Publikationen von Johannes Margraf

Wengert, S., G. Csányi, K. Reuter und J. Margraf: A Hybrid Machine Learning Approach for Structure Stability Prediction in Molecular Co-crystal Screenings. Journal of Chemical Theory and Computation 18 (7), 4586–4593 (2022).

Li, H., Y. Liu, K. Chen, J. Margraf, Y. Li und K. Reuter: 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), 7906–7914 (2021).

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

Timmermann, J., Y. Lee, C. Staacke, J. Margraf, C. Scheurer und K. Reuter: 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).

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

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

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