Machine Learning-Driven Insights for Phase-Stable FA Cs Pb(I Br ) Perovskites in Tandem Solar Cells.

The inherent chemical tunability of perovskite materials has spurred extensive research into composition engineering within the perovskite community. However, identifying the optimal composition across a broad range of variations still remains a significant challenge. Conventional trial-and-error methods are prohibitively expensive and environmentally taxing for comprehensive screening. Here, we employed machine learning-accelerated atomic simulation to guide the design of stable perovskite solar cells absorbers. Our approach entailed training of a neural network (NN) potential using data generated from first-principles calculations, yielding a perovskite NN potential exhibiting high accuracy. Utilizing this NN potential, we constructed a phase diagram for FA Cs Pb(I Br ) (where 0 ≤ ≤ 1 and 0 ≤ ≤ 1, FA denotes formamidinium cation). Integrating this with a band gap diagram, we successfully identified global optimal perovskite compositions for tandem applications with 1.7 and 1.8 eV band gaps. We have identified that all FA Cs Pb(I Br ) with >1.8 eV band gaps are thermodynamically vulnerable to phase segregation and developed a strategy to stabilize thermodynamically unstable phases by suppressing phase segregation kinetics. Finally, theoretical predictions were confirmed by the corresponding experiments. Our results suggest that creating perovskites/Si tandem solar cells with 1.7 eV FA Cs Pb(I Br ) encounters less severe challenges in addressing phase segregation issues than perovskites/perovskites tandem solar cells with 1.8 eV FA Cs Pb(I Br ).