| Gaussian moments as physically inspired molecular descriptors for accurate and scalable machine learning potentials V Zaverkin, J Kästner Journal of Chemical Theory and Computation 16 (8), 5410-5421, 2020 | 81 | 2020 |
| Fast and sample-efficient interatomic neural network potentials for molecules and materials based on Gaussian moments V Zaverkin, D Holzmüller, I Steinwart, J Kästner Journal of Chemical Theory and Computation 17 (10), 6658-6670, 2021 | 30 | 2021 |
| A framework and benchmark for deep batch active learning for regression D Holzmüller, V Zaverkin, J Kästner, I Steinwart Journal of Machine Learning Research 24 (164), 1-81, 2023 | 28 | 2023 |
| Neural-network assisted study of nitrogen atom dynamics on amorphous solid water–I. adsorption and desorption G Molpeceres, V Zaverkin, J Kästner Monthly notices of the Royal Astronomical Society 499 (1), 1373-1384, 2020 | 26 | 2020 |
| Predicting properties of periodic systems from cluster data: A case study of liquid water V Zaverkin, D Holzmüller, R Schuldt, J Kästner The Journal of Chemical Physics 156 (11), 2022 | 25 | 2022 |
| Exploring chemical and conformational spaces by batch mode deep active learning V Zaverkin, D Holzmüller, I Steinwart, J Kästner Digital Discovery 1 (5), 605-620, 2022 | 24 | 2022 |
| Transfer learning for chemically accurate interatomic neural network potentials V Zaverkin, D Holzmüller, L Bonfirraro, J Kästner Physical Chemistry Chemical Physics 25 (7), 5383-5396, 2023 | 21 | 2023 |
| Exploration of transferable and uniformly accurate neural network interatomic potentials using optimal experimental design V Zaverkin, J Kästner Machine Learning: Science and Technology 2 (3), 035009, 2021 | 16 | 2021 |
| Thermally averaged magnetic anisotropy tensors via machine learning based on Gaussian moments V Zaverkin, J Netz, F Zills, A Köhn, J Kästner Journal of Chemical Theory and Computation 18 (1), 1-12, 2021 | 15 | 2021 |
| Neural-network assisted study of nitrogen atom dynamics on amorphous solid water–II. Diffusion V Zaverkin, G Molpeceres, J Kästner Monthly Notices of the Royal Astronomical Society 510 (2), 3063-3070, 2022 | 13 | 2022 |
| Binding energies and sticking coefficients of H2 on crystalline and amorphous CO ice G Molpeceres, V Zaverkin, N Watanabe, J Kästner Astronomy & Astrophysics 648, A84, 2021 | 13 | 2021 |
| Tunnelling dominates the reactions of hydrogen atoms with unsaturated alcohols and aldehydes in the dense medium V Zaverkin, T Lamberts, MN Markmeyer, J Kästner Astronomy & Astrophysics 617, A25, 2018 | 13 | 2018 |
| Reaction dynamics on amorphous solid water surfaces using interatomic machine-learned potentials G Molpeceres, V Zaverkin, K Furuya, Y Aikawa, J Kästner Astronomy & Astrophysics 673, A51, 2023 | 9 | 2023 |
| Performance of two complementary machine-learned potentials in modelling chemically complex systems K Gubaev, V Zaverkin, P Srinivasan, AI Duff, J Kästner, B Grabowski npj Computational Materials 9 (1), 129, 2023 | 6* | 2023 |
| Uncertainty-biased molecular dynamics for learning uniformly accurate interatomic potentials V Zaverkin, D Holzmüller, H Christiansen, F Errica, F Alesiani, ... npj Computational Materials 10 (1), 83, 2024 | 4 | 2024 |
| Investigation of chemical reactivity by machine-learning techniques V Zaverkin | 2 | 2022 |
| Instanton Theory to Calculate Tunnelling Rates and Tunnelling Splittings V Zaverkin, J Kästner | 2 | 2020 |
| Adaptive Message Passing: A General Framework to Mitigate Oversmoothing, Oversquashing, and Underreaching F Errica, H Christiansen, V Zaverkin, T Maruyama, M Niepert, F Alesiani https://arxiv.org/abs/2312.16560, 2023 | 1 | 2023 |
| Physics-Informed Weakly Supervised Learning for Interatomic Potentials M Takamoto, V Zaverkin, M Niepert arXiv preprint arXiv:2408.05215, 2024 | | 2024 |
| Higher-Rank Irreducible Cartesian Tensors for Equivariant Message Passing V Zaverkin, F Alesiani, T Maruyama, F Errica, H Christiansen, M Takamoto, ... arXiv preprint arXiv:2405.14253, 2024 | | 2024 |
Johannes KästnerUniversity of Stuttgart在 theochem.uni-stuttgart.de 的电子邮件经过验证
