Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Screening of potential double perovskite materials for photovoltaic
  applications using agglomerative hierarchical clustering   Data-driven approaches to solve problems in materials science have gained
immense popularity in recent times due to their ability to predict unknown
material properties and uncover relationships between structure and property.
Machine learning algorithms like GBRT, random forest and neural networks have
had tremendous success in predicting target properties of materials and design
of structures for various applications. However, a major drawback for achieving
results within the required accuracy using these algorithms has been the need
for large datasets which can be challenging for problems when data is not
sufficiently available for training the models. In this work, we propose the
use of a hierarchical clustering algorithm which can work considerably better
on materials science problems with small dataset constraints. We apply the
algorithm to screen out promising double perovskite materials as candidates for
solar cells.

27
The categories of this paper are: comp-gas
1
The categories of this paper are: cs
13
The categories of this paper are: quant-ph
3
The categories of this paper are: physics
28
The categories of this paper are: alg-geom
17
The categories of this paper are: q-fin
37
The categories of this paper are: supr-con
30
The categories of this paper are: q-alg
21
The categories of this paper are: adap-org
10
The categories of this paper are: q-bio
20
The categories of this paper are: acc-phys
34
The categories of this paper are: chem-ph
15
The categories of this paper are: nucl-ex
7
The categories of this paper are: hep-th
25
The categories of this paper are: patt-sol
26
The categories of this paper are: bayes-an