Publikationen von Christian Kunkel

Felsen, F., C. Kunkel, K. Reuter und C. Scheurer: Adaptive designs for the exploration of reaction kinetic phase transitions. Chemical Engineering Journal Advances 22, 100746 (2025).

Kunkel, C., F. Rüther, F. Felsen, C.W.P. Pare, A. Terzi, R. Baumgarten, E. Gioria, R.N. d’Alnoncourt, C. Scheurer, F. Rosowski und K. Reuter: Systematic Exploration of a Multi-Promoter Catalyst Composition Space with Limited Experiments: Non-Oxidative Propane Dehydrogenation to Propylene. ACS Catalysis 14 (11), 9008–9017 (2024).

Li, Z., E. Öztuna, K. Skorupska, O. Vinogradova, A. Jamshaid, A. Steigert, C. Rohner, M. Dimitrakopoulou, M. Prieto, C. Kunkel, M. Stredansky, P. Kube, M. Götte, A. Dudzinski, F. Girgsdies, S. Wrabetz, W. Frandsen, R. Blume, P. Zeller, M. Muske, D. Delgado Muñoz, S. Jiang, F. Schmidt, T. Köchler, M. Arztmann, A. Efimenko, J. Frisch, T. Kokumai, R. Garcia-Diez, M. Bär, A. Hammud, J. Kröhnert, A. Trunschke, C. Scheurer, T. Schmidt, T. Lunkenbein, D. Amkreutz, H. Kuhlenbeck, V.J. Bukas, A. Knop-Gericke, R. Schlatmann, K. Reuter, B. Roldan Cuenya und R. Schlögl: Rationally Designed Laterally-Condensed-Catalysts Deliver Robust Activity and Selectivity for Ethylene Production in Acetylene Hydrogenation. Nature Communications 15, 10660 (2024).

Chen, K., C. Kunkel, B. Cheng, K. Reuter und J. Margraf: Physics-inspired machine learning of localized intensive properties. Chemical Science 14 (18), 4913–4922 (2023).

Ghan, S., E. Diesen, C. Kunkel, K. Reuter und H. Oberhofer: Interpreting ultrafast electron transfer on surfaces with a converged first-principles Newns–Anderson chemisorption function. The Journal of Chemical Physics 158 (23), 234103 (2023).

Chen, K., C. Kunkel, K. Reuter und 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).

Staacke, C., S. Wengert, C. Kunkel, G. Csányi, K. Reuter und 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).

Türk, H., E. Landini, C. Kunkel, J. Margraf und K. Reuter: Assessing Deep Generative Models in Chemical Composition Space. Chemistry of Materials 34 (21), 9455–9467 (2022).

Wengert, S., C. Kunkel, J. Margraf und 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).

Kunkel, C.: Data-driven Organic Semiconductor Discovery. Technische Universität München