Broadening Hard‐Magnet Discovery Beyond Symmetry Constraints via Unified Effective Anisotropy

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 classification‐regression pipeline that screens structures without symmetry prefiltering. Applied to Materials Project entries, it recovers established hard magnets and uncovers rare‐earth‐free κ > 1 candidates in orthorhombic and monoclinic classes—beyond most previous symmetry‐restricted screens. Complementing this search, a fine‐tuned diffusion model proposes five DFT‐confirmed rare‐earth‐free candidates, four absent from the Materials Project: refinements of a known Pt hard‐magnet chemistry plus the noble‐metal‐free TaFe 3 , three of them phonon‐stable. These results show that a physically unified anisotropy descriptor can substantially broaden data‐driven hard‐magnet discovery using established machine‐learning and generative workflows.