Machine learning-accelerated discovery of iron cobalt phosphides as rare-earth-free magnets

Liao, Timothy; Xia, Weiyi; Sakurai, Masahiro; Zhang, Chao; Sun, Huaijun; Wang, Renhai; Ho, Kai-Ming; Wang, Cai-Zhuang; Chelikowsky, James R.

doi:10.1103/physrevmaterials.8.104404

Title: Machine learning-accelerated discovery of iron cobalt phosphides as rare-earth-free magnets

Journal Article · 11 October 2024 · Physical Review Materials

DOI: https://doi.org/10.1103/physrevmaterials.8.104404 · OSTI ID:2477809

^[1]; Xia, Weiyi ^[2]; Sakurai, Masahiro ^[3];

^[4];

^[5];

^[6]; Ho, Kai-Ming ^[2]; Wang, Cai-Zhuang ^[2]; Chelikowsky, James R. ^[7]

Univ. of Texas, Austin, TX (United States); Ames Laboratory (AMES), Ames, IA (United States)
Iowa State Univ., Ames, IA (United States); Ames Laboratory (AMES), Ames, IA (United States)
Univ. of Texas, Austin, TX (United States); Univ. of Tokyo, Kashiwa (Japan)
Yantai University (China)
Iowa State Univ., Ames, IA (United States); Zhejiang Agriculture and Forestry University, Zhuji (China)
Guangdong University of Technology, Guangzhou (China)
Univ. of Texas, Austin, TX (United States)

Here, the discovery of rare-earth-free permanent magnets has been a goal of scientists for decades. The absence of rare-earth elements will alleviate a pressing concern about the availability of rare-earth elements used in permanent magnets. These magnets are crucial for applications such as wind turbines, electric cars, and memory devices. Rare-earth magnets are special owing to a large magnetic anisotropy energy (K₁). In contrast, iron cobalt phosphides hold promise since doping P into cubic FeCo can induce anisotropy, leading to a large coercivity, without introducing rare-earth elements. We present a comprehensive search over the Fe-Co-P ternary space for magnets, utilizing recently developed adaptive machine learning feedback to efficiently screen over 850 000 structures. We focus on machine learning acceleration as a paradigm for materials design. Further adaptive genetic algorithm searches and first-principles calculations aid in the identification of 16 new structures below the known convex hull. Five of them possess high magnetic polarization (J_s > 1 T). The structures with desirable magnetic properties center on (Fe,Co)_2⁢P. This supports conventional wisdom, which focuses on the mixture of the two known end compounds: Fe₂⁢P and Co₂⁢P. Our work provides guidance for synthesis. We find Fe₇⁢CoP₄ shows the most promise (J_s = 1.03T and K₁ = 0.83MJ/m³).

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Research Organization:: Ames Laboratory (AMES), Ames, IA (United States)

Sponsoring Organization:: USDOE Office of Science (SC), Basic Energy Sciences (BES). Materials Sciences & Engineering Division (MSE); Welch Foundation

Grant/Contract Number:: AC02-07CH11358

OSTI ID:: 2477809

Report Number(s):: AL-J--646

Journal Information:: Physical Review Materials, Journal Name: Physical Review Materials Journal Issue: 10 Vol. 8; ISSN 2475-9953

Publisher:: American Physical Society (APS)Copyright Statement

Country of Publication:: United States

Language:: English

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