Publications of Matthias Rupp
All genres
Journal Article (10)
Journal Article
8, 41 (2022)
Representations of molecules and materials for interpolation of quantum-mechanical simulations via machine learning. npj Computational Materials
Journal Article
11, 4428 (2020)
Identifying domains of applicability of machine learning models for materials science. Nature Communications
Journal Article
150 (20), 204121 (2019)
Chemical diversity in molecular orbital energy predictions with kernel ridge regression. The Journal of Chemical Physics
Journal Article
5, 51 (2019)
Machine-learned multi-system surrogate models for materials prediction. npj Computational Materials
Journal Article
116 (11), pp. 819 - 833 (2016)
Understanding machine-learned density functionals. International Journal of Quantum Chemistry
Journal Article
6 (16), pp. 3309 - 3313 (2015)
Machine Learning for Quantum Mechanical Properties of Atoms in Molecules. The Journal of Physical Chemistry Letters
Journal Article
115 (16), pp. 1003 - 1004 (2015)
Special issue on machine learning and quantum mechanics. International Journal of Quantum Chemistry
Journal Article
115 (16), pp. 1058 - 1073 (2015)
Machine learning for quantum mechanics in a nutshell. International Journal of Quantum Chemistry
Journal Article
115 (16), pp. 1102 - 1114 (2015)
Nonlinear gradient denoising: Finding accurate extrema from inaccurate functional derivatives. International Journal of Quantum Chemistry
Journal Article
136 (17), 174101 (2012)
Optimizing transition states via kernel-based machine learning. The Journal of Chemical Physics Talk (36)
Talk
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
Quantum Mechanics and Machine Learning: Rapid Accurate Interpolation of Electronic Structure Calculations for Molecules and Materials. BASF, Ludwigshafen, Germany (2019)
Talk
Machine Learning and Quantum Mechanics: Accurate Interpolation of Ab Initio Simulation. Warwick Centre for Predictive Modelling, University of Warwick, Coventry, UK (2019)
Talk
Machine Learning for Quantum Chemistry. The Löwdin Lectures, Uppsala University, Uppsala, Sweden (2018)
Talk
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
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
Accurate Energy Predictions via Machine Learning. Conference on Quantum Machine Learning (QML+ 2018), Innsbruck, Austria (2018)
Talk
Accurate Energy Predictions for Materials. Seminar, State Key Laboratory of Superhard Materials, College of Physics, Jilin University, Changchun, China (2018)
Talk
Kernel Methods in Machine Learning. Hands-On DFT and Beyond: Frontiers of Advanced Electronic Structure and Molecular Dynamics Methods, Beijing, China (2018)
Talk
High-Throughput Energy Predictions for Molecules and Materials via Machine Learning. Workshop: Modern Approaches to Coupling Scales in Materials Simulations, Lenggries, Germany (2018)