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Viktor Zaverkin

Showing results (1-10 of 6) with videos related to

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Journal of Chemical Theory and Computation|February 5, 2026
Performance of Universal Machine-Learned Potentials with Explicit Long-Range Interactions in Biomolecular SimulationsViktor Zaverkin, Matheus Ferraz, Francesco Alesiani, et al.
The Journal of Chemical Physics|March 23, 2022
Predicting properties of periodic systems from cluster data: A case study of liquid waterViktor Zaverkin, David Holzmüller, Robin Schuldt, et al.
Journal of Chemical Theory and Computation|September 29, 2021
Fast and Sample-Efficient Interatomic Neural Network Potentials for Molecules and Materials Based on Gaussian MomentsViktor Zaverkin, David Holzmüller, Ingo Steinwart, et al.
Physical Chemistry Chemical Physics : PCCP|February 7, 2023
Transfer learning for chemically accurate interatomic neural network potentialsViktor Zaverkin, David Holzmüller, Luca Bonfirraro, et al.
Journal of Chemical Theory and Computation|December 9, 2021
Thermally Averaged Magnetic Anisotropy Tensors via Machine Learning Based on Gaussian MomentsViktor Zaverkin, Julia Netz, Fabian Zills, et al.
The Journal of Chemical Physics|November 11, 2025
Fast, modular, and differentiable framework for machine learning-enhanced molecular simulationsHenrik Christiansen, Takashi Maruyama, Federico Errica, et al.
Pageof 1

Showing results (1-10 of 6) with videos related to

Sort By:
Pageof 1
Journal of Chemical Theory and Computation|February 5, 2026
Performance of Universal Machine-Learned Potentials with Explicit Long-Range Interactions in Biomolecular SimulationsViktor Zaverkin, Matheus Ferraz, Francesco Alesiani, et al.
The Journal of Chemical Physics|March 23, 2022
Predicting properties of periodic systems from cluster data: A case study of liquid waterViktor Zaverkin, David Holzmüller, Robin Schuldt, et al.
Journal of Chemical Theory and Computation|September 29, 2021
Fast and Sample-Efficient Interatomic Neural Network Potentials for Molecules and Materials Based on Gaussian MomentsViktor Zaverkin, David Holzmüller, Ingo Steinwart, et al.
Physical Chemistry Chemical Physics : PCCP|February 7, 2023
Transfer learning for chemically accurate interatomic neural network potentialsViktor Zaverkin, David Holzmüller, Luca Bonfirraro, et al.
Journal of Chemical Theory and Computation|December 9, 2021
Thermally Averaged Magnetic Anisotropy Tensors via Machine Learning Based on Gaussian MomentsViktor Zaverkin, Julia Netz, Fabian Zills, et al.
The Journal of Chemical Physics|November 11, 2025
Fast, modular, and differentiable framework for machine learning-enhanced molecular simulationsHenrik Christiansen, Takashi Maruyama, Federico Errica, et al.
Pageof 1