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  1. Heat flux for semilocal machine-learning potentials

    Article • September 2023, American Physical Society (APS)

    Dr. Matthias Rupp

  2. Ultra-fast interpretable machine-learning potentials

    Article • npj Computational Materials, September 2023, Springer Science + Business Media

    Dr. Matthias Rupp

  3. Unified representation of molecules and crystals for machine learning

    Article • Machine Learning Science and Technology, November 2022, Institute of Physics Publishing

    Dr. Matthias Rupp

  4. Representations of molecules and materials for interpolation of quantum-mechanical simulations via machine learning

    Article • npj Computational Materials, March 2022, Springer Science + Business Media

    Dr. Matthias Rupp

  5. Identifying domains of applicability of machine learning models for materials science

    Article • Nature Communications, September 2020, Springer Science + Business Media

    Dr. Matthias Rupp

  6. Assessing the frontier: Active learning, model accuracy, and multi-objective candidate discovery and optimization

    Article • The Journal of Chemical Physics, July 2020, American Institute of Physics

    Yoolhee Kim, Dr. Matthias Rupp

  7. Chemical diversity in molecular orbital energy predictions with kernel ridge regression

    Article • The Journal of Chemical Physics, May 2019, American Institute of Physics

    Dr. Matthias Rupp

  8. Machine-learned multi-system surrogate models for materials prediction

    Article • npj Computational Materials, April 2019, Springer Science + Business Media

    Dr. Matthias Rupp

  9. Guest Editorial: Special Topic on Data-Enabled Theoretical Chemistry

    Article • The Journal of Chemical Physics, June 2018, American Institute of Physics

    Dr. Matthias Rupp

  10. Understanding machine-learned density functionals

    Article • International Journal of Quantum Chemistry, November 2015, Wiley

    Dr. Matthias Rupp

  11. Machine Learning for Quantum Mechanical Properties of Atoms in Molecules

    Article • The Journal of Physical Chemistry Letters, August 2015, American Chemical Society (ACS)

    Dr. Matthias Rupp

  12. Machine learning for quantum mechanics in a nutshell

    Article • International Journal of Quantum Chemistry, July 2015, Wiley

    Dr. Matthias Rupp

  13. Special issue on machine learning and quantum mechanics

    Article • International Journal of Quantum Chemistry, June 2015, Wiley

    Dr. Matthias Rupp

  14. Understanding kernel ridge regression: Common behaviors from simple functions to density functionals

    Article • International Journal of Quantum Chemistry, May 2015, Wiley

    Dr. Matthias Rupp

  15. Nonlinear gradient denoising: Finding accurate extrema from inaccurate functional derivatives

    Article • International Journal of Quantum Chemistry, May 2015, Wiley

    Dr. Matthias Rupp

  16. Big Data Meets Quantum Chemistry Approximations: The Δ-Machine Learning Approach

    Article • Journal of Chemical Theory and Computation, April 2015, American Chemical Society (ACS)

    Dr. Matthias Rupp

  17. Fourier series of atomic radial distribution functions: A molecular fingerprint for machine learning models of quantum chemical properties

    Article • International Journal of Quantum Chemistry, April 2015, Wiley

    Dr. Matthias Rupp

  18. Quantum chemistry structures and properties of 134 kilo molecules

    Article • Scientific Data, August 2014, Springer Science + Business Media

    Dr. Matthias Rupp

  19. Machine Learning Estimates of Natural Product Conformational Energies

    Article • PLoS Computational Biology, January 2014, PLOS

    Dr. Matthias Rupp

  20. Orbital-free bond breaking via machine learning

    Article • The Journal of Chemical Physics, December 2013, American Institute of Physics

    Dr. Matthias Rupp

  21. Machine learning of molecular electronic properties in chemical compound space

    Article • New Journal of Physics, September 2013, Institute of Physics Publishing

    Dr. Matthias Rupp

  22. Assessment and Validation of Machine Learning Methods for Predicting Molecular Atomization Energies

    Article • Journal of Chemical Theory and Computation, July 2013, American Chemical Society (ACS)

    Dr. Matthias Rupp

  23. Pharmacophore Alignment Search Tool (PhAST): Significance Assessment of Chemical Similarity

    Article • Molecular Informatics, June 2013, Wiley

    Dr. Matthias Rupp

  24. Impact of X-Ray Structure on Predictivity of Scoring Functions: PPARγ Case Study

    Article • Molecular Informatics, August 2012, Wiley

    Dr. Matthias Rupp

  25. Ruppet al.Reply:

    Article • Physical Review Letters, August 2012, American Physical Society (APS)

    Dr. Matthias Rupp

  26. Multi-task learning for pKa prediction

    Article • Journal of Computer-Aided Molecular Design, June 2012, Springer Science + Business Media

    Dr. Matthias Rupp

  27. Finding Density Functionals with Machine Learning

    Article • Physical Review Letters, June 2012, American Physical Society (APS)

    Dr. Matthias Rupp

  28. Optimizing transition states via kernel-based machine learning

    Article • The Journal of Chemical Physics, May 2012, American Institute of Physics

    Dr. Matthias Rupp

  29. DOGS: Reaction-Driven de novo Design of Bioactive Compounds

    Article • PLoS Computational Biology, February 2012, PLOS

    Dr. Matthias Rupp

  30. Fast and Accurate Modeling of Molecular Atomization Energies with Machine Learning

    Article • Physical Review Letters, January 2012, American Physical Society (APS)

    Dr. Matthias Rupp

  31. Visual Interpretation of Kernel-Based Prediction Models

    Article • Molecular Informatics, September 2011, Wiley

    Dr. Matthias Rupp

  32. Spherical Harmonics Coefficients for Ligand-Based Virtual Screening of Cyclooxygenase Inhibitors

    Article • PLoS ONE, July 2011, PLOS

    Dr. Matthias Rupp

  33. The OCHEM web-based platform for data modeling/QSAR prediction

    Article • Journal of Computer-Aided Molecular Design, June 2011, Springer Science + Business Media

    Dr Antony John Williams, Valery Tkachenko, Honorary Professor Roberto Todeschini, Dr. Matthias Rupp

  34. Predicting the pKa of Small Molecules

    Article • Combinatorial Chemistry & High Throughput Screening, June 2011, Bentham Science Publishers

    Dr. Matthias Rupp

  35. Estimation of Acid Dissociation Constants Using Graph Kernels

    Article • Molecular Informatics, October 2010, Wiley

    Dr. Matthias Rupp

  36. Pharmacophore alignment search tool: Influence of canonical atom labeling on similarity searching

    Article • Journal of Computational Chemistry, September 2010, Wiley

    Dr. Matthias Rupp

  37. Truxillic acid derivatives act as peroxisome proliferator-activated receptor γ activators

    Article • Bioorganic & Medicinal Chemistry Letters, May 2010, Elsevier

    Dr. Matthias Rupp

  38. Graph Kernels for Molecular Similarity

    Article • Molecular Informatics, April 2010, Wiley

    Dr. Matthias Rupp

  39. Target Profile Prediction: Cross-Activation of Peroxisome Proliferator-Activated Receptor (PPAR) and Farnesoid X Receptor (FXR)

    Article • Molecular Informatics, February 2010, Wiley

    Dr. Matthias Rupp

  40. From Machine Learning to Natural Product Derivatives that Selectively Activate Transcription Factor PPARγ

    Article • ChemMedChem, February 2010, Wiley

    Dr. Matthias Rupp

  41. Distance phenomena in high-dimensional chemical descriptor spaces: Consequences for similarity-based approaches

    Article • Journal of Computational Chemistry, January 2009, Wiley

    Dr. Matthias Rupp

  42. Kernel Approach to Molecular Similarity Based on Iterative Graph Similarity

    Article • Journal of Chemical Information and Modeling, November 2007, American Chemical Society (ACS)

    Dr. Matthias Rupp

  43. Shapelets: Possibilities and limitations of shape-based virtual screening

    Article • Journal of Computational Chemistry, January 2007, Wiley

    Dr. Matthias Rupp