Publications
Publications since joining the FHI are listed below (a full list can be found on Google Scholar):
1.
, J. Margraf, , K. Reuter and : Interplay between shape and composition in bimetallic nanoparticles revealed by an efficient optimal-exchange optimization algorithm. The Journal of Chemical Physics 164 (7), 074104 (2026).
2.
Jakob, K., , K. Reuter and J. Margraf: Learning Crystallographic Disorder: Bridging Prediction and Experiment in Materials Discovery. Advanced Materials 38 (5), e14226 (2026).
3.
Strothmann, R., , , , K. Reuter and J. Margraf: Machine learning driven design of spiropyran photoswitches. Digital Discovery 4 (11), 3098–3108 (2025).
4.
Vondrák, M., K. Reuter and J. Margraf: Pushing charge equilibration-based machine learning potentials to their limits. npj Computational Materials 11, 288 (2025).
5.
, K. Jakob, J. Margraf, K. Reuter and : Predict before You Precipitate: Learning Templating Effects in Hybrid Antimony and Bismuth Halides. Chemistry of Materials 37 (14), 5027–5035 (2025).
6.
, , J. Margraf, , and : Low-Cost Periodic Calculations of Metal-Organic Frameworks: A GFN1-xTB Perspective. ChemPhysChem 26 (14), 202500081 (2025).
7.
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.
8.
, E. Keller, , J. Margraf, K. Reuter and : Self-Assembly of Monosized Cyclic Nanoarchitectures under Surface Confinement. ACS Nano 19 (24), 21942–21949 (2025).
9.
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).
10.
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).