Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Using neural network potential to study point defect properties in
  multiple charge states of GaN with nitrogen vacancy   Investigation of charged defects is necessary to understand the properties of
semiconductors. While density functional theory calculations can accurately
describe the relevant physical quantities, these calculations increase the
computational loads substantially, which often limits the application of this
method to large-scale systems. In this study, we propose a new scheme of neural
network potential (NNP) to analyze the point defect behavior in multiple charge
states. The proposed scheme necessitates only minimal modifications to the
conventional scheme. We demonstrated the prediction performance of the proposed
NNP using wurzite-GaN with a nitrogen vacancy with charge states of 0, 1+, 2+,
and 3+. The proposed scheme accurately trained the total energies and atomic
forces for all the charge states. Furthermore, it fairly reproduced the phonon
band structures and thermodynamics properties of the defective structures.
Based on the results of this study, we expect that the proposed scheme can
enable us to study more complicated defective systems and lead to breakthroughs
in novel semiconductor applications.

34
The categories of this paper are: chem-ph
24
The categories of this paper are: dg-ga
14
The categories of this paper are: math-ph
18
The categories of this paper are: econ
37
The categories of this paper are: supr-con
11
The categories of this paper are: hep-lat
23
The categories of this paper are: solv-int
13
The categories of this paper are: quant-ph
7
The categories of this paper are: hep-th
17
The categories of this paper are: q-fin
3
The categories of this paper are: physics
19
The categories of this paper are: eess
33
The categories of this paper are: plasm-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