Orbital-level engineering of bonding networks to modulate halogen migration in lead-free double perovskites.

Ionic migration plays a pivotal role in determining the performance and operational stability of a wide range of optoelectronic devices. Halide double perovskites (HDPs) have emerged as promising lead-free alternatives to conventional perovskites; however, they remain susceptible to detrimental halide ion migration. Despite this, the fundamental mechanisms underlying ionic migration in HDPs are poorly understood, hindering the development of effective strategies to mitigate such instability. Here, we have redefined ion migration as a continuous bonding reconfiguration process, demonstrating that antibonding hybridization and alignment serve as precise descriptors for governing halide ion migration. Comprehensive analyses of bonding characteristics, combined with migration pathway simulations, demonstrate that engineering the B-site cations and halogens tunes the overlap of antibonding orbitals and their energetic positions relative to the Fermi level, thereby elevating the migration barrier and suppressing halide ion motion. Based on this strategy, we further construct features related to chemical bond variations and establish a machine learning (ML) framework that enables rapid prediction across the vast chemical space of HDPs. Collectively, these findings not only advance the fundamental comprehension of ionic transport in HDPs but also furnish practical design guidelines for achieving stable, lead-free perovskite optoelectronics.