M. Kesharwani, I. Elser, J. Musso, M. Buchmeiser, und J. Kästner. 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 2020, 39 (17), 3146-3159. doi: 10.1021/acs.organomet.0c00311.
R. Schuldt, J. Kästner, und 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.
P. Probst, J. Groos, D. Wang, K. Gugeler, A. Beck, J. Kästner, W. Frey, und M. Buchmeiser. Dataset, (2024)Related to: Patrick Probst, Jonas Groos, Dongren Wang, Alexander Beck, Katrin Gugeler, Johannes Kästner, Wolfgang Frey, and Michael R. Buchmeiser, Stereoselective Ring Expansion Metathesis Polymerization with Cationic Molybdenum Alkylidyne N-Heterocyclic Carbene Complexes, Journal of the American Chemical Society 2024 146 (12), 8435-8446. doi: 10.1021/jacs.3c14457.
D. Holzmüller, V. Zaverkin, J. Kästner, und 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.
D. Holzmüller, V. Zaverkin, J. Kästner, und 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, und 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.
V. Zaverkin, D. Holzmüller, I. Steinwart, und 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.
D. Holzmüller, V. Zaverkin, J. Kästner, und 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, und 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.
S. Emmerling, R. Schuldt, S. Bette, L. Yao, R. Dinnebier, J. Kästner, und B. Lotsch. Dataset, (2023)Related to: Sebastian T. Emmerling, Robin Schuldt, Sebastian Bette, Liang Yao, Robert E. Dinnebier, Johannes Kästner, and Bettina V. Lotsch. Interlayer Interactions as Design Tool for Large-Pore COFs. J. Am. Chem. Soc. 2021, 143 (38), 15711-15722. doi: 10.1021/jacs.1c06518.