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  1. Hydrogen liquid-liquid transition from first principles and machine learning

    Article • Physical Review B, September 2025, American Physical Society (APS)

    Dr. Matthias Rupp

  2. Remaining useful life prediction of flax fibre biocomposites under creep load by acoustic emission and deep learning

    Article • Composites Part A Applied Science and Manufacturing, January 2025, Elsevier

    Dr. Matthias Rupp

  3. Machine Learning, Density Functional Theory, and Experiments to Understand the Photocatalytic Reduction of CO2 on CuPt/TiO2

    Article • The Journal of Physical Chemistry C, August 2024, American Chemical Society (ACS)

    Dr. Matthias Rupp

  4. Guest editorial: Special Topic on software for atomistic machine learning

    Article • The Journal of Chemical Physics, August 2024, American Institute of Physics

    Dr. Matthias Rupp

  5. Heat flux for semilocal machine-learning potentials

    Article • September 2023, American Physical Society (APS)

    Dr. Matthias Rupp

  6. Ultra-fast interpretable machine-learning potentials

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

    Dr. Matthias Rupp

  7. Unified representation of molecules and crystals for machine learning

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

    Dr. Matthias Rupp

  8. 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

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

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

    Dr. Matthias Rupp

  10. 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

  11. 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

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

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

    Dr. Matthias Rupp

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

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

    Dr. Matthias Rupp

  14. Understanding machine-learned density functionals

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

    Dr. Matthias Rupp

  15. 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

  16. Machine learning for quantum mechanics in a nutshell

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

    Dr. Matthias Rupp

  17. Special issue on machine learning and quantum mechanics

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

    Dr. Matthias Rupp

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

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

    Dr. Matthias Rupp

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

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

    Dr. Matthias Rupp

  20. 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

  21. 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

  22. Quantum chemistry structures and properties of 134 kilo molecules

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

    Dr. Matthias Rupp

  23. Machine Learning Estimates of Natural Product Conformational Energies

    Article • PLoS Computational Biology, January 2014, PLOS

    Dr. Matthias Rupp

  24. Orbital-free bond breaking via machine learning

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

    Dr. Matthias Rupp

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

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

    Dr. Matthias Rupp

  26. 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

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

    Article • Molecular Informatics, June 2013, Wiley

    Dr. Matthias Rupp

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

    Article • Molecular Informatics, August 2012, Wiley

    Dr. Matthias Rupp

  29. Ruppet al.Reply:

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

    Dr. Matthias Rupp

  30. Multi-task learning for pKa prediction

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

    Dr. Matthias Rupp

  31. Finding Density Functionals with Machine Learning

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

    Dr. Matthias Rupp

  32. Optimizing transition states via kernel-based machine learning

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

    Dr. Matthias Rupp

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

    Article • PLoS Computational Biology, February 2012, PLOS

    Dr. Matthias Rupp

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

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

    Dr. Matthias Rupp

  35. Visual Interpretation of Kernel-Based Prediction Models

    Article • Molecular Informatics, September 2011, Wiley

    Dr. Matthias Rupp

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

    Article • PLOS One, July 2011, PLOS

    Dr. Matthias Rupp

  37. 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

  38. Predicting the pKa of Small Molecules

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

    Dr. Matthias Rupp

  39. Estimation of Acid Dissociation Constants Using Graph Kernels

    Article • Molecular Informatics, October 2010, Wiley

    Dr. Matthias Rupp

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

    Article • Journal of Computational Chemistry, September 2010, Wiley

    Dr. Matthias Rupp

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

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

    Dr. Matthias Rupp

  42. Graph Kernels for Molecular Similarity

    Article • Molecular Informatics, April 2010, Wiley

    Dr. Matthias Rupp

  43. 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

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

    Article • ChemMedChem, February 2010, Wiley

    Dr. Matthias Rupp

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

    Article • Journal of Computational Chemistry, January 2009, Wiley

    Dr. Matthias Rupp

  46. 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

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

    Article • Journal of Computational Chemistry, January 2007, Wiley

    Dr. Matthias Rupp