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

Area of Science:

• Materials Science
• Computational Chemistry
• Solid State Physics

Background:

• Cesium lead iodide (CsPbI3) is a promising perovskite material for optoelectronic applications.
• Understanding charge carrier dynamics is crucial for improving CsPbI3 device efficiency and stability.
• Defects in CsPbI3 crystal structures can significantly alter its electronic properties.

Purpose of the Study:

• To develop an accurate and efficient computational model for simulating charge dynamics in CsPbI3.
• To investigate the impact of intrinsic defects on charge carrier trapping and recombination.
• To elucidate the mechanisms governing carrier lifetimes in pristine and defective CsPbI3.

Main Methods:

• Development of a machine-learned force field using neural network potentials for CsPbI3.
• Performing molecular dynamics (MD) simulations with ab initio accuracy.
• Utilizing ab initio MD and nonadiabatic MD to study charge carrier dynamics.
• Analyzing the role of specific defects, such as iodine vacancies and interstitial iodine.

Main Results:

• The machine-learned force field enables nanosecond-scale MD simulations with ab initio accuracy.
• Iodine vacancy and interstitial iodine defects significantly influence carrier lifetimes.
• An iodine trimer defect induces a high-frequency phonon mode that enhances nonadiabatic coupling and accelerates recombination.
• Recombination times are strongly dependent on nonadiabatic coupling and energy gaps, particularly in defective systems.

Conclusions:

• Defect evolution plays a critical role in the performance of CsPbI3 perovskites.
• Nonadiabatic coupling and pure dephasing time are key factors determining charge recombination rates.
• The findings provide insights for designing strategies to mitigate defect-induced performance losses in perovskites.