Correction: How fast do defects migrate in halide perovskites: insights from on-the-fly machine-learned force fields

Area of Science:

• Materials Science
• Computational Chemistry
• Solid-State Physics

Context:

• Halide perovskites are promising materials for optoelectronic applications.
• Understanding defect migration is crucial for device stability and performance.
• Previous studies have explored defect dynamics using various computational methods.

Purpose:

• To correct and refine the findings presented in the original article regarding defect migration speeds.
• To provide accurate data derived from on-the-fly machine-learned force fields.
• To enhance the understanding of defect behavior in halide perovskites.

Summary:

• The correction addresses specific numerical values and interpretations related to defect migration rates.
• It reaffirms the utility of on-the-fly machine-learned force fields for accurate simulations.
• The revised data offers a more precise picture of how defects move within halide perovskite structures.

Impact:

• Ensures the scientific community uses accurate data for halide perovskite research.
• Facilitates more reliable predictions of material stability and device longevity.
• Improves the foundational knowledge for designing next-generation perovskite-based technologies.