BROWSE

Related Scientist

CCES's photo.

CCES
강상관계 물질 연구단
more info

ITEM VIEW & DOWNLOAD

Classification of magnetic order from electronic structure by using machine learning

DC Field Value Language
dc.contributor.authorJang, Yerin-
dc.contributor.authorChoong H. Kim-
dc.contributor.authorGo, Ara-
dc.date.accessioned2023-09-12T22:03:28Z-
dc.date.available2023-09-12T22:03:28Z-
dc.date.created2023-08-16-
dc.date.issued2023-08-
dc.identifier.issn2045-2322-
dc.identifier.urihttps://pr.ibs.re.kr/handle/8788114/13916-
dc.description.abstractIdentifying 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).-
dc.language영어-
dc.publisherNature Research-
dc.titleClassification of magnetic order from electronic structure by using machine learning-
dc.typeArticle-
dc.type.rimsART-
dc.identifier.scopusid2-s2.0-85166072275-
dc.identifier.rimsid81448-
dc.contributor.affiliatedAuthorChoong H. Kim-
dc.identifier.doi10.1038/s41598-023-38863-7-
dc.identifier.bibliographicCitationScientific Reports, v.13, no.1-
dc.relation.isPartOfScientific Reports-
dc.citation.titleScientific Reports-
dc.citation.volume13-
dc.citation.number1-
dc.description.journalClass1-
dc.description.journalClass1-
dc.description.isOpenAccessN-
dc.description.journalRegisteredClassscie-
dc.description.journalRegisteredClassscopus-
Appears in Collections:
Center for Correlated Electron Systems(강상관계 물질 연구단) > 1. Journal Papers (저널논문)
Files in This Item:
There are no files associated with this item.

qrcode

  • facebook

    twitter

  • Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.
해당 아이템을 이메일로 공유하기 원하시면 인증을 거치시기 바랍니다.

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.

Browse