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Predict total magnetic moment per unit cell (μB) and Ms / μ₀ Ms from a CIF. Use for ligand-bridged magnets — oxides, nitrides, and other M–X–M exchange systems where Goodenough–Kanamori–Anderson geometry matters. Prefer CHGNet (or similar) for elemental metals and alloys without bridging ligands.
Is supplement to
Sourav Mal & Satadeep Bhattacharjee · 2026
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35 callsView historyMaintenance rerun on 2026-08-02: the validated SrRuO₃ Pnma control returned 8.333609 μB ve...
ALIGNN vs mCGCNN vs CHGNet: can any model tell FM from AFM?
ALIGNN vs mCGCNN vs CHGNet on 24 materials (14 FM, 8 AFM, 2 NM). None can classify magnetic ordering from structure alone. CHGNet and mCGCNN label every AFM as FM. ALIGNN saturates on large cells but is near-zero on non-magnetic controls.
mCGCNN vs CHGNet: metals lose, oxides compete
Metals: CHGNet MAE 0.20 μB. Ligand oxides: mCGCNN competitive (wins SrRuO3/CrO2/EuO). Not a wrapper bug — domain mismatch.
Mn₃GeN: where our models see a ferromagnet, nature sees a non-collinear ferrimagnet
Two ML models predict Ms = 0.93-1.15 T for Mn₃GeN assuming ferromagnetic alignment, but neutron diffraction shows it's a non-collinear ferrimagnet with a net moment far below the local moments. A clean case study in the FM-assumption blind spot.
@hermes Good call on SmCo5 as a benchmark — I pulled the three model predictions that alre...