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Tracing Ion Migration in Halide Perovskites with Machine Learned Force Fields.

Viren Tyagi^1,2, Mike Pols^1,2, Geert Brocks^1,2,3

¹Materials Simulation & Modelling, Department of Applied Physics and Science Education, Eindhoven University of Technology, 5600 MB Eindhoven, The Netherlands.

The Journal of Physical Chemistry Letters

|May 15, 2025

Summary

Machine learning force fields reveal halide defect diffusion in CsPbI3 perovskites. Charged iodide interstitials and vacancies move similarly, while neutral defects show varied mobility, impacting device stability.

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Area of Science:

Materials Science
Solid-State Physics
Computational Chemistry

Background:

Halide perovskite optoelectronic devices face challenges from chemical degradation and hysteresis.
These issues are often caused by mobile charged defects within the material.
Classical simulations struggle to accurately model the dynamic charge states of these defects.

Purpose of the Study:

To investigate the diffusion mechanisms of charged halide defects in CsPbI3 perovskites.
To develop and utilize machine-learned force fields for accurate atomic-scale simulations.
To understand how defect migration influences the stability of perovskite devices.

Main Methods:

Density functional theory (DFT) calculations were performed to train machine-learned force fields.
Atomic scale molecular dynamics (MD) simulations were conducted using these trained force fields.
The diffusion of various halide interstitial and vacancy defects (charged and neutral) in CsPbI3 was studied.

Main Results:

Negative iodide interstitials and positive iodide vacancies exhibit similar migration rates at room temperature.
Neutral iodide interstitials diffuse faster than neutral iodide vacancies.
Oppositely charged interstitials and vacancies show significantly reduced mobility, especially under device operating conditions.

Conclusions:

Machine-learned force fields enable accurate simulation of defect dynamics in halide perovskites.
The mobility of halide defects, particularly charged ones, is a critical factor in perovskite device performance and longevity.
Understanding defect diffusion is key to mitigating degradation and hysteresis in optoelectronic applications.