Viktor Zaverkin

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Johannes KästnerUniversity of StuttgartVerified email at theochem.uni-stuttgart.de
David HolzmüllerPostdoc, INRIAVerified email at inria.fr
Germán MolpeceresInstituto de Física Fundamental (IFF-CSIC)Verified email at iff.csic.es
Ingo SteinwartUniversity of StuttgartVerified email at mathematik.uni-stuttgart.de
Fabian ZillsPh.D. Student, Institute of Computational Physics, University of StuttgartVerified email at icp.uni-stuttgart.de
Prashanth SrinivasanHumboldt Fellow | Post-Doctoral ResearcherVerified email at imw.uni-stuttgart.de
Andrew Ian DuffDaresbury LaboratoryVerified email at stfc.ac.uk
Blazej GrabowskiInstitute for Materials Science, University of StuttgartVerified email at imw.uni-stuttgart.de
Kenji FuruyaNational Astronomical Observatory of JapanVerified email at nao.ac.jp
Thanja LambertsLeiden UniversityVerified email at lic.leidenuniv.nl
Mathias NiepertUniversity of Stuttgart & NEC Labs EuropeVerified email at ki.uni-stuttgart.de
Federico ErricaResearch Scientist, NEC Laboratories EuropeVerified email at neclab.eu
Henrik ChristiansenNEC Laboratories EuropeVerified email at neclab.eu
Francesco AlesianiNEC Laboratories EuropeVerified email at neclab.eu
Daniel SonntagDFKI and University of OldenburgVerified email at dfki.de
JoJo NguyenFreelanceVerified email at ifn-magdeburg.de
Tai NGUYENAI Researcher, Ex-Research Engineer @ Samsung SDSVerified email at samsung.com
TrungTin NguyenPostdoctoral Research Fellow at The University of Queensland, AustraliaVerified email at uq.edu.au
Nhat HoProfessor at University of Texas, AustinVerified email at utexas.edu
An Thai LeTechnische Universität DarmstadtVerified email at tu-darmstadt.de

Viktor Zaverkin

Other namesV Zaverkin, V A Zaverkin

NEC Laboratories Europe

Verified email at neclab.eu

Machine Learning Deep Learning Computational Chemistry Computational Materials Science


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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	72	2020
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	25	2023
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
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	25	2021
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	25	2020
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	20	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	16	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
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
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	11	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	10	2022
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	6	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	5*	2023
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
Uncertainty-biased molecular dynamics for learning uniformly accurate interatomic potentials V Zaverkin, D Holzmüller, H Christiansen, F Errica, F Alesiani, ...	1	2023
Structure-Aware E (3)-Invariant Molecular Conformer Aggregation Networks DMH Nguyen, N Lukashina, T Nguyen, AT Le, TT Nguyen, N Ho, J Peters, ... arXiv preprint arXiv:2402.01975, 2024		2024

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