Significant Impact of Defect Fluctuation on Charge Dynamics in CsPbI3: A Study Combining Machine Learning with Quantum Dynamics.

Abstract

In this study, we developed a machine-learned force field for CsPbI3 using a neural network potential, enabling molecular dynamics simulations (MD) with ab initio accuracy over nanoseconds. This approach, combined with ab initio MD and nonadiabatic MD, was used to study the charge trapping and recombination dynamics in both pristine and defective CsPbI3. Our simulations revealed key transitions affecting carrier lifetimes, especially in systems with iodine vacancy and interstitial iodine defects. An iodine trimer, formed when iodine replaces cesium, exhibits a high-frequency phonon mode. This mode enhances nonadiabatic coupling, accelerating charge recombination in defective systems compared to pristine ones. In the iodine vacancy system, recombination times varied significantly due to differences in NA coupling and energy gaps. The interplay between nonadiabatic coupling and pure dephasing time is crucial in determining recombination times for interstitial iodine defects. Our findings highlight the role of defect evolution in perovskites, offering insights for enhancing perovskite performance.