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Toward self-organizing low-dimensional organic–inorganic hybrid perovskites: Machine learning-driven co-navigation of chemical and compositional spaces

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Abstract

Low-dimensional hybrid perovskites combine the richness of physical functionalities of inorganic materials and complexity and stimulus responsiveness of organic molecules in a single bulk dynamic material. The unique aspect of these materials is the thermodynamic (meta) stability, allowing for self-organized formation of complex large-period structures. Combined with the ease of fabrication, these materials not only have extensively demonstrated state-of-the-art high-performance optoelectronics, but also offer the pathway toward versatile applications, including sensors, electronic, and neuromorphic devices as well as their cost-effective mass production. However, discovery and optimization of this material require joint optimization of the composition of the inorganic components and selection of the molecular moieties, to harness the phase formation and self-assembly processes on the material level, and extend it to micro- and macroscale functional devices. Here, we discuss the potential of machine learning-driven automated experiments to accelerate the discovery of these materials, optimize the processing pathways, and transition from the lab-level to the product-level manufacturing.

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The data sets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.

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Acknowledgments

J.Y. and M.A. acknowledge support from the National Science Foundation (NSF), Award Number No. 2043205 and Alfred P. Sloan Foundation, Award No. FG-2022-18275. All authors acknowledge support from the Center for Nanophase Materials Sciences (CNMS) user facility, which is a US Department of Energy Office of Science User Facility, Project No. CNMS2021-B-00922.

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Authors and Affiliations

  1. Department of Materials Science and Engineering, Institute for Advanced Materials and Manufacturing, The University of Tennessee, Knoxville, USA

    Jonghee Yang, Sergei V. Kalinin & Mahshid Ahmadi

  2. Brain Team, Google Research, Mountain View, USA

    Ekin D. Cubuk

  3. The Center for Nanophase Materials Sciences, Oak Ridge National Laboratory, Oak Ridge, USA

    Maxim Ziatdinov

  4. Computational Sciences and Engineering Division, Oak Ridge National Laboratory, Oak Ridge, USA

    Maxim Ziatdinov

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  1. Jonghee Yang
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Yang, J., Kalinin, S.V., Cubuk, E.D. et al. Toward self-organizing low-dimensional organic–inorganic hybrid perovskites: Machine learning-driven co-navigation of chemical and compositional spaces. MRS Bulletin 48, 164–172 (2023). https://doi.org/10.1557/s43577-023-00490-y

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