Publications of Johannes Margraf
All genres
Journal Article (25)
Journal Article
M. Cui, 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).
Journal Article
E. Keller, , 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).
Journal Article
E. Keller, , 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).
Journal Article
, H. Gao, H. Heenen, , , , , J. Margraf, , , , , K. Reuter and : 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).
Journal Article
W. Xu, E. Diesen, , 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).
Journal Article
S. Stocker, H. Jung, , 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).
Journal Article
M. Vondrák, 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).
Journal Article
H. Jung, L. Sauerland, S. Stocker, K. Reuter and J. Margraf: Machine-learning driven global optimization of surface adsorbate geometries. npj Computational Materials 9, 114 (2023).
Journal Article
J. Margraf: Science-Driven Atomistic Machine Learning. Angewandte Chemie International Edition 62 (26), e202219170 (2023).
Journal Article
K. Chen, C. Kunkel, , K. Reuter and J. Margraf: Physics-inspired machine learning of localized intensive properties. Chemical Science 14 (18), 4913–4922 (2023).
Journal Article
J. Margraf, , C. Scheurer and K. Reuter: Exploring catalytic reaction networks with machine learning. Nature Catalysis 6 (2), 112–121 (2023).
Journal Article
P. Kube, J. Dong, , , R. Schlögl, J. Margraf, K. Reuter and A. Trunschke: Green synthesis of propylene oxide directly from propane. Nature Communications 13, 7504 (2022).
Journal Article
H. Türk, , C. Kunkel, J. Margraf and K. Reuter: Assessing Deep Generative Models in Chemical Composition Space. Chemistry of Materials 34 (21), 9455–9467 (2022).
Journal Article
S. Stocker, , , 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).
Journal Article
C. Staacke, 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
S. Wengert, , 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).
Journal Article
K. Chen, 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
C. Staacke, S. Wengert, C. Kunkel, , 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
J. Margraf, , and K. Reuter: Heterogeneous Catalysis in Grammar School. The Journal of Physical Chemistry C 126 (6), 2931–2936 (2022).
Journal Article
, J. Margraf, , K. Reuter, and : CO2-Activation by size-selected tantalum cluster cations (Ta1–16+): thermalization governing reaction selectivity. Physical Chemistry Chemical Physics 24 (4), 2623–2629 (2022).