Capturing 2D van der Waals magnets with high probability for experimental demonstration from materials science literature
Haiyang Song,
Yinghe Zhao,
Eleanor Turner,
Yu Wu,
Yuan Li,
Menghao Wu,
Guang Feng,
Huiqiao Li,
Tianyou Zhai
Affiliations
Haiyang Song
State Key Laboratory of Materials Processing and Die & Mould Technology School of Materials Science and Engineering, Huazhong University of Science and Technology Wuhan the People's Republic of China
Yinghe Zhao
State Key Laboratory of Materials Processing and Die & Mould Technology School of Materials Science and Engineering, Huazhong University of Science and Technology Wuhan the People's Republic of China
Eleanor Turner
Department of Chemical & Biological Engineering Monash University, Clayton Campus Clayton Victoria Australia
Yu Wu
State Key Laboratory of Materials Processing and Die & Mould Technology School of Materials Science and Engineering, Huazhong University of Science and Technology Wuhan the People's Republic of China
Yuan Li
State Key Laboratory of Materials Processing and Die & Mould Technology School of Materials Science and Engineering, Huazhong University of Science and Technology Wuhan the People's Republic of China
Menghao Wu
School of Physics Huazhong University of Science and Technology Wuhan the People's Republic of China
Guang Feng
State Key Laboratory of Coal Combustion School of Energy and Power Engineering, Huazhong University of Science and Technology Wuhan the People's Republic of China
Huiqiao Li
State Key Laboratory of Materials Processing and Die & Mould Technology School of Materials Science and Engineering, Huazhong University of Science and Technology Wuhan the People's Republic of China
Tianyou Zhai
State Key Laboratory of Materials Processing and Die & Mould Technology School of Materials Science and Engineering, Huazhong University of Science and Technology Wuhan the People's Republic of China
Abstract 2D van der Waals (vdW) magnets have opened intriguing prospects for next‐generation spintronic nanodevices. Machine learning techniques and density functional theory calculations enable the discovery of 2D vdW magnets to be accelerated; however, current computational frameworks based on these state‐of‐the‐art approaches cannot offer probability analysis on whether a 2D vdW magnet can be experimentally demonstrated. Herein, a new framework can be established to overcome this challenge. Via the framework, 2D vdW magnets with high probability for experimental demonstration are captured from materials science literature. The key to the successful establishment is the introduction of the theory of mutual information. Historical validation of predictions substantiates the high reliability of the framework. For example, half of the 30 2D vdW magnets discovered in the literature published prior to 2017 have been experimentally demonstrated in the subsequent years. This framework has the potential to become a revolutionary force for progressing experimental discovery of 2D vdW magnets.
