Machine-Learning Force Fields Reveal Shallow Electronic States on Dynamic Halide Perovskite Surfaces

Abstract

The spectacular performance of halide perovskites in optoelectronic devices is rooted in their tolerance to defects. Previous studies showed that defects in these materials generate shallow electronic states. However, how these shallow states persist amid the pronounced atomic dynamics on halide perovskite surfaces remains unknown. This work reveals that electronic states at surfaces of prototypical CsPbBr$_3$ are energetically distributed at room temperature akin to well-passivated inorganic semiconductors, even when covalent bonds remain cleaved and undercoordinated. Specifically, a striking tendency for shallow surface states is found with approximately 70% of surface-state energies appearing within 0.2 eV or ${\approx}8k_\text{B}T$ from the valence-band edge. While these findings do not rule out occurrence of deep traps per se, they show that even when surface states appear deeper in the gap, they are not energetically isolated and are less likely to act as traps. We achieve this result by accelerating first-principles calculations via machine learning and show that the unique atomic dynamics in these materials render the formation of deep electronic states at their surfaces unlikely. These findings reveal the microscopic mechanism behind the low density of deep states at dynamic halide perovskite surfaces, which is key to their device performance.