Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Predicting tensorial molecular properties with equivariant
  machine-learning models   Embedding molecular symmetries into machine-learning models is key for
efficient learning of chemico-physical scalar properties, but little evidence
on how to extend the same strategy to tensorial quantities exists. Here we
formulate a scalable equivariant machine-learning model based on local atomic
environment descriptors. We apply it to a series of molecules and show that
accurate predictions can be achieved for a comprehensive list of dielectric and
magnetic tensorial properties of different ranks. These results show that
equivariant models are a promising platform to extend the scope of machine
learning in materials modelling.

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The categories of this paper are: q-bio
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The categories of this paper are: quant-ph
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The categories of this paper are: stat
31
The categories of this paper are: ao-sci
14
The categories of this paper are: math-ph
26
The categories of this paper are: bayes-an
28
The categories of this paper are: alg-geom
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The categories of this paper are: chem-ph
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The categories of this paper are: mtrl-th
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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: gr-qc
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The categories of this paper are: chao-dyn
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The categories of this paper are: astro-ph
3
The categories of this paper are: physics
27
The categories of this paper are: comp-gas