Publications of Johannes Margraf

Journal Article (21)

2024
Journal Article
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).
2023
Journal Article
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).
Journal Article
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).
Journal Article
Margraf, J.: Science-Driven Atomistic Machine Learning. Angewandte Chemie International Edition 62 (26), e202219170 (2023).
Journal Article
Margraf, J., H. Jung, C. Scheurer and K. Reuter: Exploring catalytic reaction networks with machine learning. Nature Catalysis 6 (2), 112–121 (2023).
Journal Article
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).
Journal Article
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).
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
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).
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
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).
Journal Article
Türk, H., E. Landini, 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
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).
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