Machine-learning-based architecting of magnetoresistance phase diagrams in anomalous Hall systems.
- DOI
- 10.1093/nsr/nwag228
- Published
- 2026 Jul
- Container
- National science review
- Publisher
- Not recorded
- Open access
- yes
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Cite this work
BibTeX
@article{allodium:10.1093/nsr/nwag228,
title = {Machine-learning-based architecting of magnetoresistance phase diagrams in anomalous Hall systems.},
author = {Chen G and Bi X and Li Z and Feng X and Feng Y and Qin F and Huang J and Zhou L and He K and Ideue T and Xue QK and Yuan H},
year = {2026},
journal = {National science review},
doi = {10.1093/nsr/nwag228},
url = {https://doi.org/10.1093/nsr/nwag228}
}RIS
TY - JOUR TI - Machine-learning-based architecting of magnetoresistance phase diagrams in anomalous Hall systems. AU - Chen G AU - Bi X AU - Li Z AU - Feng X AU - Feng Y AU - Qin F AU - Huang J AU - Zhou L AU - He K AU - Ideue T AU - Xue QK AU - Yuan H PY - 2026 JO - National science review DO - 10.1093/nsr/nwag228 UR - https://doi.org/10.1093/nsr/nwag228 ER -
APA
G, C., X, B., Z, L., X, F., Y, F., F, Q., J, H., L, Z., K, H., T, I., QK, X., & H, Y. (2026). Machine-learning-based architecting of magnetoresistance phase diagrams in anomalous Hall systems.. National science review. https://doi.org/10.1093/nsr/nwag228
Source records
- pubmed · retrieved 2026-09-26T21:03:59.097Z