Advancements in High‐Throughput Screening and Machine Learning Design for 2D Ferromagnetism: A Comprehensive Review
Chao Xin, Bingqian Song, Guangyong Jin, Yongli Song, Feng Pan
Abstract
Abstract 2D intrinsic magnetic materials possess unique physical properties distinct from bulk materials, providing an ideal research platform for the development of low‐dimensional spintronics. The traditional approach to developing new materials involves a “trial‐and‐error” method, which is inherently flawed due to long development cycles and high costs. In recent years, with the rapid improvement in computational power, the high throughput (HTP) first‐principles calculation based on density functional theory (DFT) and machine learning (ML) method have provided a highly effective means for the design of novel intrinsic ferromagnetic materials and the study of their magnetic properties. This article reviews the recent research progress in 2D ferromagnetic materials, with particular emphasis on the significant role played by HTP first‐principles calculations and ML in the exploration and fabrication of two‐dimension ferromagnetic (2DFM) materials. Finally, the future development and challenges of 2DFM materials are discussed.