107 citations · 417 across the 22 of their papers we have counts for
3 papers · 1 filter
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…
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…