Controlled growth of high-quality SnSe nanoplates assisted by machine learning
Machine learning (ML) approaches have emerged as powerful tools to accelerate materials discovery and optimization, offering a sustainable alternative to traditional trial-and-error methods in exploratory experiments.
Authors
- Huijia Luo (ORCID: https://orcid.org/0000-0003-1097-1583)
- Wen Lei (ORCID: https://orcid.org/0000-0002-4112-0434)
- Cailei Yuan (ORCID: https://orcid.org/0000-0002-8088-0313)
- Wenwu Pan (ORCID: https://orcid.org/0000-0001-8514-1117)
- Songqing Zhang (ORCID: https://orcid.org/0009-0006-7584-9289)
- Han Wang (ORCID: https://orcid.org/0000-0002-7476-6012)
- Yongling Ren (ORCID: https://orcid.org/0000-0002-4389-3756)
Institutions
- Jiangnan University (CN)
- The University of Western Australia (AU)
- Jiangxi Normal University (CN)
Publication Details
- Journal
- Journal of Materials Chemistry A
- Published
- 2024-11-25
- DOI
- https://doi.org/10.1039/d4ta06727d
- Citations
- 2
- Primary Topic
- Chalcogenide Semiconductor Thin Films
- Type
- article
- Field-Weighted Citation Impact
- 0.42
Funders
- University of Western Australia
- Australian Research Council