Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Unified Representation of Molecules and Crystals for Machine Learning   Accurate simulations of atomistic systems from first principles are limited
by computational cost. In high-throughput settings, machine learning can
potentially reduce these costs significantly by accurately interpolating
between reference calculations. For this, kernel learning approaches crucially
require a single Hilbert space accommodating arbitrary atomistic systems. We
introduce a many-body tensor representation that is invariant to translations,
rotations and nuclear permutations of same elements, unique, differentiable,
can represent molecules and crystals, and is fast to compute. Empirical
evidence is presented for energy prediction errors below 1 kcal/mol for 7k
organic molecules and 5 meV/atom for 11k elpasolite crystals. Applicability is
demonstrated for phase diagrams of Pt-group/transition-metal binary systems.

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The categories of this paper are: plasm-ph
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The categories of this paper are: atom-ph
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The categories of this paper are: patt-sol
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The categories of this paper are: nlin
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The categories of this paper are: eess
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The categories of this paper are: chem-ph
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The categories of this paper are: adap-org
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The categories of this paper are: acc-phys
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The categories of this paper are: comp-gas
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The categories of this paper are: bayes-an
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The categories of this paper are: cmp-lg
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The categories of this paper are: math-ph
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The categories of this paper are: dg-ga
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The categories of this paper are: math
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The categories of this paper are: astro-ph
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The categories of this paper are: solv-int