Publications of Johannes Margraf

Journal Article (9)

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
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
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
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
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).
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
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
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
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).

Talk (4)

Working Paper (2)

2022
Working Paper
Stocker, S., J. Gasteiger, F. Becker, S. Günnemann and J. Margraf: How Robust are Modern Graph Neural Network Potentials in Long and Hot Molecular Dynamics Simulations?, in press.
2021
Working Paper
Neumann, F., J. Margraf, K. Reuter and A. Bruix: Interplay between shape and composition in bimetallic nanoparticles revealed by an efficient optimal-exchange optimization algorithm., in press.
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