Symmetry-electronic fingerprints reveal competing magnetic phases in two-dimensional materials
This work provides a physically interpretable machine-learning representation for predicting magnetic properties in 2D materials, addressing a key bottleneck in materials discovery for spintronics and quantum technologies.
The authors introduce symmetry-electronic fingerprints (SEF) to predict magnetic ground states, moments, and anisotropy in 2D materials, achieving accurate classification and regression while identifying competing magnetic phases via model uncertainty. First-principles calculations confirm that high-uncertainty regions correspond to near-degenerate ferromagnetic and antiferromagnetic phases with frustration and non-collinear ordering.
Two-dimensional magnets offer compelling platforms for spintronics and quantum technologies, yet predicting their magnetic ground states, moments, and anisotropy remains challenging. This limitation primarily arises because existing machine-learning representations encode chemical environments without capturing the symmetry or exchange physics that govern magnetism. In this work, we introduce the symmetry-electronic fingerprint (SEF), a physically interpretable representation that encodes crystallographic symmetry operations, Wyckoff-site geometry, together with site-resolved electronic structure. Combined with ensemble learning with random forests, the SEF accurately classifies magnetic ordering while regressing moments alongside anisotropy energies while simultaneously resolving the distinct regimes of itinerant Stoner ferromagnetism from localized superexchange. What sets the SEF-trained models apart is that regions of elevated model uncertainty are not a failure but a diagnostic, identifying materials where these mechanisms compete. First-principles calculations on Co- and Ni-based halides and oxides confirm that these regions correspond to genuine near-degenerate FM and AFM phases with magnetic frustration, suppressed anisotropy, and emergent non-collinear ordering. By encoding symmetry together with exchange physics directly into the representation unlike conventional descriptors, the SEF transforms model uncertainty into a compass pointing toward two-dimensional materials where small perturbations drive transitions between collinear, frustrated, or non-collinear magnetic phases.