Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Crystal Structure Search with Random Relaxations Using Graph Networks   Materials design enables technologies critical to humanity, including
combating climate change with solar cells and batteries. Many properties of a
material are determined by its atomic crystal structure. However, prediction of
the atomic crystal structure for a given material's chemical formula is a
long-standing grand challenge that remains a barrier in materials design. We
investigate a data-driven approach to accelerating ab initio random structure
search (AIRSS), a state-of-the-art method for crystal structure search. We
build a novel dataset of random structure relaxations of Li-Si battery anode
materials using high-throughput density functional theory calculations. We
train graph neural networks to simulate relaxations of random structures. Our
model is able to find an experimentally verified structure of Li15Si4 it was
not trained on, and has potential for orders of magnitude speedup over AIRSS
when searching large unit cells and searching over multiple chemical
stoichiometries. Surprisingly, we find that data augmentation of adding
Gaussian noise improves both the accuracy and out of domain generalization of
our models.

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The categories of this paper are: hep-ex
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The categories of this paper are: math-ph
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The categories of this paper are: nucl-th
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The categories of this paper are: q-fin
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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: stat
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The categories of this paper are: adap-org
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The categories of this paper are: eess
37
The categories of this paper are: supr-con
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The categories of this paper are: q-bio
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The categories of this paper are: solv-int
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The categories of this paper are: alg-geom
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The categories of this paper are: cmp-lg
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The categories of this paper are: patt-sol
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The categories of this paper are: q-alg