Machine learning-accelerated discovery of novel 2D ferromagnetic materials with strong magnetization
Chao Xin, Yaohui Yin, Bingqian Song, Zhen Fan, Yongli Song, Feng Pan
Abstract
Two-dimensional ferromagnetic (2DFM) semiconductors (metals, half-metals, and so on) are important materials for next-generation nano-electronic and nano-spintronic devices. However, these kinds of materials remain scarce, and “trial and error” experiments and calculations are time-consuming and expensive. In the present work, to obtain optimal 2DFM materials with strong magnetization, we established a machine learning (ML) framework to search the 2D material space containing over 2417 samples, and identified 615 compounds whose magnetic orders was then determined via high-through-put first-principles calculations. Using ML algorithms, we trained two classification models and a regression model. The interpretability of the regression model was evaluated through Shapley additive explanations (SHAP) analysis. Unexpectedly, we found that Cr2NF2 is a potential antiferromagnetic ferroelectric 2D multiferroic material. More importantly, 60 novel 2DFM candidates were predicted, and among them, 13 candidates have magnetic moments of > 7 μB. Os2Cl8, Fe3GeSe2, and Mn4N3S2 were predicted to be novel 2DFM semiconductors, metals, and half-metals, respectively. Our ML approach can accelerate the prediction of 2DFM materials with strong magnetization and reduce the computation time by more than one order of magnitude.