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Preprint Aug 2026

High-throughput Discovery of Magnetic Rare Earth Transition Metal Alloys

We present an accelerated materials discovery framework that combines diffusion-based crystal structure generation with hierarchical screening to identify new rare-earth--transition-metal magnets simultaneously achieving high magnetization and thermodynamic stability. Using this workflow, we systematically explored over 3000 binary (R-T) and ternary (R-T-T$'$) compositions spanning R~$\in \{\text{Y, Sm}\}$, T~$\in \{\text{Fe, Co, Ni}\}$, and T$'\in \{\text{Ti, V, Cr, Mn, Cu, Zn}\}$, and filtered approximately 240{,}000 generated crystal structures through machine-learning interatomic potential prescreening and spin-polarized density functional theory validation. We identify 300+ low-energy magnetic candidates within 0.1~eV/atom above the convex hull at the DFT level, including 5 thermodynamically stable phases. The highest saturation magnetization reaches ${\sim}1.8$~T in Fe-rich binary and ternary phases (SmFe$_{12}$, YFe$_{12}$, YFe$_{18}$Ti and Sm$_2$Fe$_{16}$Mn). Symmetry analysis reveals that the majority of ternary candidates are subgroup derivatives of known binary prototypes through Wyckoff site splitting that accommodates T$'$ substitution. Site-resolved magnetic moment analysis further shows that Mn aligns ferromagnetically with the Fe sublattice with minimal magnetization loss, whereas Cr couples antiferromagnetically, providing systematic guidance for dopant selection. These findings demonstrate a generalizable strategy for targeted magnetic materials discovery and suggest that extending generative searches to larger unit cells ($>$20 atoms) with higher Fe fractions is a promising route toward stable phases with saturation magnetization exceeding 1.8~T.

S. Tao, Osman Goni Ridwan, Liqin Ke et al. · 0 citations
#machine learning Preprint Aug 2026

CrystalGRPO: Target-Aligned and Coverage-Preserving Reinforcement Learning for Flow-Based Crystal Structure Prediction

This work introduces CrystalGRPO, a CSP-aligned post-training framework that extends existing ODE-to-SDE policy constructions to the joint coordinate--lattice state and provides two operating modes: CrystalGRPO-Q, which prioritizes single-draw recovery, and CrystalGRPO-C, which combines full-trajectory reference regularization with a coverage-aware group advantage to preserve finite-budget target recovery.

Kaixiang Su, Hongfei Xue, Qiang Zhu · 0 citations

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