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AI-Driven Discovery of Stable Wide Band Gap Perovskite Materials for Perovskite-Silicon Multi-Junction Solar Cell

CORDIS · observation · Publication date unknown

bottleneck in advancing perovskite–silicon tandem technologies. Traditional trial-and-error approaches to materials discovery are slow and labor-intensive, limiting progress. This project proposes a machine learning–guided closed-loop experimental framework to accelerate the identification of stable wide bandgap perovskite compositions. The workflow integrates robotic synthesis, high-throughput optical characterization, rapid data analysis, and predictive modeling to efficiently navigate complex multi-dimensional composition spaces. In Phase I, a Materials Acceleration Platform (MAP) will be deployed to screen diverse perovskite formulations. Machine learning algorithms will generate composition–quality maps, enabling targeted exploration without exhaustive testing. In Phase II, an Accelerated Testing Platform (ATP) will be used to evaluate the long-term stability of ML-predicted compositions under intensified stress conditions. Multiple samples will be tested in a high throughput manner to identify robust candidates. The most stable composition will then be advanced for integration

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recordType

award

status

SIGNED

region

EU

value

217965.12

unit

EUR