Publications

2024

Anke Silvia Ulrich, Sergey Kasatikov, Till König, Andrea Fantin, Johannes T. Margraf, Mathias Galetz: Decreased Metal Dusting Resistance of Ni-Cu Alloys by Fe and Mn Additions. In: High Temperature Corrosion of Materials, 101 (2024). - S. 1301-1314.
doi:10.1007/s11085-024-10263-w

Wenbin Xu, Elias Diesen, Tianwei He, Karsten Reuter, Johannes T. Margraf: Discovering High Entropy Alloy Electrocatalysts in Vast Composition Spaces with Multiobjective Optimization. In: Journal of the American Chemical Society, 146 (2024). - S. 7698-7707.
doi:10.1021/jacs.3c14486

Philipp Pracht, Yuthika Pillai, Venkat Kapil, Gábor Csányi, Nils Gönnheimer, Martin Vondrák, Johannes T. Margraf, David J. Wales: Efficient Composite Infrared Spectroscopy: Combining the Double-Harmonic Approximation with Machine Learning Potentials. In: Journal of Chemical Theory and Computation, 20 (2024). - S. 10986-11004.
doi:10.1021/acs.jctc.4c01157

Johannes T. Margraf: Neural graph distance embedding for molecular geometry generation. In: Journal of Computational Chemistry, 45 (2024). - S. 1784-1790.
doi:10.1002/jcc.27349

Mengnan Cui, Karsten Reuter, Johannes T. Margraf: Obtaining Robust Density Functional Tight-Binding Parameters for Solids across the Periodic Table. In: Journal of Chemical Theory and Computation, 20 (2024). - S. 5276-5290.
doi:10.1021/acs.jctc.4c00228

Valentina Rein, Hao Gao, Hendrik H. Heenen, Wissal Sghaier, Anastasios C. Manikas, Christos Tsakonas, Mehdi Saedi, Johannes T. Margraf, Costas Galiotis, Gilles Renaud, Oleg V. Konovalov, Irene M. N. Groot, Karsten Reuter, Maciej Jankowski: Operando Characterization and Molecular Simulations Reveal the Growth Kinetics of Graphene on Liquid Copper During Chemical Vapor Deposition. In: ACS Nano, 18 (2024). - S. 12503-12511.
doi:10.1021/acsnano.4c02070

Elisabeth Keller, Jack Morgenstein, Karsten Reuter, Johannes T. Margraf: Small basis set density functional theory method for cost-efficient, large-scale condensed matter simulations. In: The Journal of Chemical Physics, 161 (2024). - .
doi:10.1063/5.0222649

2023

Ke Chen, Christian Kunkel, Bingqing Cheng, Karsten Reuter, Johannes T. Margraf: Physics-Inspired Machine Learning of Localized Intensive Properties. In: Chemical Science, 14 (2023). - S. 4913-4922.
doi:10.1039/D3SC00841J

Martin Vondrák, Karsten Reuter, Johannes T. Margraf: q-pac: A Python Package for Machine Learned Charge Equilibration Models. In: The Journal of Chemical Physics, 159 (2023). - .
doi:10.1063/5.0156290

Johannes T. Margraf: Science-Driven Atomistic Machine Learning. In: Angewandte Chemie International Edition, 62 (2023). - .
doi:10.1002/anie.202219170

Sina Stocker, Hyunwook Jung, Gábor Csányi, C. Franklin Goldsmith, Karsten Reuter, Johannes T. Margraf: Estimating Free Energy Barriers for Heterogeneous Catalytic Reactions with Machine Learning Potentials and Umbrella Integration. In: Journal of Chemical Theory and Computation, 19 (2023). - S. 6796-6804.
doi:10.1021/acs.jctc.3c00541

Johannes T. Margraf, Hyunwook Jung, Christoph Scheurer, Karsten Reuter: Exploring catalytic reaction networks with machine learning. In: Nature Catalysis, 6 (2023). - S. 112-121.
doi:10.1038/s41929-022-00896-y

Hyunwook Jung, Lena Sauerland, Sina Stocker, Karsten Reuter, Johannes T. Margraf: Machine-Learning Driven Global Optimization of Surface Adsorbate Geometries. In: npj Computational Materials, 9 (2023). - .
doi:10.1038/s41524-023-01065-w

2022

Hanna Türk, Elisabetta Landini, Christian Kunkel, Johannes T. Margraf, Karsten Reuter: Assessing Deep Generative Models in Chemical Composition Space. In: Chemistry of Materials, 34 (2022). - S. 9455-9467.
doi:10.1021/acs.chemmater.2c01860

Pierre Kube, Jinhu Dong, Nuria Sánchez Bastardo, Holger Ruland, Robert Schlögl, Johannes T. Margraf, Karsten Reuter, Annette Trunschke: Green synthesis of propylene oxide directly from propane. In: Nature Communications, 13 (2022). - .
doi:10.1038/s41467-022-34967-2

Johannes T. Margraf, Zachary W. Ulissi, Yousung Jung, Karsten Reuter: Heterogeneous Catalysis in Grammar School. In: The Journal of Physical Chemistry C, 126 (2022). - S. 2931-2936.
doi:10.1021/acs.jpcc.1c10285

Sina Stocker, Johannes Gasteiger, Florian Becker, Stephan Günnemann, Johannes T. Margraf: How Robust are Modern Graph Neural Network Potentials in Long and Hot Molecular Dynamics Simulations?. In: Machine Learning: Science and Technology, 3 (2022). - .
doi:10.1088/2632-2153/ac9955

Simon Wengert, Gábor Csányi, Karsten Reuter, Johannes T. Margraf: A Hybrid Machine Learning Approach for Structure Stability Prediction in Molecular Co-Crystal Screenings. In: Journal of Chemical Theory and Computation, 18 (2022). - S. 4586-4593.
doi:10.1021/acs.jctc.2c00343

Carsten G. Staacke, Simon Wengert, Christian Kunkel, Gábor Csányi, Karsten Reuter, Johannes T. Margraf: Kernel Charge Equilibration: Efficient and Accurate Prediction of Molecular Dipole Moments with a Machine-Learning Enhanced Electron Density Model. In: Machine Learning: Science and Technology, 3 (2022). - .
doi:10.1088/2632-2153/ac568d

Elisabeth Keller, Theodoros Tsatsoulis, Karsten Reuter, Johannes T. Margraf: Regularized Second-Order Correlation Methods for Extended Systems. In: The Journal of Chemical Physics, 156 (2022). - .
doi:10.1063/5.0078119

Ke Chen, Christian Kunkel, Karsten Reuter, Johannes T. Margraf: Reorganization energies of flexible organic molecules as a challenging target for machine learning enhanced virtual screening. In: Digital Discovery, 1 (2022). - S. 147-157.
doi:10.1039/D1DD00038A

Carsten G. Staacke, Tabea Huss, Johannes T. Margraf, Karsten Reuter, Christoph Scheurer: Tackling structural complexity in Li₂S-P₂S₅ Solid-State Electrolytes using Machine Learning Potentials. In: Nanomaterials, 12 (2022). - .
doi:10.3390/nano12172950

2021

Christian Kunkel, Johannes T. Margraf, Ke Chen, Harald Oberhofer, Karsten Reuter: Active discovery of organic semiconductors. In: Nature Communications, 12 (2021). - .
doi:10.1038/s41467-021-22611-4

Jakob Timmermann, Yonghyuk Lee, Carsten G. Staacke, Johannes T. Margraf, Christoph Scheurer, Karsten Reuter: Data-Efficient Iterative Training of Gaussian Approximation Potentials: Application to Surface Structure Determination of Rutile IrO₂ and RuO₂. In: The Journal of Chemical Physics, 55 (2021). - .
doi:10.1063/5.0071249

Simon Wengert, Gábor Csányi, Karsten Reuter, Johannes T. Margraf: Data-Efficient Machine Learning for Molecular Crystal Structure Prediction. In: Chemical Science, 12 (2021). - S. 4536-4546.
doi:10.1039/D0SC05765G

Carsten G. Staacke, Hendrik H. Heenen, Christoph Scheurer, Gábor Csányi, Karsten Reuter, Johannes T. Margraf: On the role of long-range electrostatics in machine-learned interatomic potentials for complex battery materials. In: ACS Applied Energy Materials, 4 (2021). - S. 12562-12569.
doi:10.1021/acsaem.1c02363

Johannes T. Margraf, Karsten Reuter: Pure non-local machine-learned density functional theory for electron correlation. In: Nature Communications, 12 (2021). - .
doi:10.1038/s41467-020-20471-y

Haobo Li, Yunxia Liu, Ke Chen, Johannes T. Margraf, Youyong Li, Karsten Reuter: Subgroup discovery points to the prominent role of charge transfer in breaking nitrogen scaling relations at single-atom catalysts on VS₂. In: ACS Catalysis, 11 (2021). - S. 7906-7914.
doi:10.1021/acscatal.1c01324

2020

Annika Stuke, Christian Kunkel, Dorothea Golze, Milica Todorović, Johannes T. Margraf, Karsten Reuter, Patrick Rinke, Harald Oberhofer: Atomic structures and orbital energies of 61,489 crystal-forming organic molecules. In: Scientific Data, 7 (2020). - .
doi:10.1038/s41597-020-0385-y

Hyunwook Jung, Sina Stocker, Christian Kunkel, Harald Oberhofer, Byungchan Han, Karsten Reuter, Johannes T. Margraf: Size-extensive molecular machine learning with global representations. In: ChemSystemsChem, 2 (2020). - .
doi:10.1002/syst.201900052

Johannes T. Margraf, Matthias Hennemann, Timothy Clark: EMPIRE: A highly parallel semiempirical molecular orbital program: 3: Born-Oppenheimer molecular dynamics. In: Journal of Molecular Modeling, 26 (2020). - .
doi:10.1007/s00894-020-4293-z

J. Klicpera, S. Giri, Johannes T. Margraf, S. Günnemann: Fast and Uncertainty-Aware Directional Message Passing for Non-Equilibrium Molecules. - (Paper), Veranstaltung: Workshop on Machine Learning for Molecules, NeurIPS 2020, .

Chiara Panosetti, Artur Engelmann, Lydia Nemec, Karsten Reuter, Johannes T. Margraf: Learning to Use the Force: Fitting Repulsive Potentials in Density-Functional Tight-Binding with Gaussian Process Regression. In: Journal of Chemical Theory and Computation, 16 (2020). - S. 2181-2191.
doi:10.1021/acs.jctc.9b00975

Sina Stocker, Gábor Csányi, Karsten Reuter, Johannes T. Margraf: Machine learning in chemical reaction space. In: Nature Communications, 11 (2020). - .
doi:10.1038/s41467-020-19267-x

Benjamin G. Peyton, Connor Briggs, Ruhee D'Cunha, Johannes T. Margraf, T. Daniel Crawford: Machine-Learning Coupled Cluster Properties through a Density Tensor Representation. In: The Journal of Physical Chemistry A, 124 (2020). - S. 4861-4871.
doi:10.1021/acs.jpca.0c02804

Bingqing Cheng, Ryan-Rhys Griffiths, Simon Wengert, Christian Kunkel, Tamas Stenczel, Bonan Zhu, Volker L. Deringer, Noam Bernstein, Johannes T. Margraf, Karsten Reuter, Gábor Csányi: Mapping Materials and Molecules. In: Accounts of Chemical Research, 53 (2020). - S. 1981-1991.
doi:10.1021/acs.accounts.0c00403

2019

Christian Kunkel, Christoph Schober, Johannes T. Margraf, Karsten Reuter, Harald Oberhofer: Finding the right bricks for molecular legos : A data mining approach to organic semiconductor design. In: Chemistry of Materials, 31 (2019). - S. 969-978.
doi:10.1021/acs.chemmater.8b04436

Alejandro Cadranel, Johannes T. Margraf, Volker Strauss, Timothy Clark, Dirk M. Guldi: Carbon Nanodots for Charge-Transfer Processes. In: Accounts of Chemical Research, 52 (2019). - S. 955-963.
doi:10.1021/acs.accounts.8b00673

Albert Bruix, Johannes T. Margraf, Mie Andersen, Karsten Reuter: First-principles-based multiscale modelling of heterogeneous catalysis. In: Nature Catalysis, 2 (2019). - S. 659-670.
doi:10.1038/s41929-019-0298-3

Johannes T. Margraf, Karsten Reuter: Systematic Enumeration of Elementary Reaction Steps in Surface Catalysis. In: ACS Omega, 4 (2019). - S. 3370-3379.
doi:10.1021/acsomega.8b03200

Johannes T. Margraf, Christian Kunkel, Karsten Reuter: Towards Density Functional Approximations from Coupled Cluster Correlation Energy Densities. In: The Journal of Chemical Physics, 150 (2019). - .
doi:10.1063/1.5094788

Duminda S. Ranasinghe, Johannes T. Margraf, Ajith Perera, Rodney J. Bartlett: Vertical Valence Ionization Potential Benchmarks from Equation-of-Motion Coupled Cluster Theory and QTP Functionals. In: The Journal of Chemical Physics, 150 (2019). - .
doi:10.1063/1.5084728

Johannes T. Margraf, Pavlo O. Dral: What is semiempirical molecular orbital theory approximating?. In: Journal of Molecular Modeling, 25 (2019). - .
doi:10.1007/s00894-019-4005-8