Publications of Johannes Margraf

Journal Article (21)

Journal Article
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)
Journal Article
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)
Journal Article
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)
Journal Article
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)
Journal Article
Margraf, J.: Science-Driven Atomistic Machine Learning. Angewandte Chemie International Edition 62 (26), e202219170 (2023)
Journal Article
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)
Journal Article
Margraf, J.; Jung, H.; Scheurer, C.; Reuter, K.: Exploring catalytic reaction networks with machine learning. Nature Catalysis 6 (2), pp. 112 - 121 (2023)
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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)
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Türk, H.; Landini, E.; Kunkel, C.; Margraf, J.; Reuter, K.: Assessing Deep Generative Models in Chemical Composition Space. Chemistry of Materials 34 (21), pp. 9455 - 9467 (2022)
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Stocker, S.; Gasteiger, J.; Becker, F.; Günnemann, S.; Margraf, J.: 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
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)
Journal Article
Wengert, S.; Csányi, G.; Reuter, K.; Margraf, J.: A Hybrid Machine Learning Approach for Structure Stability Prediction in Molecular Co-crystal Screenings. Journal of Chemical Theory and Computation 18 (7), pp. 4586 - 4593 (2022)
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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)
Journal Article
Staacke, C.; Wengert, S.; Kunkel, C.; Csányi, G.; Reuter, K.; Margraf, J.: 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.; Ulissi, Z. W.; Jung, Y.; Reuter, K.: Heterogeneous Catalysis in Grammar School. The Journal of Physical Chemistry C 126 (6), pp. 2931 - 2936 (2022)
Journal Article
Levin, N.; Margraf, J.; Lengyel, J.; Reuter, K.; Tschurl, M.; Heiz, U.: CO2-Activation by size-selected tantalum cluster cations (Ta1–16+): thermalization governing reaction selectivity. Physical Chemistry Chemical Physics 24 (4), pp. 2623 - 2629 (2022)
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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)
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Timmermann, J.; Lee, Y.; Staacke, C.; Margraf, J.; Scheurer, C.; Reuter, K.: 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.; Heenen, H.; Scheurer, C.; Csányi , G.; Reuter, K.; Margraf, J.: On the Role of Long-Range Electrostatics in Machine-Learned Interatomic Potentials for Complex Battery Materials. ACS Applied Energy Materials 4 (11), pp. 12562 - 12569 (2021)
Journal Article
Li, H.; Liu, Y.; Chen, K.; Margraf, J.; Li, Y.; Reuter, K.: 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), pp. 7906 - 7914 (2021)
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