Machine-Learning-Assisted Design of Buried-Interface Engineering Materials for High-Efficiency and Stable Perovskite Solar Cells

Abstract

Buried-interface engineering is crucial to the performance of perovskite solar cells. Self-assembled monolayers and buffer layers at the buried interface can optimize charge transfer and reduce recombination losses. However, the complex mechanisms and the difficulty in selecting suitable functional groups pose great challenges. Machine learning (ML) offers a powerful tool for screening and identifying effective structures for interface modification. Our ML-driven approach led to the preparation of two promising organic molecules, PAPzO and PAPz, which exhibit synergistic interactions with SnO₂ and perovskites. These molecules decrease charge trap densities, elongate carrier lifetimes, and retard perovskite crystallization. PAPzO, with a stronger binding energy and better aligned energy levels, enables a power conversion efficiency (PCE) of 26.04% and long-term stability, maintaining 91.24% of its original PCE after 1,200 h of continuous maximum power point tracking. This ML-integrated approach marks a significant advancement in the development of efficient and stable perovskite photovoltaics.