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Artificial Intelligence for Perovskite Additive Engineering: From Molecular Screening to Autonomous Discovery
Xin-De Wang1, Zhi-Rui Chen2, Wen-Kao Li1
1School of Physics, Renmin University of China, Beijing 100872, China.
Artificial intelligence accelerates perovskite solar cell (PSC) development by enabling data-driven additive discovery. AI optimizes formulations and elucidates mechanisms, paving the way for faster commercialization.
Area of Science:
- Materials Science
- Renewable Energy
- Artificial Intelligence
Background:
- Additive engineering is vital for improving perovskite solar cell (PSC) performance.
- Discovering effective additives is challenging due to the vast chemical space and traditional trial-and-error methods.
Purpose of the Study:
- To present a paradigm shift towards AI-driven additive discovery for PSCs.
- To explore the integration of AI in optimizing PSC formulations and understanding additive mechanisms.
Main Methods:
- Establishing physicochemical foundations and machine learning descriptors for additive engineering.
- Utilizing active learning algorithms for intelligent process optimization of precursor formulations.
- Investigating AI for mechanism elucidation and employing generative models for additive discovery.
Main Results:
- AI significantly reduces experimental iterations in tuning precursor formulations.
- AI facilitates deeper understanding of additive mechanisms in PSCs.
- Generative models show potential for novel additive identification.
Conclusions:
- AI-driven approaches offer a more efficient and systematic method for additive discovery in PSCs.
- The integration of AI with autonomous laboratories promises accelerated commercialization of PSC technology.
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