Publikationen von Johannes Margraf

Keller, E.; Tsatsoulis, T.; Reuter, K.; Margraf, J.: Regularized second-order correlation methods for extended systems. The Journal of Chemical Physics 156 (2), 024106 (2022)

Timmermann, J.; Lee, Y.; Staacke, C.; Margraf, J.; Scheurer, C.; Reuter, K.: Data-Efficient Iterative Training of Gaussian Approximation Potentials: Application to Surface Structure Determination of Rutile IrO₂ and RuO₂. The Journal of Chemical Physics 155 (24), 244107 (2021)

Staacke, C.; Heenen, H.; Scheurer, C.; Csányi , G.; Reuter, K.; Margraf, J.: On the Role of Long-Range Electrostatics in Machine-Learned Interatomic Potentials for Complex Battery Materials. ACS Applied Energy Materials 4 (11), S. 12562 - 12569 (2021)

Li, H.; Liu, Y.; Chen, K.; Margraf, J.; Li, Y.; Reuter, K.: Subgroup Discovery Points to the Prominent Role of Charge Transfer in Breaking Nitrogen Scaling Relations at Single-Atom Catalysts on VS₂. ACS Catalysis 11 (13), S. 7906 - 7914 (2021)

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 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)