Publications of Matthias Rupp

Journal Article (9)

Journal Article
Sutton, C. A.; Boley, M.; Ghiringhelli, L. M.; Rupp, M.; Vreeken, J.; Scheffler, M.: Identifying domains of applicability of machine learning models for materials science. Nature Communications 11, 4428 (2020)
Journal Article
Stuke, A.; Todorović, M.; Rupp, M.; Kunkel, C.; Ghosh, K.: Chemical diversity in molecular orbital energy predictions with kernel ridge regression. The Journal of Chemical Physics 150 (20), 204121 (2019)
Journal Article
Nyshadham, C.; Rupp, M.; Bekker, B.; Shapeev, A. V.; Mueller, T.; Rosenbrock, C. W.; Csányi, G.; Wingate, D. W.; Hart, G. L. W.: Machine-learned multi-system surrogate models for materials prediction. npj Computational Materials 5, 51 (2019)
Journal Article
Li, L.; Snyder, J. C.; Pelaschier, I. M.; Huang, J.; Niranjan, U.; Duncan, P.; Rupp, M.; Müller, K.-R.; Burke, K.: Understanding machine-learned density functionals. International Journal of Quantum Chemistry 116 (11), pp. 819 - 833 (2016)
Journal Article
Rupp, M.; Ramakrishnan, R.; von Lilienfeld, O. A.: Machine Learning for Quantum Mechanical Properties of Atoms in Molecules. The Journal of Physical Chemistry Letters 6 (16), pp. 3309 - 3313 (2015)
Journal Article
Rupp, M.: Special issue on machine learning and quantum mechanics. International Journal of Quantum Chemistry 115 (16), pp. 1003 - 1004 (2015)
Journal Article
Rupp, M.: Machine learning for quantum mechanics in a nutshell. International Journal of Quantum Chemistry 115 (16), pp. 1058 - 1073 (2015)
Journal Article
Snyder, J. C.; Rupp, M.; Müller, K.-R.; Burke, K.: Nonlinear gradient denoising: Finding accurate extrema from inaccurate functional derivatives. International Journal of Quantum Chemistry 115 (16), pp. 1102 - 1114 (2015)
Journal Article
Pozun, Z. D.; Hansen, K.; Sheppard, D.; Rupp, M.; Müller, K.-R.; Henkelman, G.: Optimizing transition states via kernel-based machine learning. The Journal of Chemical Physics 136 (17), 174101 (2012)

Talk (36)

Talk
Rupp, M.: Exact Representations of Molecules and Materials for Accurate Interpolation of Ab Initio Simulations. Workshop, Developing High-Dimensional Potential Energy Surfaces – From the Gas Phase to Materials, Georg-August-Universität Göttingen, Göttingen, Germany (2019)
Talk
Rupp, M.: Quantum Mechanics and Machine Learning: Rapid Accurate Interpolation of Electronic Structure Calculations for Molecules and Materials. BASF, Ludwigshafen, Germany (2019)
Talk
Rupp, M.: Machine Learning and Quantum Mechanics: Accurate Interpolation of Ab Initio Simulation. Warwick Centre for Predictive Modelling, University of Warwick, Coventry, UK (2019)
Talk
Rupp, M.: Machine Learning for Quantum Chemistry. The Löwdin Lectures, Uppsala University, Uppsala, Sweden (2018)
Talk
Rupp, M.: Accurate Interpolation of Ab Initio Calculations with Machine Learning. Sackler-CECAM school and workshop on Frontiers in Molecular Dynamics: Machine Learning, Deep Learning and Coarse Graining, Tel Aviv, Israel (2018)
Talk
Rupp, M.: Accurate Energy Predictions for Materials and Molecules via Machine Learning. E-CAM Workshop, Improving the accuracy of ab-initio predictions for materials, Paris, France (2018)
Talk
Rupp, M.: Accurate Energy Predictions via Machine Learning. Conference on Quantum Machine Learning (QML+ 2018), Innsbruck, Austria (2018)
Talk
Rupp, M.: Accurate Energy Predictions for Materials. Seminar, State Key Laboratory of Superhard Materials, College of Physics, Jilin University, Changchun, China (2018)
Talk
Rupp, M.: Kernel Methods in Machine Learning. Hands-On DFT and Beyond: Frontiers of Advanced Electronic Structure and Molecular Dynamics Methods, Beijing, China (2018)
Talk
Rupp, M.: High-Throughput Energy Predictions for Molecules and Materials via Machine Learning. Workshop: Modern Approaches to Coupling Scales in Materials Simulations, Lenggries, Germany (2018)
Talk
Rupp, M.: Machine Learning for Interpolation of Electronic Structure Calculations. The First International Conference on Machine Learning and Physics, Beijing, China (2018)
Go to Editor View