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.
K. Gugeler, and J. Kästner. Dataset, (2023)Related to: Carolin Rieg, Manuel Kirchhof, Katrin Gugeler, Ann-Katrin Beurer, Lukas Stein, Klaus Dirnberger, Wolfgang Frey, Johanna R. Bruckner, Yvonne Traa, Johannes Kästner, Sabine Ludwigs, Sabine Laschat and Michael Dyballa. Determination of accessibility and spatial distribution of chiral Rh diene complexes immobilized on SBA-15 via phosphine-based solid-state NMR probe molecules. Catal. Sci. Technol. 13, 410-425, 2023. doi: 10.1039/d2cy01578a.
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.
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.
S. Emmerling, R. Schuldt, S. Bette, L. Yao, R. Dinnebier, J. Kästner, and 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.