Z.D., 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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