107 citations · 179 across the 3 of their papers we have counts for
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physics.comp-ph2021★ 41 cited
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…
physics.comp-ph2021★ 107 cited
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…