Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Machine Learning for Novel Thermal-Materials Discovery: Early Successes,
  Opportunities, and Challenges   High-throughput computational and experimental design of materials aided by
machine learning have become an increasingly important field in material
science. This area of research has emerged in leaps and bounds in the thermal
sciences, in part due to the advances in computational and experimental methods
in obtaining thermal properties of materials. In this paper, we provide a
current overview of some of the recent work and highlight the challenges and
opportunities that are ahead of us in this field. In particular, we focus on
the use of machine learning and high-throughput methods for screening of
thermal conductivity for compounds, composites and alloys as well as
interfacial thermal conductance. These new tools have brought about a feedback
mechanism for understanding new correlations and identifying new descriptors,
speeding up the discovery of novel thermal functional materials.

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The categories of this paper are: gr-qc
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The categories of this paper are: mtrl-th
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The categories of this paper are: atom-ph
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The categories of this paper are: adap-org
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The categories of this paper are: stat
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The categories of this paper are: math-ph
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The categories of this paper are: acc-phys
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The categories of this paper are: astro-ph
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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: nucl-ex
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The categories of this paper are: dg-ga
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The categories of this paper are: patt-sol
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The categories of this paper are: chem-ph
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The categories of this paper are: hep-ph
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The categories of this paper are: q-alg