期刊
JOURNAL OF PHYSICS-CONDENSED MATTER
卷 30, 期 6, 页码 -出版社
IOP Publishing Ltd
DOI: 10.1088/1361-648X/aaa471
关键词
magnet; materials informatics; density functional theory; machine learning
资金
- JSPS KAKENHI [JP17K14803]
- Materials research by Information Integration Initiative (MI2I) project of the Support Program for Starting Up Innovation Hub from Japan Science and Technology Agency (JST)
- Grants-in-Aid for Scientific Research [17K14803] Funding Source: KAKEN
Two-dimensional (2D) magnets are explored in terms of data science and first principle calculations. Machine learning determines four descriptors for predicting the magnetic moments of 2D materials within reported 216 2D materials data. With the trained machine, 254 2D materials are predicted to have high magnetic moments. First principle calculations are performed to evaluate the predicted 254 2D materials where eight undiscovered stable 2D materials with high magnetic moments are revealed. The approach taken in this work indicates that undiscovered materials can be surfaced by utilizing data science and materials data, leading to an innovative way of discovering hidden materials.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据