Work

AI-accelerated discovery of altermagnetic metal-organic frameworks via interpretable machine learning

Year: 2026

Type: article

Abstract: Altermagnetism has emerged as a third class of collinear magnetism, yet its exploration remains largely confined to inorganic materials. Here, we establish an AI-accelerated pipeline for discovering altermagnetic metal-organic frameworks (MOFs). From symmetry-guided DFT screening of 350 candidates, we identified six altermagnetic MOFs. A two-stage machine learning strategy was then employed: First, an XGBoost classifier pre-screened 65,578 candidates, narrowing the pool to 145 high-potential linkers, from which DFT confirmed six new altermagnets. Building on this enriched dataset, we trained a KNN classifier (F1 = 0.8793) and a CatBoost regressor ( R ² = 0.7732) for predicting altermagnetism and spin-splitting magnitudes, respectively. SHAP analysis translated predictions into design principles: conjugated backbones favor altermagnetism, while moderate push-pull groups enhance spin splitting. Guided by these principles, we designed three additional MOF candidates, all confirmed by DFT. In total, this workflow identified 15 altermagnetic MOFs, establishing a generalizable framework for accelerating quantum materials discovery.

Source: npj Computational Materials

DOI: 10.1038/s41524-026-02323-3

Authors: Yue Ran, Jingyi Duan, Yikai Chen, Wei Jiang, Yugui Yao

Institution: Beijing Institute of Technology

Language: English

Topic: Machine Learning in Materials Science

Subfield: Materials Chemistry

Field: Materials Science

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $3,490

Funders: National Natural Science Foundation of China, Beijing Institute of Technology, Beijing Institute of Technology Research Fund Program for Young Scholars