Publikationen von Johannes Margraf

Margraf, J.: Data-Efficient Chemical Machine Learning. KAIST Theory Seminar, Seoul, South Korea, Online Event (2022)

Margraf, J.: Data-Efficient Chemical Machine Learning. Institutskolloquium, Institute of Chemistry, University of Potsdam, Online Event (2022)

Margraf, J.: Chemical ML Beyond Established Benchmark Datasets. Workshop, ELLIS Machine Learning (ML) for Molecule Discovery, Online Event (2021)

Margraf, J.: Predicting Molecular Properties Through Machine Learned Energy Functionals. Discussion Meeting, GdR REST Machine Learning (ML), Online Event (2021)

Margraf, J.: Integrating Machine Learning and Electronic Structure Theory. FHI-Workshop on Current Research Topics at the FHI, Online Event (2021)

Margraf, J.: Molecular Machine Learning: From Chemical Space to Reaction Space. FHI-Workshop on Current Research Topics at the FHI, Online Event (2020)

Jakob, K.; Reuter, K.; Margraf, J.: Universally Accurate or Specifically Inadequate? Stress-testing General Purpose Machine Learning Interatomic Potentials. (2025)

Neumann, F.; Margraf, J.; Reuter, K.; Bruix, A.: Interplay between shape and composition in bimetallic nanoparticles revealed by an efficient optimal-exchange optimization algorithm. (2021)