Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Predicting materials properties without crystal structure: Deep
  representation learning from stoichiometry   Machine learning has the potential to accelerate materials discovery by
accurately predicting materials properties at a low computational cost.
However, the model inputs remain a key stumbling block. Current methods
typically use descriptors constructed from knowledge of either the full crystal
structure -- therefore only applicable to materials with already characterised
structures -- or structure-agnostic fixed-length representations
hand-engineered from the stoichiometry. We develop a machine learning approach
that takes only the stoichiometry as input and automatically learns appropriate
and systematically improvable descriptors from data. Our key insight is to
treat the stoichiometric formula as a dense weighted graph between elements.
Compared to the state of the art for structure-agnostic methods, our approach
achieves lower errors with less data.

26
The categories of this paper are: bayes-an
18
The categories of this paper are: econ
28
The categories of this paper are: alg-geom
20
The categories of this paper are: acc-phys
15
The categories of this paper are: nucl-ex
29
The categories of this paper are: funct-an
9
The categories of this paper are: nlin
34
The categories of this paper are: chem-ph
13
The categories of this paper are: quant-ph
31
The categories of this paper are: ao-sci
12
The categories of this paper are: nucl-th
30
The categories of this paper are: q-alg
0
The categories of this paper are: hep-ph
2
The categories of this paper are: math
7
The categories of this paper are: hep-th
14
The categories of this paper are: math-ph