54 citations · 181 across the 8 of their papers we have counts for
8 papers
Reaction dynamics on amorphous solid water surfaces using interatomic machine learned potentials. Microscopic energy partition revealed from the P + H -> PH reaction
Germán Molpeceres, Viktor Zaverkin, Kenji Furuya +2
Energy redistribution after a chemical reaction is one of the few mechanisms to explain the diffusion and desorption of molecules which require more energy than the thermal energy…
Hydrogen abstraction reactions in formic and thioformic acid isomers by hydrogen and deuterium atoms. Insights on isomerism and deuteration
Germán Molpeceres, Izaskun Jiménez-Serra, Yasuhiro Oba +6
The isomerism of molecules in the interstellar medium and the mechanisms behind it are essential questions in the chemistry of organic molecules in space. In particular, for the si…
Suppression of Quasiperiodicity in Circle Maps with Quenched Disorder
David Müller-Bender, Johann Luca Kastner, Günter Radons
We show that introducing quenched disorder into a circle map leads to the suppression of quasiperiodic behavior in the limit of large system sizes. Specifically, for most parameter…
Neural-Network Assisted Study of Nitrogen Atom Dynamics on Amorphous Solid Water -- II. Diffusion
Viktor Zaverkin, Germán Molpeceres, Johannes Kästner
The diffusion of atoms and radicals on interstellar dust grains is a fundamental ingredient for predicting accurate molecular abundances in astronomical environments. Quantitative…
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…
Potential energy surface interpolation with neural networks for instanton rate calculations
April M. Cooper, Philipp P. Hallmen, Johannes Kästner
Artificial neural networks are used to fit a potential energy surface. We demonstrate the benefits of using not only energies, but also their first and second derivatives as traini…