Tuning Hot Carrier Dynamics in Vacancy-Ordered Halide Perovskites through Lattice Compression: Insight from ab Initio Quantum Dynamics and Machine Learning

Abstract

The efficient harvesting of hot carriers (HCs) from high-energy photons can significantly enhance the optoelectronic performances. However, ultrafast HC cooling through intraband transitions poses a significant challenge for extraction using traditional semiconductor absorbers. The stable and compressible vacancy-ordered halide perovskites with isolated octahedra exhibit discrete electronic states at the conduction band edge, indicating the possible slow cooling of hot electrons (HEs). Using state-of-the-art ab initio quantum dynamics simulations and unsupervised machine learning (ML), we investigate the effect of lattice stress on HE dynamics in vacancy-ordered Cs₂SnBr₆. The moderate stress enhances structural rigidity and weakens dynamic electron–phonon interactions at the conduction bands. Such modifications and the widened energy gap at the conduction band edge partially suppress intraband nonadiabatic transitions, eventually elongating the HE lifetime. The pairwise mutual information extracts hard-to-find highly nonlinear dynamic structure-excited state property correlations, offering the unique opportunity to design efficient lead-free halide perovskites for HE-based optoelectronics strategically.