Broadening Hard‐Magnet Discovery Beyond Symmetry Constraints via Unified Effective Anisotropy
2026 · Advanced Science
저자
Hojae Kim, Hyeondeok Shin, Kyungju Nam, Seung Hyo Noh, Donghwi Kim, 정유성 (Yousung Jung)
연구주제
Machine Learning in Materials Science
NTIS 과학기술표준분류
가장 가까운 분류(재료 > 금속재료)가 화학·화공 범위 밖이다. 배정 근거: 지원 과제·키워드 근거가 없어 AI가 논문 제목·초록을 읽고 고른 분류.
AI 연구 키워드·방법
hard magnet magnetocrystalline anisotropy rare earth lean magnet
키워드 (OpenAlex)
magnetocrystalline anisotropy effective anisotropy constant crystal symmetry magnetization reversal barrier diffusion models
초록
ABSTRACT Machine‐learning‐driven discovery of rare‐earth‐lean hard magnets has advanced rapidly, yet existing pipelines have remained constrained either to a fixed composition family or to a single crystal‐system class, leaving most compositional and structural space unexplored. We address both limitations by introducing a unified effective anisotropy constant, K eff , defined consistently across all seven crystal systems, equal to the conventional K 1 in uniaxial cases and—as established here by symmetry‐based derivation and density functional theory calculations—designed to approximate the minimum magnetization‐reversal barrier that governs the hardness parameter κ in orthorhombic, monoclinic, and triclinic systems, with symmetry‐dependent accuracy quantified by angular DFT sampling. With this descriptor, we assemble a magnetocrystalline‐anisotropy database and train a two‐stage classi…
이 논문을 지원한 국가R&D 과제 (NTIS 성과)
NTIS 성과DB(2020–24)에서 DOI 매칭 없음
인접 그래프
노드 hover = 이웃 강조 · 1회 클릭 = 고정 · 고정된 노드 다시 클릭 = 페이지 이동
이 문서와 연결된 페이지
교수 1
연구주제 1
Machine Learning in Materials Science