Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Prediction of Large Magnetic Moment Materials With Graph Neural Networks
  and Random Forests   Magnetic materials are crucial components of many technologies that could
drive the ecological transition, including electric motors, wind turbine
generators and magnetic refrigeration systems. Discovering materials with large
magnetic moments is therefore an increasing priority. Here, using
state-of-the-art machine learning methods, we scan the Inorganic Crystal
Structure Database (ICSD) of hundreds of thousands of existing materials to
find those that are ferromagnetic and have large magnetic moments. Crystal
graph convolutional neural networks (CGCNN), materials graph network (MEGNet)
and random forests are trained on the Materials Project database that contains
the results of high-throughput DFT predictions. For random forests, we use a
stochastic method to select nearly one hundred relevant descriptors based on
chemical composition and crystal structure. This turns out to give results for
the test sets that are comparable to those of neural networks. The comparison
between these different machine learning approaches gives an estimate of the
errors for our predictions on the ICSD database.

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The categories of this paper are: math-ph
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The categories of this paper are: astro-ph
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The categories of this paper are: eess
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The categories of this paper are: nucl-th
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The categories of this paper are: hep-ex
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The categories of this paper are: alg-geom
2
The categories of this paper are: math
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The categories of this paper are: econ
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The categories of this paper are: chao-dyn
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The categories of this paper are: solv-int
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The categories of this paper are: nlin
5
The categories of this paper are: gr-qc
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The categories of this paper are: supr-con
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The categories of this paper are: q-alg
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The categories of this paper are: mtrl-th
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The categories of this paper are: stat