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Related Experiment Video

Updated: Jun 15, 2026

Flash Infrared Annealing for Perovskite Solar Cell Processing

Flash Infrared Annealing for Perovskite Solar Cell Processing

Published on: February 3, 2021

Algorithm-Guided Experimentation for Optimization of High-Performance Perovskite Solar Cells.

Donghyun Oh¹, Sanggyun Kim², Carlo A R Perini²

¹School of Chemical and Biomolecular Engineering, Georgia Institute of Technology, Atlanta, Georgia 30332, United States.

ACS Energy Letters

|December 18, 2025

Summary

This summary is machine-generated.

This study introduces a data-driven framework to optimize perovskite solar cells (PSCs). The algorithm-guided approach enhanced power conversion efficiency from 20.3% to 23.1% using fewer than 100 experiments.

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Area of Science:

Materials Science
Renewable Energy
Chemical Engineering

Background:

Perovskite solar cells (PSCs) face performance challenges due to complex, interdependent fabrication variables.
Optimizing PSCs requires navigating a vast design space with numerous processing parameters and material compositions.

Purpose of the Study:

To develop a systematic, data-driven framework for optimizing perovskite solar cell performance.
To utilize algorithm-guided experimentation to efficiently explore the PSC design space.

Main Methods:

Implemented a model-based, derivative-free optimization algorithm for systematic exploration.
Focused on optimizing key processing parameters across the PSC device structure.
Employed fewer than 100 experimental designs for optimization.

Main Results:

Achieved a significant improvement in reverse-scanning power conversion efficiency from 20.3% to 23.1%.
Optimized up to six processing parameters without changing chemical composition or device configuration.
Demonstrated efficient design space exploration and identification of optimal processing conditions.

Conclusions:

The data-driven, algorithm-guided framework effectively optimizes PSC performance.
This approach offers a powerful combination of mathematical optimization and experimental research.
The methodology is applicable to diverse scientific fields with complex, interrelated optimization challenges.