Machine Learning Accelerates Innovation in Perovskite Manufacturing Scale-up
Sector: Manufacturing (Industrial) • Location: Cambridge, Massachusetts, United States of America
Source: Department of Energy (DOE)
This project is using machine learning to improve the manufacturing scale-up process for perovskite photovoltaic (PV) technologies. The methodology will speed up the research and development cycle for emerging perovskite PV technologies via the machine-learning-assisted experimental design. The team will develop a framework that combines sequential machine learning and process engineering to maxim
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Participants
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Status
Original status | closed |
Taiyo status | Obfuscated Data |
Taiyo last update | 00-00-0000 |
Available timestamps | 00-00-0000 |
Available timestamp type | Obfuscated Data |
Contact
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Description
Description | This project is using machine learning to improve the manufacturing scale-up process for perovskite photovoltaic (PV) technologies. The methodology will speed up the research and development cycle for emerging perovskite PV technologies via the machine-learning-assisted experimental design. The team will develop a framework that combines sequential machine learning and process engineering to maximize process improvements with fewer required experiments. This framework will enable rapid development of scalable deposition process for perovskite PV manufacturing. |
Original sub-sector | Obfuscated |
Original Currency | USD |
Original budget | 000000000000000 |
Procurement method | Obfuscated Data |
Budget | 000000000000000 |
Location
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Source
Source reliability | High |
Data quality score | 100% |
Source | Obfuscated Data |
URL | obfuscated_data,obfuscateddata.com |
More Details
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