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Classification of magnetic order from electronic structure by using machine learning

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Title
Classification of magnetic order from electronic structure by using machine learning
Author(s)
Jang, Yerin; Choong H. Kim; Go, Ara
Publication Date
2023-08
Journal
Scientific Reports, v.13, no.1
Publisher
Nature Research
Abstract
Identifying the magnetic state of materials is of great interest in a wide range of applications, but direct identification is not always straightforward due to limitations in neutron scattering experiments. In this work, we present a machine-learning approach using decision-tree algorithms to identify magnetism from the spin-integrated excitation spectrum, such as the density of states. The dataset was generated by Hartree–Fock mean-field calculations of candidate antiferromagnetic orders on a Wannier Hamiltonian, extracted from first-principle calculations targeting BaOsO 3 . Our machine learning model was trained using various types of spectral data, including local density of states, momentum-resolved density of states at high-symmetry points, and the lowest excitation energies from the Fermi level. Although the density of states shows good performance for machine learning, the broadening method had a significant impact on the model’s performance. We improved the model’s performance by designing the excitation energy as a feature for machine learning, resulting in excellent classification of antiferromagnetic order, even for test samples generated by different methods from the training samples used for machine learning. © 2023, The Author(s).
URI
https://pr.ibs.re.kr/handle/8788114/13916
DOI
10.1038/s41598-023-38863-7
ISSN
2045-2322
Appears in Collections:
Center for Correlated Electron Systems(강상관계 물질 연구단) > 1. Journal Papers (저널논문)
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