Wang Pengju, Xing Jianpei, Jiang Xue, Zhao Jijun
Key Laboratory of Materials Modification by Laser, Ion and Electron Beams (Dalian University of Technology), Ministry of Education, Dalian 116024, China.
ACS Appl Mater Interfaces. 2022 Jul 27;14(29):33726-33733. doi: 10.1021/acsami.2c08991. Epub 2022 Jul 13.
Two-dimensional (2D) metal-organic framework (MOF) materials with large perpendicular magnetic anisotropy energy (MAE) are important candidates for high-density magnetic storage. The MAE-targeted high-throughput screening of 2D MOFs is currently limited by the time-consuming electronic structure calculations. In this study, a machine learning model, namely, transition-metal interlink neural network (TMINN) based on a database with 1440 2D MOF materials is developed to quickly and accurately predict MAE. The well-trained TMINN model for MAE successfully captures the general correlation between the geometrical configurations and the MAEs. We explore the MAEs of 2583 other 2D MOFs using our trained TMINN model. From these two databases, we obtain 11 unreported 2D ferromagnetic MOFs with MAEs over 35 meV/atom, which are further demonstrated by the high-level density functional theory calculations. Such results show good performance of the extrapolation predictions of TMINN. We also propose some simple design rules to acquire 2D MOFs with large MAEs by building a Pearson correlation coefficient map between various geometrical descriptors and MAE. Our developed TMINN model provides a powerful tool for high-throughput screening and intentional design of 2D magnetic MOFs with large MAE.
具有大垂直磁各向异性能量（MAE）的二维（2D）金属有机框架（MOF）材料是高密度磁存储的重要候选材料。当前，针对2D MOF的MAE的高通量筛选受到耗时的电子结构计算的限制。在本研究中，基于包含1440种2D MOF材料的数据库开发了一种机器学习模型，即过渡金属互连神经网络（TMINN），以快速准确地预测MAE。经过良好训练的用于MAE的TMINN模型成功捕捉了几何构型与MAE之间的一般相关性。我们使用训练好的TMINN模型探索了其他2583种2D MOF的MAE。从这两个数据库中，我们获得了11种未报道的MAE超过35 meV/原子的2D铁磁MOF，通过高水平密度泛函理论计算进一步证实了这一点。这些结果显示了TMINN外推预测的良好性能。我们还通过构建各种几何描述符与MAE之间的皮尔逊相关系数图，提出了一些简单的设计规则来获得具有大MAE的2D MOF。我们开发的TMINN模型为高通量筛选和有意设计具有大MAE的2D磁性MOF提供了一个强大的工具。
