Machine Learning Enhanced High‐Throughput Fabrication and Optimization of Quasi‐2D Ruddlesden–Popper Perovskite Solar Cells

Abstract Organic–inorganic perovskite solar cells (PSCs) are promising candidates for next‐generation, inexpensive solar panels due to their commercially competitive cost and high power conversion efficiencies. However, PSCs suffer from poor stability. A new and vast subset of PSCs, quasi‐two‐dimensional Ruddlesden–Popper PSCs (quasi‐2D RP PSCs), has improved photostability and superior resilience to environmental conditions compared to three‐dimensional metal‐halide PSCs. To accelerate the search for new quasi‐2D RP PSCs, this work reports a combinatorial, machine learning (ML) enhanced high‐throughput perovskite film fabrication and optimization study. This work designs a bespoke experimental strategy and produces perovskite films with a range of different compositions using only spin‐coating free, reproducible robotic fabrication processes. The performance and characterization data of these solar cells are used to train a ML model that allow materials parameters to be optimized and direct the design of improved materials. The new, ML‐optimized, drop‐cast quasi‐2D RP perovskite films yield solar cells with power conversion efficiencies of up to 16.9%.