On-the-fly interpretable machine learning for rapid discovery of two-dimensional ferromagnets with high Curie temperature

Summary

Machine learning (ML) techniques have accelerated the discovery of new materials. However, challenges such as data scarcity, representations without deep physical insights, and uninterpretable models restrict the widespread ML applications in complex systems. Herein, in order to obtain optimal two-dimensional (2D) ferromagnetic (FM) materials, we develop an adaptive ML framework to search the chemical space containing over 2 × 10⁵ candidates. Two key technique breakthroughs drive the progress. (1) An iterative feedback loop method to generate data on-the-fly is proposed. (2) An adaptive representation set, coupling with magnetism, crystal field theory, and atomic environments, is built. Consequently, ML models achieve a prediction accuracy of over 90% on the key FM properties. Furthermore, the "black box" of ML models is opened and general design principles are extracted. Our framework offers an easy way to facilitate efficient search of chemical space with regard to data scarcity and enables the model interpretability.