In Silico Design of Freeform Solar Cell Structures from High-Throughput Artificial Intelligence-Generated Configurations

Abstract

Recent advances in artificial intelligence-generated configurations (AIGC) have transformed various fields in science and technology domains and enabled the inverse design of materials and structures for enhanced performance with minimal human input. The patterned micro-/nanostructures are widely adopted to increase power conversion efficiencies in thin-film organic solar cells (OSCs), making these structures highly suitable for AIGC applications. Although the computational cost of traditional numerical simulations is a barrier to making AIGC high throughput, this issue is addressed by integrating the high-speed automated machine learning (AutoML) Analyzer with genetic algorithm-based topology optimization algorithms. Compared to standard numerical solutions, the AutoML Analyzer in the proposed system predicts outcomes ≈22 700 times faster, resulting in a 98.47% reduction in computation costs, while maintaining high average accuracies of ≈99%. By evaluating an extensive dataset of 600 000 AIGCs, an optimized PM6:Y6 OSC device design with a power conversion efficiency of 17.58% is identified, significantly outperforming the 14.70% baseline efficiency of the OSC device without the poly methyl methacrylate scattering and substrate layers. Results underscore the potential of AIGC techniques for efficiently enhancing the performance of photovoltaic devices in renewable energy applications.