Computational prediction of new magnetic materials - Laboratoire des Sciences des Procédés et des Matériaux Accéder directement au contenu
Article Dans Une Revue Journal of Chemical Physics Année : 2022

Computational prediction of new magnetic materials

Guang-Rui Qian
  • Fonction : Auteur
Hao Li
  • Fonction : Auteur

Résumé

The discovery of new magnetic materials is a big challenge in the field of modern materials science. We report the development of a new extension of the evolutionary algorithm USPEX, enabling the search for half-metals (materials that are metallic only in one spin channel) and hard magnetic materials. First, we enabled the simultaneous optimization of stoichiometries, crystal structures, and magnetic structures of stable phases. Second, we developed a new fitness function for half-metallic materials that can be used for predicting half-metals through an evolutionary algorithm. We used this extended technique to predict new, potentially hard magnets and rediscover known half-metals. In total, we report five promising hard magnets with high energy product (|BH|max), anisotropy field ( Ha), and magnetic hardness (κ) and a few half-metal phases in the Cr-O system. A comparison of our predictions with experimental results, including the synthesis of a newly predicted antiferromagnetic material (WMnB2), shows the robustness of our technique.
Fichier principal
Vignette du fichier
1296836_0 (HAL).pdf (4.2 Mo) Télécharger le fichier
Origine : Fichiers produits par l'(les) auteur(s)

Dates et versions

hal-03791608 , version 1 (29-09-2022)

Identifiants

Citer

Saeed Rahmanian Koshkaki, Zahed Allahyari, Artem Oganov, Vladimir Solozhenko, Ilya Polovov, et al.. Computational prediction of new magnetic materials. Journal of Chemical Physics, 2022, 157 (12), pp.124704. ⟨10.1063/5.0113745⟩. ⟨hal-03791608⟩
33 Consultations
125 Téléchargements

Altmetric

Partager

Gmail Facebook X LinkedIn More