Publications

Strothmann, R., M. Amanpur, T. Neveselý, S. Hecht, K. Reuter and J. Margraf: Machine learning driven design of spiropyran photoswitches. Digital Discovery 4 (11), 3098–3108 (2025).

Jakob, K., A. Walsh, K. Reuter and J. Margraf: Learning Crystallographic Disorder: Bridging Prediction and Experiment in Materials Discovery. Advanced Materials, in press.

Vondrák, M., K. Reuter and J. Margraf: Pushing charge equilibration-based machine learning potentials to their limits. npj Computational Materials 11, 288 (2025).

Blahusch, J., K. Jakob, J. Margraf, K. Reuter and B.V. Lotsch: Predict before You Precipitate: Learning Templating Effects in Hybrid Antimony and Bismuth Halides. Chemistry of Materials 37 (14), 5027–5035 (2025).

Pokora, M., J. Goclon, J. Margraf, C. Panosetti, A. Samtsevych and P. Paneth: Low-Cost Periodic Calculations of Metal-Organic Frameworks: A GFN1-xTB Perspective. ChemPhysChem 26 (14), 202500081 (2025).

Jakob, K., K. Reuter and J. Margraf: Universally Accurate or Specifically Inadequate? Stress-testing General Purpose Machine Learning Interatomic Potentials. Advanced Intelligent Systems, in press.

Cao, Y., E. Keller, J.F. Rowen, J. Margraf, K. Reuter and K. Morgenstern: Self-Assembly of Monosized Cyclic Nanoarchitectures under Surface Confinement. ACS Nano 19 (24), 21942–21949 (2025).

Gönnheimer, N., K. Reuter and J. Margraf: Beyond Numerical Hessians: Higher-Order Derivatives for Machine Learning Interatomic Potentials via Automatic Differentiation. Journal of Chemical Theory and Computation 21 (9), 4742–4752 (2025).

Cui, M., K. Reuter and J. Margraf: Multi-fidelity transfer learning for quantum chemical data using a robust density functional tight binding baseline. Machine Learning: Science and Technology 6 (1), 015071 (2025).

Keller, E., V. Blum, K. Reuter and J. Margraf: Exploring atom-pairwise and many-body dispersion corrections for the BEEF-vdW functional. The Journal of Chemical Physics 162 (07), 074111 (2025).