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

Sakurai, Masahiro; Wang, Renhai; Liao, Timothy; Zhang, Chao; Sun, Huaijun; Sun, Yang; Wang, Haidi; Zhao, Xin; Wang, Song-You; Balamurugan, B.; Xu, Xiaoshan; Sellmyer, David J.; Antropov, Vladimir; Zhang, Jianhua; Wang, Cai Zhuang; Ho, Kai Ming; Chelikowsky, James R.

doi:10.1103/physrevmaterials.4.114408

Cited by 14 publications

(8 citation statements)

References 65 publications

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“…12 and 24), we are not aware of many databases that include a range of magnetic properties of materials (T c , saturation magnetization, magnetic anisotropy). Extensive databases of magnetic properties constructed from DFT calculations, 25,26 or scavenged from the published literature using Natural Language Processing (NLP) techniques have started coming up recently. 24 Such efforts will definitely proliferate in the coming years facilitating application of ML to the study of magnetic properties.…”

Section: Introductionmentioning

confidence: 99%

Machine learning assisted hierarchical filtering: a strategy for designing magnets with large moment and anisotropy energy

Dutta

Sen

2022

J. Mater. Chem. C

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Section: Introductionmentioning

confidence: 99%

Machine learning assisted hierarchical filtering: a strategy for designing magnets with large moment and anisotropy energy

Dutta

Sen

2022

J. Mater. Chem. C

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“…The 1G-CGCNN model is used to perform the screening of the hypothetical structures as described in the main text. Then the 2G-CGCNN model is trained based on the DFT formation energies for the structures from 1G screening and from those Fe-Co–based structures from our magnetic materials database ( 30 ) as discussed in the main text. The dataset is divided into a training set (80%), validation set (10%), and test set (10%), respectively.…”

Section: Methodsmentioning

confidence: 99%

“…We also train a CGCNN ML model specifically for predicting Fe-Co–based ternary compounds using DFT formation energies of the 400 Fe-Co-B structures from the 1G CGCNN model and those of 3,469 Fe-Co-X (X = C, N, Si, and S) ternary structures from our magnetic materials database ( 30 ). We refer to this CGCNN model as the second-generation (2G) CGCNN model.…”

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confidence: 99%

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Accelerating the discovery of novel magnetic materials using machine learning–guided adaptive feedback

Xia

Sakurai

Balamurugan

et al. 2022

Proc. Natl. Acad. Sci. U.S.A.

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Magnetic materials are essential for energy generation and information devices, and they play an important role in advanced technologies and green energy economies. Currently, the most widely used magnets contain rare earth (RE) elements. An outstanding challenge of notable scientific interest is the discovery and synthesis of novel magnetic materials without RE elements that meet the performance and cost goals for advanced electromagnetic devices. Here, we report our discovery and synthesis of an RE-free magnetic compound, Fe 3 CoB 2 , through an efficient feedback framework by integrating machine learning (ML), an adaptive genetic algorithm, first-principles calculations, and experimental synthesis. Magnetic measurements show that Fe 3 CoB 2 exhibits a high magnetic anisotropy ( K 1 = 1.2 MJ/m 3 ) and saturation magnetic polarization ( J s = 1.39 T), which is suitable for RE-free permanent-magnet applications. Our ML-guided approach presents a promising paradigm for efficient materials design and discovery and can also be applied to the search for other functional materials.

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“…Nieves et al have been developing a database for rare-earth free/lean permanent magnets [ 91 ]. Independently, Sakurai et al developed a database for rare-earth-free magnetic materials [ 92 ].…”

Section: Materials Informaticsmentioning

confidence: 99%

Understanding and optimization of hard magnetic compounds from first principles

Miyake

Harashima

Fukazawa

et al. 2021

Science and Technology of Advanced Materials

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First-principles calculation based on density functional theory is a powerful tool for understanding and designing magnetic materials. It enables us to quantitatively describe magnetic properties and structural stability, although further methodological developments for the treatment of strongly-correlated 4f electrons and finite-temperature magnetism are needed. Here, we review recent developments of computational schemes for rare-earth magnet compounds, and summarize our theoretical studies on Nd 2 Fe 14 B and R Fe 12 -type compounds. Effects of chemical substitution and interstitial dopants are clarified. We also discuss how data-driven approaches are used for studying multinary systems. Chemical composition can be optimized with fewer trials by the Bayesian optimization. We also present a data-assimilation method for predicting finitetemperature magnetization in wide composition space by integrating computational and experimental data.

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

Cited by 14 publications

References 65 publications

Machine learning assisted hierarchical filtering: a strategy for designing magnets with large moment and anisotropy energy

Machine learning assisted hierarchical filtering: a strategy for designing magnets with large moment and anisotropy energy

Accelerating the discovery of novel magnetic materials using machine learning–guided adaptive feedback

Understanding and optimization of hard magnetic compounds from first principles

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