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DOE OSTI · 2468802

Machine Learning Accelerates Innovation in Perovskite Manufacturing Scale-up (Final Technical Report (FTR))

Buonassisi, Tonio
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Abstract

We propose to address the challenge of the vast parameter space associated with perovskite manufacturing optimization, by developing a machine learning (ML)-assisted optimization framework for a scalable perovskite PV manufacturing tool. This framework will be interpretable, sequential, and rapidly adaptable to upgraded systems (e.g., via transfer learning). The tool is an open-air rapid spray plasma process (RSPP) of perovskite films, which has already been established at Stanford and is a unique platform to test and deploy the proposed ML-guided framework because the RSPP technique is able to conduct optimization experiments with a high throughput, and easily adjust a wide range of process variables.

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14 SOLAR ENERGYBayesian OptimizationMachine LearningOptimizationPerovskitePhotovoltaics

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Buonassisi, Tonio. 2024-11-01. Machine Learning Accelerates Innovation in Perovskite Manufacturing Scale-up (Final Technical Report (FTR)). https://doi.org/10.2172/2468802

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