Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Machine Learning for compositional disorder: A Comparison Between
  Different Descriptors and Machine Learning Frameworks   Compositional disorder is common in crystal compounds. In these compounds,
some atoms are randomly distributed at some crystallographic sites. For such
compounds, randomness forms many non-identical independent structures. Thus,
calculating the energy of all structures using ordinary quantum ab initio
methods can be significantly time-consuming. Machine learning can be a reliable
alternative to ab initio methods. We calculate the energy of these compounds
with an accuracy close to that of density functional theory calculations in a
considerably shorter time using machine learning. In this study, we use kernel
ridge regression and neural network to predict energy. In the KRR, we employ
sine matrix, Ewald sum matrix, SOAP, ACSF, and MBTR. To implement the neural
network, we use two important classes of application of the neural network in
material science, including high-dimensional neural network and convolutional
neural network based on crystal graph representation. We show that kernel ridge
regression using MBTR and neural network using ACSF can provide better accuracy
than other methods.

13
The categories of this paper are: quant-ph
8
The categories of this paper are: hep-ex
23
The categories of this paper are: solv-int
2
The categories of this paper are: math
24
The categories of this paper are: dg-ga
1
The categories of this paper are: cs
29
The categories of this paper are: funct-an
19
The categories of this paper are: eess
30
The categories of this paper are: q-alg
11
The categories of this paper are: hep-lat
34
The categories of this paper are: chem-ph
35
The categories of this paper are: mtrl-th
5
The categories of this paper are: gr-qc
26
The categories of this paper are: bayes-an
17
The categories of this paper are: q-fin
18
The categories of this paper are: econ