2021/12/10 by Haidi Wang, Qingqing Feng, Wang, Haidi +5 · 1 citation
Materials Science · #FOS: Physical sciences #Machine Learning in Materials Science #Materials Science (cond-mat.mtrl-sci)
paper · pdf · doi:10.48550/arxiv.2112.05421
openalex publication_date 2021/12/10 · openalex created_date 2022/05/05 · openalex updated_date 2026/07/28
Searching ferromagnetic semiconductor materials with electrically controllable spin polarization is a long-term challenge for spintronics. Bipolar magnetic semiconductors (BMS), with valence and conduction band edges fully spin-polarized in different spin directions, show great promise in this aspect because the carrier's spin polarization direction can be easily tuned by voltage gate. Here, we propose a standard high-throughput computational screening scheme for searching BMS materials. The application of this scheme to the Materials Project database gives 11 intrinsic BMS materials (1 experimental and 10 theoretical) from nearly 40000 structures. Among them, a room temperature BMS Li2V3TeO8 (mp-771246) is discovered with a Curie temperature of 478K. Moreover, the BMS feature can be maintained well when cutting the bulk Li2V3TeO8 into (001) nanofilms for realistic applications. This work provides a feasible solution for discovering novel intrinsic BMS materials from various crystal structure databases, paving the way for realizing electric-field controlled spintronic devices.