Publications of Matthias Rupp

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).

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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).

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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).

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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).

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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).

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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).

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Journal Article

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

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Journal Article

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

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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).

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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).

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