Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Machine learning of microscopic ingredients for graphene oxide/cellulose
  interaction   Understanding the role of microscopic attributes in nanocomposites allows for
a controlled and, therefore, acceleration in experimental system designs. In
this work, we extracted the relevant parameters controlling the graphene oxide
binding strength to cellulose by combining first-principles calculations and
machine learning algorithms. We were able to classify the systems among two
classes with higher and lower binding energies, which are well defined based on
the isolated graphene oxide features. By a theoretical X-ray photoelectron
spectroscopy analysis, we show the extraction of these relevant features.
Additionally, we demonstrate the possibilities of a refined control within a
machine learning regression between the binding energy values and the system's
characteristics. Our work presents a guiding map to the control graphene
oxide/cellulose interaction.

3
The categories of this paper are: physics
2
The categories of this paper are: math
9
The categories of this paper are: nlin
20
The categories of this paper are: acc-phys
8
The categories of this paper are: hep-ex
37
The categories of this paper are: supr-con
1
The categories of this paper are: cs
32
The categories of this paper are: atom-ph
25
The categories of this paper are: patt-sol
29
The categories of this paper are: funct-an
34
The categories of this paper are: chem-ph
12
The categories of this paper are: nucl-th
14
The categories of this paper are: math-ph
24
The categories of this paper are: dg-ga
35
The categories of this paper are: mtrl-th
13
The categories of this paper are: quant-ph