Publications

Viktor Zaverkin, David Holzmüller, Luca Bonfirraro, and Johannes Kästner. Transfer learning for chemically accurate interatomic neural network potentials. Phys. Chem. Chem. Phys., (25)7:5383-5396, The Royal Society of Chemistry, 2023. [PUMA: EXC2075 PN3 PN3-4 PN6 PN6-3 PN6A-1 curated] URL

David Holzmüller, Viktor Zaverkin, Johannes Kästner, and Ingo Steinwart. A Framework and Benchmark for Deep Batch Active Learning for Regression. Journal of Machine Learning Research, (24)164:1--81, 2023. [PUMA: EXC2075 PN6 PN6-3 PN6A-1 curated] URL

Viktor Zaverkin, David Holzmüller, Ingo Steinwart, and Johannes Kästner. Exploring chemical and conformational spaces by batch mode deep active learning. Digital Discovery, (1):605-620, 2022. [PUMA: EXC2075 PN6 PN6-3 PN6A-1 curated]

Viktor Zaverkin, David Holzmüller, Robin Schuldt, and Johannes Kästner. Predicting properties of periodic systems from cluster data: A case study of liquid water. The Journal of Chemical Physics, (156)11:114103, 2022. [PUMA: EXC2075 PN3 PN3-4 PN6 PN6-3 PN6A-1 curated] URL

Viktor Zaverkin, David Holzmüller, Ingo Steinwart, and Johannes Kästner. Fast and Sample-Efficient Interatomic Neural Network Potentials for Molecules and Materials Based on Gaussian Moments. Journal of Chemical Theory and Computation, (17)10:6658-6670, 2021. [PUMA: EXC2075 PN6 PN6-3 PN6A-1 curated] URL