Coupling a Crystal Graph Multilayer Descriptor to Active Learning for Rapid Discovery of 2D Ferromagnetic Semiconductors/Half-Metals/Metals
© 2020 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.
| Veröffentlicht in: | Advanced materials (Deerfield Beach, Fla.). - 1998. - 32(2020), 29 vom: 21. Juli, Seite e2002658 |
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| Weitere Verfasser: | , , , , |
| Format: | Online-Aufsatz |
| Sprache: | English |
| Veröffentlicht: |
2020
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| Zugriff auf das übergeordnete Werk: | Advanced materials (Deerfield Beach, Fla.) |
| Schlagworte: | Journal Article 2D ferromagnetic materials first-principle methods machine learning material descriptors |
| Zusammenfassung: | © 2020 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim. 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 |
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| Beschreibung: | Date Revised 30.09.2020 published: Print-Electronic Citation Status PubMed-not-MEDLINE |
| ISSN: | 1521-4095 |
| DOI: | 10.1002/adma.202002658 |