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Exploring the Relationship of Microstructure and Conductivity in Metal Halide Perovskites via Active Learning-Driven Automated Scanning Probe Microscopy.
Liu, Yongtao; Yang, Jonghee; Vasudevan, Rama K; Kelley, Kyle P; Ziatdinov, Maxim; Kalinin, Sergei V; Ahmadi, Mahshid.
Afiliação
  • Liu Y; Center for Nanophase Materials Sciences, Oak Ridge National Laboratory, Oak Ridge, Tennessee 37830, United States.
  • Yang J; Department of Materials Science and Engineering, University of Tennessee, Knoxville, Tennessee 37996, United States.
  • Vasudevan RK; Center for Nanophase Materials Sciences, Oak Ridge National Laboratory, Oak Ridge, Tennessee 37830, United States.
  • Kelley KP; Center for Nanophase Materials Sciences, Oak Ridge National Laboratory, Oak Ridge, Tennessee 37830, United States.
  • Ziatdinov M; Center for Nanophase Materials Sciences, Oak Ridge National Laboratory, Oak Ridge, Tennessee 37830, United States.
  • Kalinin SV; Computational Sciences and Engineering Division, Oak Ridge National Laboratory, Oak Ridge, Tennessee 37830, United States.
  • Ahmadi M; Department of Materials Science and Engineering, University of Tennessee, Knoxville, Tennessee 37996, United States.
J Phys Chem Lett ; 14(13): 3352-3359, 2023 Apr 06.
Article em En | MEDLINE | ID: mdl-36994975
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.

Texto completo: 1 Base de dados: MEDLINE Idioma: En Ano de publicação: 2023 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Idioma: En Ano de publicação: 2023 Tipo de documento: Article