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Coupling a Crystal Graph Multilayer Descriptor to Active Learning for Rapid Discovery of 2D Ferromagnetic Semiconductors/Half‐Metals/Metals
Author(s) -
Lu Shuaihua,
Zhou Qionghua,
Guo Yilv,
Zhang Yehui,
Wu Yilei,
Wang Jinlan
Publication year - 2020
Publication title -
advanced materials
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 10.707
H-Index - 527
eISSN - 1521-4095
pISSN - 0935-9648
DOI - 10.1002/adma.202002658
Subject(s) - spintronics , materials science , ferromagnetism , magnetism , semiconductor , band gap , coupling (piping) , density functional theory , chemical stability , stability (learning theory) , magnetic semiconductor , nanotechnology , condensed matter physics , machine learning , optoelectronics , computer science , metallurgy , quantum mechanics , physics , thermodynamics
2D ferromagnetic (FM) semiconductors/half‐metals/metals are the key materials toward next‐generation spintronic devices. However, such materials are still rather rare and the material search space is too large to explore exhaustively. Here, an adaptive framework to accelerate the discovery of 2D intrinsic FM materials is developed, by combining advanced machine‐learning (ML) techniques with high‐throughput density functional theory calculations. Successfully, about 90 intrinsic FM materials with desirable bandgap and excellent thermodynamic stability are screened out and a database containing 1459 2D magnetic materials is set up. To improve the performance of ML models on small‐scale datasets like diverse 2D materials, a crystal graph multilayer descriptor using the elemental property is proposed, with which ML models achieve prediction accuracy over 90% on thermodynamic stability, magnetism, and bandgap. This study not only provides dozens of compelling FM candidates for future spintronics, but also paves a feasible route for ML‐based rapid screening of diverse structures and/or complex properties.