Advancing Perovskite PVs: The Role of Machine Learning in Stability and Scalability

Perovskite solar photovoltaics (PVs) are promising due to their high efficiency and affordability. Numerous experimental methods have been developed over the past decade to enhance stability, yet identifying the most effective strategies remains challenging. Machine learning (ML) offers a powerful approach by analyzing data to identify optimal conditions for stability. ML can predict material properties like bandgap and defect energies with accuracy comparable to Density Functional Theory (DFT) [1]. It has also improved compositional engineering, uncovering stable formulations [2]. Understanding defect chemistry is key to reducing recombination losses and prolonging device lifetimes [3]. ML aids in managing defects for better stability and performance [4]. Integrating ML with scalable fabrication can drive commercialization of efficient, flexible multi-junction perovskite PVs.

1. Hui et al, "Machine learning for perovskite solar cell design" Computational Materials Science Vol 226, 112215 (2023)

2. Guo et al, "Understanding Defects in Perovskite Solar Cells through Computation: Current Knowledge and Future Challenge" Advanced Science Vol 11, Issue 20 2305799 (2024)

3. Li et al, "Current State and Future Perspectives of Printable Organic and Perovskite Solar Cells" Advanced Materials Volume 36, Issue 17 2307161 (2023)

4. Tao et al, "Machine learning for perovskite materials design and discovery" npj Computational Materials volume 7, Article number: 23 (2021)