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Viktor Zaverkin
Tytuł
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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
932020
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
412023
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
352023
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
342022
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
332021
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
262022
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
262020
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
232021
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
182024
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
182021
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
17*2023
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
142022
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
132021
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
132018
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
112023
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
52023
Structure-Aware E (3)-Invariant Molecular Conformer Aggregation Networks
DMH Nguyen, N Lukashina, T Nguyen, AT Le, TT Nguyen, N Ho, J Peters, ...
International Conference on Machine Learning (ICML), 2024
42024
Instanton Theory to Calculate Tunnelling Rates and Tunnelling Splittings
V Zaverkin, J Kästner
32020
Higher-Rank Irreducible Cartesian Tensors for Equivariant Message Passing
V Zaverkin, F Alesiani, T Maruyama, F Errica, H Christiansen, M Takamoto, ...
Neural Information Processing Systems (NeurIPS), 2024
22024
Investigation of chemical reactivity by machine-learning techniques
V Zaverkin
22022
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