Matthias Rupp

Machine learning for atomistic systems

Publications

Journal articles

  1. Vaidish Sumaria, Takat B. Rawal, Young Feng Li, David Sommer, Jake Vikoren, Robert J. Bondi, Matthias Rupp, Amrit Prasad, Deeptanshu Prasad: Machine Learning, Density Functional Theory, and Experiments to Understand the Photocatalytic Reduction of CO2 by CuPt/TiO2, arXiv: 2402.08884, Cornell University, 2024. [url]
  2. Marcel F. Langer, Florian Knoop, Christian Carbogno, Matthias Scheffler, Matthias Rupp: Heat Flux for Semilocal Machine-Learning Potentials, Physical Review B, 108(10): L100302, American Physical Society, 2023. [doi] [pdf]
  3. Stephen R. Xie, Matthias Rupp, Richard G. Hennig: Ultra-fast Interpretable Machine-Learning Potentials, npj Computational Materials, 9: 162, Springer, 2023. [doi] [pdf]
  4. Haoyan Huo, Matthias Rupp: Unified Representation of Molecules and Crystals for Machine Learning, Machine Learning: Science & Technology, 3(4): 045017, 2022. [doi] [pdf]
  5. Marcel F. Langer, Alex Goeßmann, Matthias Rupp: Representations of Molecules and Materials for Interpolation of Quantum-Mechanical Simulations via Machine Learning, npj Computational Materials, 8: 41, Nature Springer, 2022. [doi] [pdf]
  6. Christopher Sutton, Mario Boley, Luca M. Ghiringhelli, Matthias Rupp, Jilles Vreeken, Matthias Scheffler: Identifying Domains of Applicability of Machine Learning Models for Materials Science, Nature Communications, 11: 4428, Springer Nature, 2020. [doi]
  7. Zachary del Rosario, Matthias Rupp, Yoolhee Kim, Erin Antono, Julia Ling: Assessing the Frontier: Active Learning, Model Accuracy, and Multi-Objective Candidate Discovery and Optimization, The Journal of Chemical Physics, 153(2): 024112, American Institute of Physics, 2020. [doi]
  8. Annika Stuke, Milica Todorović, Matthias Rupp, Christian Kunkel, Kunal Ghosh, Lauri Himanen, Patrick Rinke: Chemical Diversity in Molecular Orbital Energy Predictions with Kernel Ridge Regression, The Journal of Chemical Physics, 150(20): 204121, American Institute of Physics, 2019. [doi] [pdf]
  9. Chandramouli Nyshadham, Matthias Rupp, Brayden Bekker, Alexander V. Shapeev, Tim Mueller, Conrad W. Rosenbrock, Gábor Csányi, David W. Wingate, Gus L.W. Hart: Machine-Learned Multi-System Surrogate Models for Materials Prediction, Nature Partner Journal Computational Materials, 5: 51, Springer Nature, 2019. [doi] [pdf]
  10. Matthias Rupp, O. Anatole von Lilienfeld, Kieron Burke: Guest Editorial: Special Topic on Data-Enabled Theoretical Chemistry, Journal of Chemical Physics, 148(24): 241401, American Institute of Physics, 2018. [doi] [pdf]
  11. Li Li, John C. Snyder, Isabelle M. Pelaschier, Jessica Huang, Uma-Naresh Niranjan, Paul Duncan, Matthias Rupp, Klaus-Robert Müller, Kieron Burke: Understanding Machine-Learned Density Functionals, International Journal of Quantum Chemistry, 116(11): 819–833, Wiley, 2016. [doi]
  12. Matthias Rupp, Raghunathan Ramakrishnan, O. Anatole von Lilienfeld: Machine Learning for Quantum Mechanical Properties of Atoms in Molecules, Journal of Physical Chemistry Letters, 6(16): 3309–3313, American Chemical Society, 2015. [doi]
  13. Matthias Rupp: Special Issue on Machine Learning and Quantum Mechanics, International Journal of Quantum Chemistry, 115(16): 1003–1004, Wiley, 2015. [doi]
  14. Matthias Rupp: Machine Learning for Quantum Mechanics in a Nutshell, International Journal of Quantum Chemistry, 115(16): 1058–1073, Wiley, 2015. [doi] [supplement]
  15. Kevin Vu, John C. Snyder, Li Li, Matthias Rupp, Brandon F. Chen, Tarek Khelif, Klaus-Robert Müller, Kieron Burke: Understanding Kernel Ridge Regression: Common Behaviors from Simple Functions to Density Functionals, International Journal of Quantum Chemistry, 115(16): 1115–1128, Wiley, 2015. [doi]
  16. John C. Snyder, Matthias Rupp, Klaus-Robert Müller, Kieron Burke: Nonlinear Gradient Denoising: Finding Accurate Extrema from Inaccurate Functional Derivatives, International Journal of Quantum Chemistry, 115(16): 1102–1114, Wiley, 2015. [doi]
  17. O. Anatole von Lilienfeld, Raghunathan Ramakrishnan, Matthias Rupp, Aaron Knoll: Fourier Series of Atomic Radial Distribution Functions: A Molecular Fingerprint for Machine Learning Models of Quantum Chemical Properties, International Journal of Quantum Chemistry, 115(16): 1084–1093, Wiley, 2015. [doi]
  18. Raghunathan Ramakrishnan, Pavlo O. Dral, Matthias Rupp, O. Anatole von Lilienfeld: Big Data Meets Quantum Chemistry Approximations: The Δ-Machine Learning Approach, Journal of Chemical Theory and Computation, 11(5): 2087–2096, American Chemical Society, 2015. [doi]
  19. Raghunathan Ramakrishnan, Pavlo Dral, Matthias Rupp, O. Anatole von Lilienfeld: Quantum Chemistry Structures and Properties of 134 kilo Molecules, Scientific Data, 1: 140022, Nature Publishing Group, 2014. [doi] [pdf]
  20. Matthias Rupp, Matthias R. Bauer, Rainer Wilcken, Andreas Lange, Michael Reutlinger, Frank M. Boeckler, Gisbert Schneider: Machine Learning Estimates of Natural Product Conformational Energies, PLoS Computational Biology, 10(1): e1003400, Public Library of Science, 2014. [doi] [pdf]
  21. John C. Snyder, Matthias Rupp, Katja Hansen, Leo Blooston, Klaus-Robert Müller, Kieron Burke: Orbital-free Bond Breaking via Machine Learning, Journal of Chemical Physics, 139(22): 224104, American Institute of Physics, 2013. [doi] [pdf]
  22. Katja Hansen, Grégoire Montavon, Franziska Biegler, Siamac Fazli, Matthias Rupp, Matthias Scheffler, O. Anatole von Lilienfeld, Alexandre Tkatchenko, Klaus-Robert Müller: Assessment and Validation of Machine Learning Methods for Predicting Molecular Atomization Energies, Journal of Chemical Theory and Computation, 9(8): 3543–3556, American Chemical Society, 2013. [doi]
  23. Volker Hähnke, Matthias Rupp, Alexander K. Hartmann, Gisbert Schneider: Pharmacophore Alignment Search Tool (PhAST): Significance Assessment of Chemical Similarity, Molecular Informatics, 32(7): 625–646, Wiley, 2013. [doi]
  24. Grégoire Montavon, Matthias Rupp, Vivekanand Gobre, Alvaro Vazquez-Mayagoitia, Katja Hansen, Alexandre Tkatchenko, Klaus-Robert Müller, O. Anatole von Lilienfeld: Machine learning of molecular electronic properties in chemical compound space, New Journal of Physics, 15(9): 095003, IOP Publishing, 2013. [doi] [pdf] [dataset]
  25. Matthias Rupp, Alexandre Tkatchenko, Klaus-Robert Müller, O. Anatole von Lilienfeld: Reply to Comment by J.E. Moussa (Physical Review Letters 109(5): 059801, 2012), Physical Review Letters, 109(5): 059802, American Physical Society, 2012. [doi] [pdf] See our original article (Physical Review Letters 108(5): 058301, 2012).
  26. Julia Weber, Matthias Rupp, Ewgenij Proschak: Impact of X-Ray Structure on Predictivity of Scoring Functions: PPARγ Case Study, Molecular Informatics, 31(9): 631–633, Wiley, 2012. [doi]
  27. John C. Snyder, Matthias Rupp, Katja Hansen, Klaus-Robert Müller, Kieron Burke: Finding Density Functionals with Machine Learning, Physical Review Letters, 108(25): 253002, American Physical Society, 2012. [doi] [pdf]
  28. Grigorios Skolidis, Katja Hansen, Guido Sanguinetti, Matthias Rupp: Multi-task learning for pKa prediction, Journal of Computer-Aided Molecular Design, 26(7): 883–895, Springer, 2012. [doi]
  29. Zachary D. Pozun, Katja Hansen, Daniel Sheppard, Matthias Rupp, Klaus-Robert Müller, Graeme Henkelman: Optimizing transition states via kernel-based machine learning, Journal of Chemical Physics, 136(17): 174101, American Institute of Physics, 2012. [doi] Top 20 Most Read in 5/2012
  30. Markus Hartenfeller, Heiko Zettl, Miriam Walter, Matthias Rupp, Felix Reisen, Ewgenij Proschak, Sascha Weggen, Holger Stark, Gisbert Schneider: DOGS: Reaction-Driven De Novo Design of Bioactive Compounds, PLoS Computational Biology, 8(2): e1002380, Public Library of Science, 2012. [doi] [pdf]
  31. Matthias Rupp, Alexandre Tkatchenko, Klaus-Robert Müller, O. Anatole von Lilienfeld: Fast and Accurate Modeling of Molecular Atomization Energies with Machine Learning, Physical Review Letters, 108(5): 058301, American Physical Society, 2012. [doi] [pdf] See also comment by J.E. Moussa in Physical Review Letters 109(5): 059801, 2012, and our reply to it.
  32. Katja Hansen, David Baehrens, Timon Schroeter, Matthias Rupp, Klaus-Robert Müller: Visual Interpretation of Kernel-Based Prediction Models, Molecular Informatics, 30(9): 817–826, Wiley, 2011. [doi]
  33. Quan Wang, Kerstin Birod, Carlo Angioni, Sabine Grösch, Tim Geppert, Petra Schneider, Matthias Rupp, Gisbert Schneider: Spherical Harmonics Coefficients for Ligand-Based Virtual Screening of Cyclooxygenase Inhibitors, PLoS ONE, 6(7): e21554, Public Library of Science, 2011. [doi] [pdf]
  34. Iurii Sushko, Sergii Novotarskyi, Robert Körner, Anil Kumar Pandey, Matthias Rupp, Wolfram Teetz, Stefan Brandmaier, Ahmed Abdelaziz, Volodymyr V. Prokopenko, Vsevolod Y. Tanchuk, Roberto Todeschini, Alexandre Varnek, Gilles Marcou, Peter Ertl, Vladimir Potemkin, Maria Grishina, Johann Gasteiger, Christof Schwab, Igor I. Baskin, Vladimir A. Palyulin, Eugene V. Radchenko, William J. Welsh, Vladyslav Kholodovych, Dmitriy Chekmarev, Artem Cherkasov, Joao Aires-de-Sousa, Qing-You Zhang, Andreas Bender, Florian Nigsch, Luc Patiny, Antony Williams, Valery Tkachenko, Igor V. Tetko: Online chemical modeling environment (OCHEM): web platform for data storage, model development and publishing of chemical information, Journal of Computer Aided Molecular Design, 25(6): 533–554, Springer, 2011. [doi]
  35. Matthias Rupp, Robert Körner, Igor V. Tetko: Predicting the pKa of small molecules, Combinatorial Chemistry & High Throughput Screening, 14(5): 307–327, Bentham, 2011. [doi] [pdf]
  36. Matthias Rupp, Robert Körner, Igor V. Tetko: Estimation of acid dissociation constants using graph kernels, Molecular Informatics, 29(10): 731–740, Wiley, 2010. [doi]
  37. Volker Hähnke, Matthias Rupp, Mireille Krier, Friedrich Rippmann, Gisbert Schneider: Pharmacophore alignment search tool (PhAST): Influence of Canonical Atom Labeling on Similarity Searching, Journal of Computational Chemistry, 31(15): 2810–2826, Wiley, 2010. [doi]
  38. Matthias Rupp, Gisbert Schneider: Graph kernels for molecular similarity, Molecular Informatics, 29(4): 266–273, Wiley, 2010. [doi]
  39. Ramona Steri, Petra Schneider, Alexander Klenner, Matthias Rupp, Manfred Schubert-Zsilavecz, Gisbert Schneider: Target profile prediction: Cross-activation of peroxisome proliferator-activated receptor (PPAR) and farnesoid X receptor (FXR), Molecular Informatics, 29(4): 287–292, Wiley, 2010. [doi]
  40. Ramona Steri, Matthias Rupp, Ewgenij Proschak, Timon Schroeter, Heiko Zettl, Katja Hansen, Oliver Schwarz, Lutz Müller-Kuhrt, Klaus-Robert Müller, Gisbert Schneider, Manfred Schubert-Zsilavecz: Truxillic acid derivatives act as peroxisome proliferator-activated receptor γ activators, Bioorganic & Medicinal Chemistry Letters, 20(9): 2920–2923, Elsevier, 2010. [doi]
  41. Matthias Rupp, Timon Schroeter, Ramona Steri, Heiko Zettl, Ewgenij Proschak, Katja Hansen, Oliver Rau, Oliver Schwarz, Lutz Müller-Kuhrt, Manfred Schubert-Zsilavecz, Klaus-Robert Müller, Gisbert Schneider: From machine learning to natural product derivatives selectively activating transcription factor PPARγ, ChemMedChem, 5(2): 191–194, Wiley, 2010. [doi]
  42. Matthias Rupp, Petra Schneider, Gisbert Schneider: Distance phenomena in high-dimensional chemical descriptor spaces: Consequences for similarity-based approaches, Journal of Computational Chemistry, 30(14): 2285–2296, Wiley, 2009. [doi]
  43. Ewgenij Proschak, Matthias Rupp, Swetlana Derksen, Gisbert Schneider: Shapelets: Possibilities and limitations of shape-based virtual screening, Journal of Computational Chemistry, 29(1): 108–114, Wiley, 2008. [doi]
  44. Matthias Rupp, Ewgenij Proschak, Gisbert Schneider: Kernel approach to molecular similarity based on iterative graph similarity, Journal of Chemical Information and Modeling, 47(6): 2280–2286, American Chemical Society, 2007. [doi]

Conferences, workshops, schools

  1. Bastian Jäckl, Thomas Bischoff, Matthias Rupp: Warm Dense Hydrogen as a Benchmark for Machine-Learning Potentials, Spring Meeting of the German Physical Society (DPG), Berlin, Germany, March 17–22, 2024.
  2. Marcel F. Langer, Florian Knoop, J. Thorben Frank, Christian Carbogno, Matthias Scheffler, Matthias Rupp: Computing Green-Kubo Thermal Conductivities with Semi-Local Machine-Learning Potentials, Spring Meeting of the German Physical Society (DPG), Berlin, Germany, March 17–22, 2024.
  3. Richard Hennig, Ajinkya Hire, Jason Gibson, Hendrik Kraß, Ming Li, Pawan Prakash, Stephen Xie, Matthias Rupp: Exascale Simulations using Ultra-Fast Force Field for Materials Discovery and Design, 153rd Annual Meeting & Exhibition of the Minerals, Metals & Materials Society (TMS), Orlando, Florida, USA, March 3–7, 2024.
  4. Matthias Rupp: Towards Quantum Monte Carlo-based Machine-Learning Potentials, TREX Symposium on Bridging Quantum Monte Carlo and High-Performance Simulations, Belval, Luxembourg, February 5–9, 2024.
  5. Matthias Rupp: Ultra-Fast Potentials, 2019 to 2023, IPAM Second Reunion Workshop on Machine Learning for Physics, Lake Arrowhead, California, USA, December 11–15, 2023.
  6. Matthias Rupp, Thomas Bischoff, Bastian Jäckl: Warm Dense Hydrogen as a Benchmark for Machine-Learning Force Fields, Workshop on Crash Testing Machine Learning Force Fields: Applicability, Best Practices, Limitations (TEA), University of Luxembourg, Luxembourg, October 23–25, 2023.
  7. Matthias Rupp: Machine-Learning Potentials, TREX Hackathon IV, Megware GmbH, Chemnitz, Germany, October 16–20, 2023.
  8. Marcel F. Langer, J. Thorben Frank, Christian Carbogno, Matthias Scheffler, Matthias Rupp, Florian Knoop: Thermal Transport with Message-Passing Neural Network Potentials, CECAM/Ψk Research Conference on Bridging Length Scales with Machine Learning: From Wavefunctions to Thermodynamics, Berlin, June 19–23, 2023.
  9. Matthias Rupp: Machine-Learning Potentials, TREX Workshop on Quantum-Chemical Methods for Strongly Correlated Systems, Lodz University of Technology, Łódź, Poland, 18–20 April, 2023.
  10. Matthias Rupp: Ultra-Fast Potentials for Thermal Transport, NWO Physics, Veldhoven, The Netherlands, April 4–5, 2023.
  11. Thomas Bischoff, Bastian Jäckl, Matthias Rupp: Ultra-Fast Machine Learning Potentials for Hydrogen under Pressure, Spring Meeting of the German Physical Society (DPG), Leipzig, Germany, March 26–31, 2023.
  12. Marcel F. Langer, Florian Knoop, J. Thorben Frank, Christian Carbogno, Matthias Scheffler, Matthias Rupp: Stress and Heat Flux via Automatic Differentiation, Spring Meeting of the German Physical Society (DPG), Leipzig, Germany, March 26–31, 2023.
  13. Hendrik Kraß, Benedikt Ziebarth, Wolfgang Mannstadt, Matthias Rupp: Properties of Sodium-Borosilicate Glasses via Dynamics Simulations with Ultra-Fast Machine-Learning Potentials, Spring Meeting of the German Physical Society (DPG), Leipzig, Germany, March 26–31, 2023.
  14. Matthias Rupp: Predicting Spectra with Atomistic Machine-Learning Models, Lorentz Workshop on Accelerating Theoretical Spectroscopy for Complex Multiscale Materials, Lorentz Center, Leiden, The Netherlands, March 20–24, 2023.
  15. Richard G. Hennig, Stephen R. Xie, Pawan Prakash, Ajinkya Hire, Robert Schmid, Hendrik Kraß, Matthias Rupp: Ultra-Fast Interpretable Machine-Learning Potentials for Accelerated Structure Prediction of Materials, 152nd Annual Meeting & Exhibition of the Minerals, Metals & Materials Society (TMS), San Diego, California, USA, March 19–23, 2023.
  16. Matthias Rupp: All-Atom Dynamics Simulations with Ultra-Fast Machine-Learning Potentials, Theoretical Chemical Physics Group Workshop, University of Luxembourg, Luxembourg, December 14, 2022.
  17. Matthias Rupp: All-Atom Dynamics Simulations with Machine-Learning Potentials, 16th International Symposium on Integrative Bioinformatics, Konstanz, Germany, September 15–16, 2022.
  18. Marcel F. Langer, Florian Knoop, Christian Carbogno, Matthias Scheffler, Matthias Rupp: Thermal Transport via Green-Kubo Method and Message-Passing Neural-Network Potentials, Spring Meeting of the German Physical Society (DPG), Regensburg, Germany, September 4–9, 2022.
  19. Stephen R. Xie, Richard G. Hennig, Matthias Rupp: Fast, robust, interpretable machine-learning potentials, Spring Meeting of the German Physical Society (DPG), Regensburg, Germany, September 4–9, 2022.
  20. Florian Knoop, Marcel F. Langer, Matthias Rupp, Christian Carbogno, Matthias Scheffler: First principles meet machine learning: Green-Kubo simulations for the discovery of thermal insulators, The 4th Conference on Advanced Functional Materials (AFM 2022), Linköping University, Sweden, August 29–31, 2022.
  21. Richard G. Hennig, Stephen R. Xie, Ajinkya Hire, Yundi Quan, P. Dee, P. Prakash, Robert Schmid, Thomas Bischoff, Matthias Rupp, Boning Deng, Jonathan M. DeStefano, I. Salinas, Urja S. Shah, Laura Fanfarillo, Jinhyuk Lim, Jungsoo Kim, Gregory R. Stewart, James J. Hamlin, Peter J. Hirschfeld: Machine Learning for the Search of New Superconductors, Workshop on Challenges in Designing Room Temperature Superconductors, Gran Sasso Science Institute, L'Aquila, Italy, July 26–29, 2022.
  22. Matthias Rupp: Ultra-Fast Interpretable Machine-Learning Potentials, Workshop on Methods in Molecular Simulations and Machine Learning (MMSML), Barcelona, Spain, July 14–16, 2022.
  23. Matthias Rupp: Development and Applications of Ultra-fast Potentials, IPAM Reunion Workshop on Machine Learning for Physics, Lake Arrowhead, California, USA, June 6–10, 2022.
  24. Florian Knoop, Marcel F. Langer, Matthias Rupp, Christian Carbogno, Matthias Scheffler: Data-Driven Search for Thermal Insulators Guided by Anharmonicity: From First Principles to Machine Learning, 48th International Conference on Metallurgical Coatings and Thin Films (ICMCTF), San Diego, California, USA, May 22–27, 2022.
  25. Matthias Rupp: Materials Properties via Machine-Learning Potentials, Workshop on Machine Learning in Chemical and Materials Sciences, Center for Nonlinear Studies, Los Alamos National Laboratory, New Mexico, USA, May 23–26, 2022.
  26. Matthias Rupp: Introduction to Learning with Kernels, Young Researcher's Workshop on Machine Learning for Materials, Trieste, Italy, May 9–13, 2022.
  27. Matthias Rupp: Learning with Kernels, Toulouse School on Machine Learning for Quantum Many-Body Physics (MLQMB), Toulouse, France, April 4–8, 2022.
  28. Marcel F. Langer, Florian Knoop, Christian Carbogno, Matthias Scheffler, Matthias Rupp: Thermal Transport with Message Passing Neural Networks via the Green-Kubo Method, American Physical Society March Meeting, Chicago, March 14–18, 2022.
  29. Stephen R. Xie, Robert Schmid, Matthias Rupp, Richard G. Hennig: Ultra-fast Force Fields (UF3) Framework for Machine-learning Interatomic Potentials, American Physical Society March Meeting, Chicago, March 14–18, 2022.
  30. Matthias Rupp: Machine-Learning Potentials: Introduction and Examples, Discussion Meeting on Machine Learning. Groupements de Recherche REncontres de Spectroscopie Theorique (GDR REST Workshop), Ecole Polytechnique, Palaiseau, France, December 9–10, 2021.
  31. Matthias Rupp: Machine-Learning Potentials: Splines, Kernels, Neural Networks, Workshop on Computational Materials Chemistry, Telluride, CO, USA, June 27–July 1, 2021.
  32. Marcel F. Langer, Florian Knoop, Christian Carbogno, Matthias Scheffler, Matthias Rupp: Green-Kubo Thermal Conductivities with Message-Passing Neural Networks, American Physical Society March Meeting, virtual meeting, March 15–19, 2021.
  33. Matthias Rupp: Prediction Errors of Machine-Learning Models, American Physical Society Topical Group on Data Science (APS GDS) Virtual March Meeting Session 12 on Scientific Machine Learning for Molecules and Materials, virtual meeting, June 19, 2020.
  34. Mario Boley, Christopher Sutton, Luca Ghiringhelli, Matthias Rupp, Matthias Scheffler, Jilles Vreeken: Identifying Domains of Applicability of Machine Learning Models for Materials Science, Conference on a FAIR Data Infrastructure for Materials Genomics (FAIR-DI), virtual meeting, June 3–5, 2020.
  35. Matthias Rupp: Machine Learning for Molecules and Materials, CECAM/Lorentz Workshop on Computing Complex Mechanical Systems, Lausanne, Switzerland, January 22–24, 2020.
  36. Marcel Langer, Alex Goeßmann, Maxwell Hutchinson, Matthias Rupp: How to Assess Scientific Machine Learning Models? Prediction Errors and Predictive Uncertainty Quantification, Institute for Pure and Applied Mathematics (IPAM) Workshop on Using Physical Insights for Machine Learning, Los Angeles, California, USA, November 18–22, 2019.
  37. Marcel Langer, Alex Goeßmann, Matthias Rupp: Representations of Molecules and Materials for Interpolation of Ab Initio Calculations, Workshop From Passive to Active: Generative and Reinforcement Learning with Physics, Institute for Pure and Applied Mathematics (IPAM), Los Angeles, California, USA, September 23–27, 2019.
  38. Marcel Langer, Alex Goeßmann, Matthias Rupp: Exact Representations of Molecules and Materials for Accurate Interpolation of Ab Initio Simulations, Workshop on Developing High-Dimensional Potential Energy Surfaces—From the Gas Phase to Materials, Göttingen, Germany, April 24–26, 2019.
  39. Marcel Langer, Alex Goeßmann, Matthias Rupp: Representing Molecules and Materials for Accurate Interpolation of Quantum-Mechanical Calculations, Spring Meeting of the German Physical Society, Regensburg, Germany, March 31–April 5, 2019.
  40. Max Hutchinson, Matthias Rupp, Bryce Meredig: Gluing Together Multiscale Computational & Experimental Information Sources with Machine Learning, 148th Annual Meeting & Exhibition of the Minerals, Metals & Materials Society (TMS), San Antonio, Texas, USA, March 10–14, 2019.
  41. Matthias Rupp: Machine Learning for Quantum Chemistry, The Löwdin Lectures, Uppsala, Sweden, November 15–16, 2018.
  42. Matthias Rupp: Accurate Interpolation of Ab Initio Calculations with Machine Learning, CECAM-Sackler Workshop on Frontiers in Molecular Dynamics: Machine Learning, Deep Learning and Coarse-Graining, Tel Aviv, Israel, October 8–12, 2018.
  43. Matthias Rupp: Accurate Energy Predictions for Materials and Molecules via Machine Learning, CECAM Workshop on Improving the Accuracy of Ab-Initio Predictions for Materials, Paris, France, September 17–20, 2018.
  44. Matthias Rupp: Accurate Energy Predictions via Machine Learning, Conference on Quantum Machine Learning Plus (QML+ 2018), Innsbruck, Austria, September 17–21, 2018.
  45. Matthias Rupp: Machine Learning for Interpolation of Electronic Structure Calculations, International Conference on Machine Learning and Physics, Institute for Advanced Study, Tsinghua University, Beijing, China, July 4–6, 2018.
  46. Matthias Rupp: High-throughput Energy Predictions for Molecules and Materials via Machine Learning, Workshop on Modern Approaches to Coupling Scales in Materials Simulation, Lenggries, Germany, July 2–4, 2018.
  47. Matthias Rupp: Accurate Energy Predictions for Materials, Workshop on Machine Learning for Quantum Many-body Physics, Max Planck Institute for Physics of Complex Systems, Dresden, Germany, June 25–29, 2018.
  48. Matthias Rupp: Representing molecules and materials for interpolation of quantum-mechanical calculations, IPAM Reunion Workshop on Many-Particle Systems with Machine Learning, Lake Arrowhead, California, USA, June 10–15, 2018.
  49. Matthias Rupp: The Many-Body Tensor Representation, CECAM Workshop on Machine Learning at Interfaces, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland, June 4–8, 2018.
  50. Matthias Rupp: Kernel-based Machine Learning for Materials, BigMax Workshop on Big-Data-Driven Materials Science, Kloster Irsee, Germany, April 10–13, 2018 .
  51. Matthias Rupp: Machine Learning for Materials, 147th Annual Meeting & Exhibition of the Minerals, Metals & Materials Society (TMS), March 11–15, Phoenix, Arizona, USA, 2018.
  52. Matthias Rupp: Machine Learning for Molecules and Materials. Potential and Limitations of Data-Driven Chemistry, 27th Austin Symposium on Molecular Structure and Dynamics at Dallas, Dallas, Texas, March 3–5, 2018.
  53. Matthias Rupp: Interpolation of Electronic Structure Calculations via Machine Learning, International Workshop on Atomic Physics, Max Planck Institute for the Physics of Complex Systems, Dresden, Germany, November 26–December 1, 2017.
  54. Matthias Rupp: Machine Learning for Quantum Mechanics, Hands-on Workshop on Density Functional Theory and Beyond–-Accuracy, Efficiency and Reproducibility in Computational Materials Science, Humboldt University, Berlin, Germany, July 31–August 11, 2017.
  55. Matthias Rupp: Unified Representation for Machine Learning of Molecules and Crystals, Workshop on Machine Learning and Many-Body Physics, Kavli Institute for Theoretical Sciences, University of Chinese Academy of Sciences, Beijing, China, June 28–July 7, 2017.
  56. Matthias Rupp: Many-Body Tensor Representation, Working Conference on Materials and Data Analysis, Harvard University, Cambridge, Massachusetts, USA, March 27–31, 2017.
  57. Matthias Rupp: Many-Body Tensor Representation for Machine Learning of Materials, March Meeting of the American Physical Society, New Orleans, Louisiana, USA, March 13–17, 2017.
  58. Matthias Rupp: Accurate Machine Learning Predictions for Materials Properties, International Workshop on Machine Learning for Materials Science, Aalto University, Espoo, Finland, 2017.
  59. Haoyan Huo, Matthias Scheffler, Matthias Rupp: Many-Body Tensor Representation for Machine Learning of Solids, The 57th Sanibel Symposium, St. Simons Island, Georgia, USA, February 19–24, 2017.
  60. Matthias Rupp: New Data, Validation, Code and Representation for Interpolation Across Chemical Compound Space, Institute for Pure and Applied Mathematics (IPAM) Workshop on Machine Learning Meets Many-Particle Problems, Los Angeles, California, September 26–30, 2016.
  61. Matthias Rupp: Practical Tutorial on Machine Learning for Electronic Structure Calculations, Institute for Pure and Applied Mathematics (IPAM), Understanding Many-Particle Systems with Machine Learning Tutorials, Los Angeles, California, September 13–16, 2016.
  62. Matthias Rupp: Atomistic Machine Learning Models, Probing Potential Energy Surfaces IV (PPES IV), Zermatt, Switzerland, April 10–15, 2016.
  63. Matthias Rupp: Challenges in Development of Accurate and Efficient Atomistic Machine Learning Models, CECAM Workshop on Big Data of Materials Science–Critical Next Steps, Lausanne, Switzerland, November 30–December 4, 2015.
  64. Matthias Rupp: Machine Learning for Quantum Mechanical Properties of Atoms in Molecules, 18th Asian Workshop on First-Principles Electronic Structure Calculations, Tokyo, Japan, November 9–11, 2015.
  65. Matthias Rupp: Predicting Results of Quantum Mechanical Calculations: Challenges for Machine Learning, Frontiers in Data-Driven Science and Technology: Recent Advances in Machine Learning and Applications, Nagoya, Japan, November 5–6, 2015.
  66. Matthias Rupp: Quantum Mechanical Properties of Atoms in Molecules via Machine Learning, Ψk 2015 Conference, San Sebastián, Spain, September 6–10, 2015. [pdf]
  67. Matthias Rupp: Quantum Mechanics / Machine Learning Models, Hands-on Workshop Density Functional Theory and Beyond: First-principles Simulations of Molecules and Materials, Berlin, Germany, July 13–23, 2015. [pdf]
  68. Matthias Rupp, Raghunathan Ramakrishnan, O. Anatole von Lilienfeld: Representing Atoms in Molecules, CECAM/Ψk Workshop From Many-Body Hamiltonians to Machine Learning and Back, Berlin, Germany, May 11–13, 2015.
  69. Matthias Rupp: Properties of Atoms in Molecules via Machine Learning, Workshop on Machine Learning for Many-Particle Systems, Institute for Pure and Applied Mathematics (IPAM), Los Angeles, California, February 23–27, 2015. 
  70. Matthias Rupp: Properties of Atoms in Molecules via Machine Learning, CECAM/Ψk Research Conference on Frontiers of First-Principles Simulations: Materials Design and Discovery, Berlin, Germany, February 1–5, 2015. 
  71. Pavlo O. Dral, Raghunathan Ramakrishnan, Matthias Rupp, Walter Thiel, O. Anatole von Lilienfeld: Combining Semiempirical Quantum Mechanics with Machine Learning: Towards Hybrid Quantum Mechanics/Machine Learning (QM/ML), 50th Symposium on Theoretical Chemistry (STC 2014), Vienna, Austria, September 14–18, 2014. [pdf]
  72. Matthias Rupp: Quantum Mechanics / Machine Learning Models, Institute for Pure and Applied Mathematics, Hands-on Summer School on Electronic Structure Theory for Materials and (Bio)molecules (IPAM GSS2014), Los Angeles, California, USA, July  21–August 1, 2014, 2014. [pdf]
  73. Matthias Rupp: Quantum Mechanics / Machine Learning Models. Recent Successes and Challenges, White Nights of Materials Science: From Physics and Chemistry to Data Analysis, and Back, St. Petersburg, Russia, June 16–20, 2014.
  74. Matthias Rupp: Hybrid Quantum Mechanics/Machine Learning Models, HP2C/PASC Materials Simulation Junior Retreat, Boldern, Männedorf, Switzerland, July 09–12, 2013.
  75. Matthias Rupp, Grégoire Montavon, Vivekanand Gobre, Alvaro Vazquez-Mayagoitia, Katja Hansen, Alexandre Tkatchenko, Klaus-Robert Müller, O. Anatole von Lilienfeld: Machine Learning in Chemical Space: Predicting Electronic Structure Properties, 7th Molecular Quantum Mechanics. Electron Correlation: The Many-Body Problem at the Heart of Chemistry, Lugano, Switzerland, June 2-7, 2013. [pdf]
  76. Grégoire Montavon, Katja Hansen, Siamac Fazli, Matthias Rupp, Franziska Biegler, Andreas Ziehe, Alexandre Tkatchenko, O. Anatole von Lilienfeld, Klaus-Robert Müller: Learning invariant representations of molecules for atomization energy prediction, Advances in Neural Information Processing Systems 25 (NIPS 2012), Lake Tahoe, Nevada, USA, December 3-6, 2012. [pdf]
  77. Matthias Rupp: Kernel-based Machine Learning for Molecular Energy Estimation, CECAM Workshop on Machine Learning in Atomistic Simulations, Lugano, Switzerland, September 10–12, 2012. [pdf]
  78. Matthias Rupp: Modeling of molecular atomization energies using machine learning, 7th German Conference on Chemoinformatics, Goslar, Germany, November 6–8, 2011. [pdf]
  79. Katja Hansen, David Baehrens, Timon Schroeter, Matthias Rupp, Klaus-Robert Müller: Interpretation and explanation of kernel-based prediction models, 242nd Annual Meeting of the American Chemical Society, Denver, Colorado, USA, August 28–September 1, 2011.
  80. Matthias Rupp: From machine learning to novel agonists of the peroxisome proliferator-activated receptor, 24th Annual Conference on Neural Information Processing Systems (NIPS 2010) Workshop on Charting Chemical Space: Challenges and Opportunities for AI and Machine Learning, Whistler, Canada, December 10–11, 2010. [pdf]
  81. Matthias Rupp: Graph kernels for chemoinformatics. A critical discussion, 6th German Conference on Chemoinformatics, Goslar, Germany, November 7–9, 2010. [pdf]
  82. Matthias Rupp, Timon Schroeter, Ramona Steri, Ewgenij Proschak, Katja Hansen, Heiko Zettl, Oliver Rau, Manfred Schubert-Zsilavecz, Klaus-Robert Müller, Gisbert Schneider: Kernel learning for virtual screening: Discovery of a new PPARγ agonist, 5th German C onference on Chemoinformatics, Goslar, Germany, November 8–10, 2009. [pdf] [doi]
  83. Igor Tetko, Iurii Sushko, Sergeii Novotarsky, Robert Körner, Anil Kumar Pandey, Matthias Rupp: Online chemical modeling environment, 1st World Conference on Physico-Chemical Methods in Drug Discovery and Development, Rovinj, Croatia, September 27–October 1, 2009. [pdf]
  84. Matthias Rupp, Petra Schneider, Gisbert Schneider: Distance phenomena in chemical spaces: Consequences for similarity approaches, 4th German Conference on Chemoinformatics, Goslar, Germany, November 9–11, 2008. [pdf]
  85. Timon Schroeter, Matthias Rupp, Katja Hansen, Klaus-Robert Müller, Gisbert Schneider: Virtual screening for PPAR-gamma ligands using the ISOAK molecular graph kernel and Gaussian processes, 4th German Conference on Chemoinformatics, Goslar, Germany, November 9–11, 2008. [pdf]
  86. Matthias Rupp, Ewgenij Proschak, Gisbert Schneider: Molecular similarity for machine learning in drug development, 3rd German Conference on Chemoinformatics, Goslar, Germany, November 11–13, 2007. [pdf] Best poster award
  87. Matthias Rupp, Wolfgang Mergenthaler, Bernhard Mauersberg, Jens Feller: Markov mills, reliable rolls and Monte-Carlo mines: Minimizing the operating costs of grinding mills, Proceedings of the 2005 International Conference on Numerical Analysis and Applied Mathematics (ICNAAM 2005), Rhodes, Greece, September 26–20, 2005. [pdf]

Book chapters

  1. Matthias Rupp: Graph kernels. In Matthias Dehmer, Subhash Basak (editors): Machine Learning Approach for Network Analysis: Novel Graph Classes for Classification Techniques, Wiley, chapter 8, p. 217-243, 2012. [doi]

Outreach

  1. Matthias Rupp: How Much Chemistry and Physics Can We Push AI to Do?. Interview in ChemistryViews, Wiley, 2023. [doi]

Theses

  1. Matthias Rupp: Kernel methods for virtual screening, PhD dissertation, University of Frankfurt, Germany, 2009. [pdf]
  2. Matthias Rupp: Zeitoptimale Bearbeitungsreihenfolgen für mehrere Schweißroboter: Modelle und Algorithmen, degree dissertation, University of Frankfurt, Germany, 2004. [pdf]

Software

  1. Matthias Rupp: Machine Learning for Quantum Mechanics in a Nutshell, Mathematica implementation, version 2015-07-04. [zip]
  2. Grigorios Skolidis: Multi-task Gaussian process regression, Matlab code, version 2012-05-10. [zip]
  3. Matthias Rupp: Iterative similarity optimal assignment kernel (ISOAK), Java implementation, version 2008-01-15. [mloss] [zip]