Publikationen von Johannes Margraf

Margraf, J.: Robust and Electrostatics-Aware Machine Learning Potentials. CECAM Psi-k Research Conference, Bridging Length Scales with Machine Learning, Berlin, Germany (2023)

Margraf, J.: Physical Description of Long-Range Interactions in Atomistic Machine Learning Models. Seminars on Machine Learning in Quantum Chemistry and Quantum Computing for Quantum Chemistry (SMLQC), Online Event (2023)

Margraf, J.: Science-Driven Chemical Machine Learning. Colloquium for Theoretical Chemistry, Universität Marburg, Online Event (2023)

Margraf, J.: Science Driven Chemical Machine Learning. Thomas Young Center-FHI Workshop, London, UK (2023)

Margraf, J.: Integrating Machine Learning and Electronic Structure Theory. Seminar, Department of Chemistry, Humboldt-Universität zu Berlin, Berlin, Germany (2023)

Margraf, J.: Science Driven Chemical Machine Learning. Seminar, Debye Institute for Nanomaterials Science, Utrecht University, Utrecht, The Netherlands (2022)

Margraf, J.: ∆-Learning with DFTB: What makes a good baseline? Workshop, Multi-Scale Quantum Mechanical Analysis of Condensed Phase Systems: Methods and Applications, Telluride Science Research Center, Telluride, CO, USA (2022)

Margraf, J.: Predicting Molecular Properties through Machine Learned Energy Functionals. Seminar, VirtMat, Karlsruhe Institute of Technology (KIT), Online Event (2022)

Margraf, J.: Heterogeneous Catalysis in Grammar School. FHI-Workshop on Current Research Topics at the FHI, Potsdam, Germany (2022)

Margraf, J.: Predicting Molecular Properties through Machine Learned Energy Functionals. ML4M 2022, Young Researcher’s Workshop on Machine Learning for Materials 2022, Trieste, italy (2022)

Margraf, J.: Describing Complex Polar Materials With Physics-Enhanced Machine Learning. ACS Spring Meeting 2022, Symposium, Complexity in Computational Catalysis: Balancing Model and Method Accuracy: Machine Learning and Kinetic Modeling, Online Event (2022)

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)

Cui, M.: Density Functional Tight Binding Theory Informed Multi-fidelity Machine Learning. Dissertation, Humboldt-Universität zu, Berlin (2025)

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)