Publications of Johannes Margraf

Journal Article (11)

2022
Journal Article
Chen, K., C. Kunkel, K. Reuter and J. Margraf: Reorganization energies of flexible organic molecules as a challenging target for machine learning enhanced virtual screening. Digital Discovery 1 (2), 147–157 (2022).
Journal Article
Keller, E., T. Tsatsoulis, K. Reuter and J. Margraf: Regularized second-order correlation methods for extended systems. The Journal of Chemical Physics 156 (2), 024106 (2022).
Journal Article
Levin, N., J. Margraf, J. Lengyel, K. Reuter, M. Tschurl and U. Heiz: CO2-Activation by size-selected tantalum cluster cations (Ta1–16+): thermalization governing reaction selectivity. Physical Chemistry Chemical Physics 24 (4), 2623–2629 (2022).
Journal Article
Margraf, J., Z.W. Ulissi, Y. Jung and K. Reuter: Heterogeneous Catalysis in Grammar School. The Journal of Physical Chemistry C 126 (6), 2931–2936 (2022).
Journal Article
Staacke, C., T. Huss, J. Margraf, K. Reuter and C. Scheurer: Tackling Structural Complexity in Li2 S-P2S5 Solid-State Electrolytes Using Machine Learning Potentials. Nanomaterials 12 (17), 2950 (2022).
Journal Article
Staacke, C., S. Wengert, C. Kunkel, G. Csányi, K. Reuter and J. Margraf: Kernel charge equilibration: efficient and accurate prediction of molecular dipole moments with a machine-learning enhanced electron density model. Machine Learning: Science and Technology 3 (1), 015032 (2022).
Journal Article
Wengert, S., G. Csányi, K. Reuter and 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).
2021
Journal Article
Li, H., Y. Liu, K. Chen, J. Margraf, Y. Li and 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).
Journal Article
Staacke, C., H. Heenen, C. Scheurer, G. Csányi , K. Reuter and 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).
Journal Article
Timmermann, J., Y. Lee, C. Staacke, J. Margraf, C. Scheurer and 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).
Journal Article
Wengert, S., G. Csányi, K. Reuter and J. Margraf: Data-efficient machine learning for molecular crystal structure prediction. Chemical Science 12 (12), 4536–4546 (2021).

Book Chapter (1)

2021
Book Chapter
Wengert, S., C. Kunkel, J. Margraf and 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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