Machine Learning-Driven Insights for Phase-Stable FAxCs1–xPb(IyBr1–y)3 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 x Cs1-x Pb(I y Br1-y )3 (where 0 ≤ x ≤ 1 and 0 ≤ y ≤ 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 x Cs1-x Pb(I y Br1-y )3 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 x Cs1-x Pb(I y Br1-y )3 encounters less severe challenges in addressing phase segregation issues than perovskites/perovskites tandem solar cells with 1.8 eV FA x Cs1-x Pb(I y Br1-y )3.