范德瓦尔斯力
磁铁
自旋电子学
可靠性(半导体)
密度泛函理论
计算机科学
纳米技术
统计物理学
物理
材料科学
凝聚态物理
铁磁性
量子力学
分子
功率(物理)
作者
Haiyang Song,Yinghe Zhao,Emma Turner,Yuechao Wu,Yuan Li,Menghao Wu,Guang Feng,Huiqiao Li,Tianyou Zhai
出处
期刊:InfoMat
[Wiley]
日期:2023-01-03
卷期号:5 (4)
摘要
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. image
科研通智能强力驱动
Strongly Powered by AbleSci AI