107 citations · 179 across the 3 of their papers we have counts for
4 papers
Fast and Sample-Efficient Interatomic Neural Network Potentials for Molecules and Materials Based on Gaussian Moments
Viktor Zaverkin, David Holzmüller, Ingo Steinwart +1
Artificial neural networks (NNs) are one of the most frequently used machine learning approaches to construct interatomic potentials and enable efficient large-scale atomistic simu…
Gaussian Moments as Physically Inspired Molecular Descriptors for Accurate and Scalable Machine Learning Potentials
Viktor Zaverkin, Johannes Kästner
Machine learning techniques allow a direct mapping of atomic positions and nuclear charges to the potential energy surface with almost ab-initio accuracy and the computational effi…
Neural-Network Assisted Study of Nitrogen Atom Dynamics on Amorphous Solid Water. I. Adsorption & Desorption
Germán Molpeceres, Viktor Zaverkin, Johannes Kästner
Dynamics of adsorption and desorption of (4S)-N on amorphous solid water are analyzed using molecular dynamics simulations. The underlying potential energy surface was provided by…
Tunnelling dominates the reactions of hydrogen atoms with unsaturated alcohols and aldehydes in the dense medium
V. Zaverkin, T. Lamberts, M. N. Markmeyer +1
Hydrogen addition and abstraction reactions play an important role as surface reactions in the buildup of complex organic molecules in the dense interstellar medium. Addition react…