Publications of Johannes Margraf

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).

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).

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).

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).

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).

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).

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).

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).

Wengert, S., G. Csányi, K. Reuter and J. Margraf: Data-efficient machine learning for molecular crystal structure prediction. Chemical Science 12 (12), 4536–4546 (2021).

Wengert, S., C. Kunkel, J. Margraf and K. Reuter: Accelerating molecular materials discovery with machine-learning. In: High-Performance Computing and Data Science in the Max Planck Society. Max Planck Computing and Data Facility, Garching, 40–41 (2021).

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