Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Insights of cation ordering in double perovskites oxides from machine
  learning   This work investigates origins of cation ordering of double perovskites using
first-principles theory computations combined with machine learning (ML). We
have considered various possible oxidation states of A, A', B and B' from the
family of transition metal ions to construct datasets. A conventional framework
employing traditional ML classification algorithm such as Random Forest (RF)
combined with appropriate features including geometry-driven and key structural
modes leads to highly accurate prediction (~98%) of A-site cation ordering.
This study evaluates accuracy of ML models by entailing analyses of decision
paths, assignments of probabilistic confidence bound, and finally introducing a
direct non-Gaussian acyclic structural equation model to investigate causality.
A comparative study of utilization of first-principles driven features versus
those computed with lower fidelity is added to further elucidate the role of
structural modes for accurate prediction of cation ordering in double
perovskites.

36
The categories of this paper are: cmp-lg
13
The categories of this paper are: quant-ph
33
The categories of this paper are: plasm-ph
24
The categories of this paper are: dg-ga
7
The categories of this paper are: hep-th
25
The categories of this paper are: patt-sol
34
The categories of this paper are: chem-ph
3
The categories of this paper are: physics
1
The categories of this paper are: cs
21
The categories of this paper are: adap-org
2
The categories of this paper are: math
23
The categories of this paper are: solv-int
15
The categories of this paper are: nucl-ex
20
The categories of this paper are: acc-phys
6
The categories of this paper are: astro-ph
27
The categories of this paper are: comp-gas