Publications of Matthias Rupp

Langer, M. F.; Knoop, F.; Carbogno, C.; Scheffler, M.; Rupp, M.: Heat flux for semilocal machine-learning potentials. Physical Review B 108 (10), L100302 (2023)

Huo, H.; Rupp, M.: Unified representation of molecules and crystals for machine learning. Machine Learning: Science and Technology 3 (4), 045017 (2022)

Langer, M. F.; Goeßmann, A.; Rupp, M.: Representations of molecules and materials for interpolation of quantum-mechanical simulations via machine learning. npj Computational Materials 8, 41 (2022)

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)

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)

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)

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)

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

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

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)

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)

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)

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)

Rupp, M.: Quantum Mechanics and Machine Learning: Rapid Accurate Interpolation of Electronic Structure Calculations for Molecules and Materials. BASF, Ludwigshafen, Germany (2019)

Rupp, M.: Machine Learning and Quantum Mechanics: Accurate Interpolation of Ab Initio Simulation. Warwick Centre for Predictive Modelling, University of Warwick, Coventry, UK (2019)

Rupp, M.: Machine Learning for Materials. TMS 2018, 147th Annual Meeting & Exhibition, The Minerals, Metals & Materials Society, Phoenix, AZ, USA (2018)

Rupp, M.: Machine Learning for Molecules and Materials: Potential and Limitations of Data-Driven Chemistry. 27th Austin Symposium on Molecular Structure and Dynamics, ASMD, Dallas, TX, USA (2018)

Rupp, M.: Machine Learning for Quantum Mechanics: Interpolation of Electronic Structure Calculations. Seminar, Los Alamos National Laboratory, Los Alamos, NM, USA (2018)

Rupp, M.: Machine Learning for Quantum Mechanics. Data Science Workshop, Scuola Internazionale Superiore di Studi Avanzati, SISSA, Trieste, Italy (2018)

Rupp, M.: Kernel-Based Machine Learning for Materials. BiGmax Workshop 2018 on Big-Data-Driven Materials Science, Kloster Irsee, Irsee, Germany (2018)