Symmetry-electronic fingerprints reveal competing magnetic phases in two-dimensional materials

2026-06-11

AI Summary

Predicting the ground state, magnetic moments, and magnetic anisotropy of two-dimensional magnetic materials remains challenging, primarily because existing machine learning approaches struggle to effectively encode the symmetry operations and exchange interactions that govern magnetism. This work proposes a Symmetry–Electronic Fingerprint (SEF) that integrates crystallographic symmetry operations, Wyckoff site geometry, and local electronic structure, combined with a random forest ensemble to simultaneously classify magnetic order, regress magnetic moments and anisotropy energies, and distinguish between itinerant Stoner ferromagnetism and localized superexchange mechanisms. By directly embedding physical priors into the feature representation, the model’s predictive uncertainty serves as an effective diagnostic for identifying regions of competing magnetic phases. Applied to Co- and Ni-based halides and oxides, the method successfully uncovers nearly degenerate ferromagnetic/antiferromagnetic states, magnetic frustration, weak anisotropy, and noncollinear magnetic orders, demonstrating SEF’s strong potential for discovering tunable magnetic phase-transition materials.

Abstract

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.

Problem

• two-dimensional magnets
• magnetic ground states
• symmetry
• exchange physics
• machine learning representations

Innovation

• symmetry-electronic fingerprint
• magnetic phase competition
• machine learning representation
• exchange physics
• magnetic frustration