Publications of Johannes Margraf

Cui, M., K. Reuter and J. Margraf: 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., K. Reuter and J. Margraf: Beyond Numerical Hessians: Higher-Order Derivatives for Machine Learning Interatomic Potentials via Automatic Differentiation. Journal of Chemical Theory and Computation, in press.

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

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

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

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

Jung, H., L. Sauerland, S. Stocker, K. Reuter and J. Margraf: 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., H. Jung, C. Scheurer and K. Reuter: Exploring catalytic reaction networks with machine learning. Nature Catalysis 6 (2), 112–121 (2023).

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

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

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).

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).

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

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

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).

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).

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).

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? Machine Learning: Science and Technology 3 (4), 045010 (2022).