Machine learning study on magnetic structure of rare earth based magnetic materials
Rattachement africain : cn. Niveau de preuve : code pays fourni par la source.
Le résumé fourni par la source
• 11 machine learning methods were trained to predict the material magnetic structure. • Rare earth content seriously affects the generation of nonlinear magnetic structure. • Neural Network performs best in skyrmion prediction, with accuracy 0.93 and reliability 97 %. • Skyrmion observed in SrFeScMgO and LaBaMnO effectively verified model reliability. Machine learning is playing an increasingly important role in discovery and design of new materials. In this work, 11 machine learning algorithms were trained to predict the material magnetic structure. Material composition and crystal structure are used to classify the dataset, and the relationship between multi-feature variables is constructed in a small sample space. The prediction accuracy of all models is above 0.73. Compared with non-decision tree models, optimized decision tree algorithms such as Gradient Boosting have greater advantages in binary classification. Neural Network has the best performance in predicting skyrmion structure, with accuracy and reliability of 0.93 and 97 %, respectively. Rare earth elements have a great influence on the material magnetic structure, and their proportion is negatively correlated with the generation of nonlinear or skyrmion structures. The material is more prone to nonlinear magnetic structure when the space group belongs to the cubic and the hexagonal crystal systems. Based on the Neural Network, the magnetic structures of several rare earth oxides are predicted. The skyrmion in SrR x Fe 12- x - y Mg y O 19 and La x Ba 1- x MnO 3 was observed by Neutron Powder Diffraction and magnetic force microscope, which effectively verified the model accuracy. This work provides new perspectives for machine learning in the discovery of nonlinear magnetic structures and rapid design of material compositions.
Ce résumé expose les affirmations des auteurs. BNTIC ne l’interprète pas comme une validation indépendante des résultats.
Le contrôle bibliographique ouvert
DOI retrouvé dans Crossref DOI retrouvé ; titre concordant.
- Titre Crossref
- Machine learning study on magnetic structure of rare earth based magnetic materials
- Date Crossref
- 01/03/2025
- Éditeur
- Elsevier BV
- Type
- journal-article
Ce recoupement confirme des métadonnées liées au DOI. Il ne confirme ni la méthode ni les conclusions de l’étude, et il ne compte pas comme une seconde source scientifique indépendante.
Où se fait cette recherche
-
Beijing Technology and Business University Department of Physics pays non établi dans la noticeUniversité ou école supérieure
-
Nanjing University of Aeronautics and Astronautics Department of Applied Physics pays non établi dans la noticeUniversité ou école supérieure
-
Beihang University pays non établi dans la noticeUniversité ou école supérieure
-
University of Science and Technology Beijing pays non établi dans la noticeUniversité ou école supérieure
-
China Spallation Neutron Source pays non établi dans la noticeStructure de recherche
-
Institute of Physics pays non établi dans la noticeStructure de recherche
-
Ningbo Institute of Industrial Technology pays non établi dans la noticeStructure de recherche
-
School of Integrated Circuit Science and Engineering pays non établi dans la noticeUniversité ou école supérieure
-
School of Materials Science and Engineering pays non établi dans la noticeUniversité ou école supérieure
-
Dongguan 523803 China Spallation Neutron Source Science Center (SNSSC) pays non établi dans la noticeOrganisation à but non lucratif
-
State Key Laboratory of Magnetism pays non établi dans la noticeStructure de recherche
-
Ningbo Institute of Materials Technology & Engineering pays non établi dans la noticeStructure de recherche
Department of Physics — Beijing Technology and Business University, Department of Applied Physics — Nanjing University of Aeronautics and Astronautics et Beihang University, avec 9 autres affiliations.
Une affiliation ne permet pas de déduire la nationalité d’un auteur.