Publikationen von Johannes Margraf

Wengert, S.; Csányi, G.; Reuter, K.; Margraf, J.: Data-efficient machine learning for molecular crystal structure prediction. Chemical Science 12 (12), S. 4536 - 4546 (2021)

Wengert, S.; Kunkel, C.; Margraf, J.; Reuter, K.: Accelerating molecular materials discovery with machine-learning. In: High-Performance Computing and Data Science in the Max Planck Society, S. 40 - 41. Max Planck Computing and Data Facility, Garching (2021)

Margraf, J.: Science Driven Chemical Machine Learning. 12th SolTech Conference 2023, Würzburg, Germany (2023)

Margraf, J.: A Personal Perspective on ML Interatomic Potentials. Crash TEsting machine learning force fields: Applicability, best practices, limitations (TEA 2023), Luxembourg, Luxembourg (2023)

Margraf, J.: Science-Driven Chemical Machine Learning. MCIC 2023: Materials Science Meets Artificial Intelligence – Advancements in Research and Innovation, Bochum, Germany (2023)

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 at the Interface of Physics and Chemistry, 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)