Publications of Johannes Margraf

Cao, Y.; Keller, E.; Rowen, J. F.; Margraf, J.; Reuter, K.; Morgenstern, K.: Self-Assembly of Monosized Cyclic Nanoarchitectures under Surface Confinement. ACS Nano 19 (24), pp. 21942 - 21949 (2025)

Cui, M.; Reuter, K.; Margraf, J.: Multi-fidelity transfer learning for quantum chemical data using a robust density functional tight binding baseline. Machine Learning: Science and Technology 6 (1), 015071 (2025)

Gönnheimer, N.; Reuter, K.; Margraf, J.: Beyond Numerical Hessians: Higher-Order Derivatives for Machine Learning Interatomic Potentials via Automatic Differentiation. Journal of Chemical Theory and Computation 21 (9), pp. 4742 - 4752 (2025)

Keller, E.; Blum, V.; Reuter, K.; Margraf, J.: Exploring atom-pairwise and many-body dispersion corrections for the BEEF-vdW functional. The Journal of Chemical Physics 162 (07), 074111 (2025)

Pokora, M.; Goclon, J.; Margraf, J.; Panosetti, C.; Samtsevych, A.; Paneth, P.: Low-Cost Periodic Calculations of Metal-Organic Frameworks: A GFN1-xTB Perspective. ChemPhysChem, 202500081 (2025)

Keller, E.; Morgenstein, J.; Reuter, K.; Margraf, J.: Small basis set density functional theory method for cost-efficient, large-scale condensed matter simulations. The Journal of Chemical Physics 161 (7), 074104 (2024)

Rein, V.; Gao, H.; Heenen, H.; Sghaier, W.; Manikas, A. C.; Tsakonas, C.; Saedi, M.; Margraf, J.; Galiotis, C.; Renaud, G. et al.; Konovalov, O. V.; Groot, I. M. N.; Reuter, K.; Jankowski, M.: Operando Characterization and Molecular Simulations Reveal the Growth Kinetics of Graphene on Liquid Copper During Chemical Vapor Deposition. ACS Nano 18 (19), pp. 12503 - 12511 (2024)

Xu, W.; Diesen, E.; He, T.; Reuter, K.; Margraf, J.: Discovering High Entropy Alloy Electrocatalysts in Vast Composition Spaces with Multiobjective Optimization. Journal of the American Chemical Society 146 (11), pp. 7698 - 7707 (2024)

Chen, K.; Kunkel, C.; Cheng, B.; Reuter, K.; Margraf, J.: Physics-inspired machine learning of localized intensive properties. Chemical Science 14 (18), pp. 4913 - 4922 (2023)

Jung, H.; Sauerland, L.; Stocker, S.; Reuter, K.; Margraf, J.: Machine-learning driven global optimization of surface adsorbate geometries. npj Computational Materials 9, 114 (2023)

Margraf, J.: Science-Driven Atomistic Machine Learning. Angewandte Chemie International Edition 62 (26), e202219170 (2023)

Margraf, J.; Jung, H.; Scheurer, C.; Reuter, K.: Exploring catalytic reaction networks with machine learning. Nature Catalysis 6 (2), pp. 112 - 121 (2023)

Stocker, S.; Jung, H.; Csányi, G.; Goldsmith, C. F.; Reuter, K.; Margraf, J.: Estimating Free Energy Barriers for Heterogeneous Catalytic Reactions with Machine Learning Potentials and Umbrella Integration. Journal of Chemical Theory and Computation 19 (19), pp. 6796 - 6804 (2023)

Vondrák, M.; Reuter, K.; Margraf, J.: q-pac: A Python package for machine learned charge equilibration models. The Journal of Chemical Physics 159 (5), 054109 (2023)

Chen, K.; Kunkel, C.; Reuter, K.; Margraf, J.: Reorganization energies of flexible organic molecules as a challenging target for machine learning enhanced virtual screening. Digital Discovery 1 (2), pp. 147 - 157 (2022)

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)

Kube, P.; Dong, J.; Sánchez Bastardo, N.; Ruland, H.; Schlögl, R.; Margraf, J.; Reuter, K.; Trunschke, A.: Green synthesis of propylene oxide directly from propane. Nature Communications 13, 7504 (2022)

Levin, N.; Margraf, J.; Lengyel, J.; Reuter, K.; Tschurl, M.; Heiz, U.: CO₂-Activation by size-selected tantalum cluster cations (Ta_1–16⁺): thermalization governing reaction selectivity. Physical Chemistry Chemical Physics 24 (4), pp. 2623 - 2629 (2022)

Margraf, J.; Ulissi, Z. W.; Jung, Y.; Reuter, K.: Heterogeneous Catalysis in Grammar School. The Journal of Physical Chemistry C 126 (6), pp. 2931 - 2936 (2022)

Staacke, C.; Huss, T.; Margraf, J.; Reuter, K.; Scheurer, C.: Tackling Structural Complexity in Li₂ S-P₂S₅ Solid-State Electrolytes Using Machine Learning Potentials. Nanomaterials 12 (17), 2950 (2022)