期刊
SCIENCE ADVANCES
卷 6, 期 20, 页码 -出版社
AMER ASSOC ADVANCEMENT SCIENCE
DOI: 10.1126/sciadv.aaz8867
关键词
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资金
- Natural Resources Canada [EIP2-MAT-001]
- Natural Sciences and Engineering Research Council of Canada (NSERC) [RGPIN 337345-13]
- Canadian Foundation for Innovation [229288]
- Canadian Institute for Advanced Research [BSE-BERL-162173]
- Canada Research Chairs Program
- SBQMI's Quantum Electronic Science and Technology Initiative
- Canada First Research Excellence Fund
- Quantum Materials and Future Technologies Program
- NSERC [RGPIN 2016-04613]
- Canada Foundation for Innovation [35833]
- NSERC Strategic Partnership Grant [STPGP 493833-16]
- Herchel Smith Graduate Fellowship
- Jacques-Emile Dubois Student Dissertation Fellowship
- Tata Sons Limited Alliance Agreement [A32391]
- Office of Naval Research [N00014-19-1-2134]
Discovering and optimizing commercially viable materials for clean energy applications typically takes more than a decade. Self-driving laboratories that iteratively design, execute, and learn from materials science experiments in a fully autonomous loop present an opportunity to accelerate this research process. We report here a modular robotic platform driven by a model-based optimization algorithm capable of autonomously optimizing the optical and electronic properties of thin-film materials by modifying the film composition and processing conditions. We demonstrate the power of this platform by using it to maximize the hole mobility of organic hole transport materials commonly used in perovskite solar cells and consumer electronics. This demonstration highlights the possibilities of using autonomous laboratories to discover organic and inorganic materials relevant to materials sciences and clean energy technologies.
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