Publication date: 11th March 2026
Solution-processed bulk heterojunction (BHJ) organic solar cells (OSCs) have emerged as a promising next-generation photovoltaic technology. A rapidly growing strategy in this field is the use of solid additives (SAs) to precisely tailor BHJ morphology and unlock the full potential of OSCs. SA engineering provides several advantages for commercialization, including: i) tuning film-forming kinetics to accelerate high-throughput manufacturing; ii) exploiting weak noncovalent interactions between SAs and active-layer materials to enhance device efficiency and stability; and iii) simplifying processing steps to enable cost-effective, scalable fabrication. These benefits position SA engineering as a key driver for advancing OSC technologies. However, the fundamental principles governing the discovery of high-performance SAs remain unclear. In this presentation, I will introduce a closed-loop workflow that integrates high-throughput experimentation—with the capacity to generate large and diverse datasets—with Bayesian optimization to discover promising SAs for high-efficiency OSCs. By building predictive models based on molecular descriptors, we established clear correlations between SA molecular structure and device performance. Through this data-driven approach, we successfully identified a series of high-performance SAs from minimal iterative suggestions, enabling the realization of highly efficient OSCs.
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