Nanoscale imaging of local electrical behavior in halide perovskites

Abstract Halide perovskites (HPs) exhibit favorable characteristics for photovoltaics and LEDs including optimal optoelectronic properties, bandgap tunability, and defect tolerance. However, these devices are currently limited by their instability and the mechanism behind their degradation is not currently fully understood. Atomic force microscopy (AFM), specifically conductive (c-AFM) and Kelvin-probe force microscopy (KPFM), have been widely utilized to investigate the nanoscale electrical behavior of HPs to further the understanding of the factors driving material and device degradation. In this mini-review, we briefly discuss the operating principles of c-AFM and KPFM and highlight experiments in which these techniques have been employed to investigate the effects of grain boundaries, chemical composition, and environmental factors to the electrical stability of HPs. We also provide an overview of recent applications of machine learning (ML) to automate AFM data extraction and analysis and share a perspective on the opportunities for ML methods in AFM measurements of HPs. Graphical abstract

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Publication Details

Journal
Journal of materials research/Pratt's guide to venture capital sources
Published
2026-01-05
DOI
https://doi.org/10.1557/s43578-025-01764-8
Primary Topic
Machine Learning in Materials Science
Type
article
Field-Weighted Citation Impact
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article

Nanoscale imaging of local electrical behavior in halide perovskites

Hannah Darr, Ece Deniz, Marina S. Leite
Journal of materials research/Pratt's guide to venture capital sources
Machine Learning in Materials Science
article

Nanoscale imaging of local electrical behavior in halide perovskites

Hannah Darr, Ece Deniz, Marina S. Leite
article en

Abstract

Abstract Halide perovskites (HPs) exhibit favorable characteristics for photovoltaics and LEDs including optimal optoelectronic properties, bandgap tunability, and defect tolerance. However, these devices are currently limited by their instability and the mechanism behind their degradation is not currently fully understood. Atomic force microscopy (AFM), specifically conductive (c-AFM) and Kelvin-probe force microscopy (KPFM), have been widely utilized to investigate the nanoscale electrical behavior of HPs to further the understanding of the factors driving material and device degradation. In this mini-review, we briefly discuss the operating principles of c-AFM and KPFM and highlight experiments in which these techniques have been employed to investigate the effects of grain boundaries, chemical composition, and environmental factors to the electrical stability of HPs. We also provide an overview of recent applications of machine learning (ML) to automate AFM data extraction and analysis and share a perspective on the opportunities for ML methods in AFM measurements of HPs. Graphical abstract

Journal of materials research/Pratt's guide to venture capital sourcesVol. 41(2)
Openalex Percentile: Top 97%
Machine Learning in Materials Science
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