Discovering rare-earth-free magnetic materials through the development of a database

Authors

Masahiro Sakurai, The University of Texas at Austin
Renhai Wang, Iowa State University, University of Science and Technology of China
Timothy Liao, The University of Texas at Austin
Chao Zhang, Iowa State University
Huaijun Sun, Iowa State University
Yang Sun, Iowa State University
Haidi Wang, Iowa State University, Hefei University of Technology
Xin Zhao, Iowa State University
Songyou Wang, Fudan University, U.S. Department of Energy, Ames
Balamurugan Balasubramanian, University of Nebraska
Xiaoshan S. Xu, University of Nebraska-LincolnFollow
David J. Sellmyer, NCMNFollow
Vladimir Antropov, U.S. Department of Energy, Ames
Jianhua Zhang, Iowa State University
Cai-Zhuang Wang, Iowa State University, U.S. Department of Energy, Ames
Kai-Ming Ho, Iowa State University, U.S. Department of Energy, Ames
James R. Chelikowsky, The University of Texas at Austin

Document Type

Article

Date of this Version

11-11-2020

Citation

MASAHIRO SAKURAI et al. PHYSICAL REVIEW MATERIALS 4, 114408 (2020). DOI: 10.1103/PhysRevMaterials.4.114408

Comments

Used by permission.

Abstract

We develop an open-access database that provides a large array of datasets specialized for magnetic compounds as well as magnetic clusters. Our focus is on rare-earth-free magnets. Available datasets include (i) crystallography, (ii) thermodynamic properties, such as the formation energy, and (iii) magnetic properties that are essential for magnetic-material design. Our database features a large number of stable and metastable structures discovered through our adaptive genetic algorithm (AGA) searches. Many of these AGA structures have better magnetic properties when compared to those of the existing rare-earth-free magnets and the theoretical structures in other databases. Our database places particular emphasis on site-specific magnetic data, which are obtained by high-throughput first-principles calculations. Such site-resolved data are indispensable for machine-learning modeling. We illustrate how our data-intensive methods promote efficiency of the experimental discovery of new magnetic materials. Our database provides massive datasets that will facilitate an efficient computational screening, machine-learning-assisted design, and the experimental fabrication of new promising magnets.