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David Holzmüller

1 paper here

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • middle author1

Across the 1 of 1 paper where every author was matched, so the position is known.

fields
  • physics.comp-ph1

identity via Semantic Scholar / OpenAlex

most citedFast and Sample-Efficient Interatomic Neural Network Potentials for Molecules and Materials Based on Gaussian Moments

41 citations · 41 across the 1 of their papers we have counts for

collaborators

1 paper

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…

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Not affiliated with arXiv. Researcher data from Semantic Scholar (ODC-BY) and OpenAlex.