Exploring the microstructural origins of conductivity and hysteresis in metal halide perovskites via active learning driven automated scanning probe microscopy

TL;DR

This paper presents an active machine learning framework that automates scanning probe microscopy to identify microstructural features in metal halide perovskites that influence their electronic transport and hysteresis behavior.

Contribution

The study introduces a novel active learning approach for automated SPM to discover microstructures affecting transport properties in MHPs, enhancing materials analysis capabilities.

Findings

Automated SPM can identify microstructures linked to conduction and hysteresis.

Active learning accelerates the discovery of microstructural origins of transport phenomena.

Framework can be integrated with other characterization techniques for comprehensive analysis.

Abstract

Electronic transport and hysteresis in metal halide perovskites (MHPs) are key to the applications in photovoltaics, light emitting devices, and light and chemical sensors. These phenomena are strongly affected by the materials microstructure including grain boundaries, ferroic domain walls, and secondary phase inclusions. Here, we demonstrate an active machine learning framework for 'driving' an automated scanning probe microscope (SPM) to discover the microstructures responsible for specific aspects of transport behavior in MHPs. In our setup, the microscope can discover the microstructural elements that maximize the onset of conduction, hysteresis, or any other characteristic that can be derived from a set of current-voltage spectra. This approach opens new opportunities for exploring the origins of materials functionality in complex materials by SPM and can be integrated with other characterization techniques either before (prior knowledge) or after (identification of locations of interest for detail studies) functional probing.