D. Holzmüller, V. Zaverkin, J. Kästner, and I. Steinwart. Software, (2022)Related to: David, Holzmüller, Viktor Zaverkin, Johannes Kästner, and Ingo Steinwart. A Framework and Benchmark for Deep Batch Active Learning for Regression, 2022. arXiv: 2203.09410.
D. Holzmüller, V. Zaverkin, J. Kästner, and I. Steinwart. Software, (2022)Related to: David Holzmüller, Viktor Zaverkin, Johannes Kästner, and Ingo Steinwart. A Framework and Benchmark for Deep Batch Active Learning for Regression, 2022. arXiv: 2203.09410.
D. Holzmüller, V. Zaverkin, J. Kästner, and I. Steinwart. Software, (2023)Related to: David Holzmüller, Viktor Zaverkin, Johannes Kästner, and Ingo Steinwart. A Framework and Benchmark for Deep Batch Active Learning for Regression, 2023. arXiv: 2203.09410.
V. Zaverkin, D. Holzmüller, I. Steinwart, and J. Kästner. Software, (2021)Related to: V. Zaverkin, D. Holzmüller, I. Steinwart, and J. Kästner, “Fast and Sample-Efficient Interatomic Neural Network Potentials for Molecules and Materials Based on Gaussian Moments,” J. Chem. Theory Comput. 17, 6658–6670 (2021). doi: 10.1021/acs.jctc.1c00527.
J. Smiatek, N. Hansen, and J. Kästner. Simulating enzyme reactivity : Computational methods in enzyme catalysis, 9, The Royal Society of Chemistry, Cambridge, (2017)
A. Denzel, B. Haasdonk, and J. Kästner. Journal of Physical Chemistry. A, Molecules, spectroscopy, kinetics, environment & general theory, 123 (44):
9600-9611(2019)
V. Zaverkin, D. Holzmüller, L. Bonfirraro, and J. Kästner. Dataset, (2023)Related to: Viktor Zaverkin, David Holzmüller, Luca Bonfirraro, Johannes Kästner. Transfer learning for chemically accurate interatomic neural network potentials, Phys. Chem. Chem. Phys., 2023, 25, 5383-5396. doi: 10.1039/D2CP05793J.
C. Dietrich, R. Schuldt, D. Born, H. Solodenko, G. Schmitz, and J. Kästner. Dataset, (2021)Related to: Carolin A. Dietrich, Robin Schuldt, Daniel Born, Helena Solodenko, Guido Schmitz, and Johannes Kästner "Evaporation and Fragmentation of Organic Molecules in Strong Electric Fields Simulated with DFT" The Journal of Physical Chemistry A, 2020 124 (41), 8633-8642. doi: 10.1021/acs.jpca.0c06887.
K. Gugeler, and J. Kästner. Dataset, (2021)Related to: M. Kirchhof, K. Gugeler, F. R. Fischer, M. Nowakowski, A. Bauer, S. Alvarez-Barcia, K. Abitaev, M. Schnierle, Y. Qawasmi, W. Frey, A. Baro, D. P. Estes, T. Sottmann, M. R. Ringenberg, B. Plietker, M. Bauer, J. Kästner, S. Laschat, Organometallics 2020, 39, 3131-3145. doi: 10.1021/acs.organomet.0c00310.
R. Schuldt, J. Kästner, and S. Naumann. Dataset, (2021)Related to: Schuldt, R., Kästner, J., & Naumann, S. “Proton Affinities of N-Heterocyclic Olefins and Their Implications for Organocatalyst Design.” J. Org. Chem. 84, 2209-2218 (2019). doi: 10.1021/acs.joc.8b03202.
J. Kästner, and M. Kesharwani. Dataset, (2021)Related to: Charge Distribution in Cationic Molybdenum Imido Alkylidene N-Heterocyclic Carbene Complexes: A Combined X-Ray, XAS, XES, DFT, Mössbauer and Catalysis Approach. Mathis Benedikter, Janis Musso, Manoj K. Kesharwani, K. Leonard Sterz, Iris Elser, Felix Ziegler, Felix Fischer, Bernd Plietker, Wolfgang Frey, Johannes Kästner, Mario Winkler, Joris van Slageren, Michal Nowakowski, Matthias Bauer, and Michael R. Buchmeiser. ACS Catalysis 2020, 10, 24, 14810-14823. doi: 10.1021/acscatal.0c03978.
J. Kästner, M. Kesharwani, I. Elser, J. Musso, and M. Buchmeiser. Dataset, (2021)Related to: Kesharwani, M. K., Elser, I., Musso, J. V., Buchmeiser, M. R., & Kästner, J. “Reaction Mechanism of Ring-Closing Metathesis with a Cationic Molybdenum Imido Alkylidene N-Heterocyclic Carbene Catalyst.” Organometallics 39, 3146-3159 (2020). doi: 10.1021/acs.organomet.0c00311.