Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Fast and Sample-Efficient Interatomic Neural Network Potentials for
  Molecules and Materials Based on Gaussian Moments   Artificial neural networks (NNs) are one of the most frequently used machine
learning approaches to construct interatomic potentials and enable efficient
large-scale atomistic simulations with almost ab initio accuracy. However, the
simultaneous training of NNs on energies and forces, which are a prerequisite
for, e.g., molecular dynamics simulations, can be demanding. In this work, we
present an improved NN architecture based on the previous GM-NN model [V.
Zaverkin and J. K\"astner, J. Chem. Theory Comput. 16, 5410-5421 (2020)], which
shows an improved prediction accuracy and considerably reduced training times.
Moreover, we extend the applicability of Gaussian moment-based interatomic
potentials to periodic systems and demonstrate the overall excellent
transferability and robustness of the respective models. The fast training by
the improved methodology is a pre-requisite for training-heavy workflows such
as active learning or learning-on-the-fly.

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The categories of this paper are: cond-mat
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The categories of this paper are: plasm-ph
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The categories of this paper are: nucl-ex
24
The categories of this paper are: dg-ga
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The categories of this paper are: hep-ex
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The categories of this paper are: astro-ph
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The categories of this paper are: cs
25
The categories of this paper are: patt-sol
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The categories of this paper are: math-ph
27
The categories of this paper are: comp-gas
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The categories of this paper are: hep-ph
20
The categories of this paper are: acc-phys
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The categories of this paper are: q-alg
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The categories of this paper are: funct-an
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The categories of this paper are: quant-ph
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The categories of this paper are: hep-th