Dimensional control over metal halide perovskite crystallization guided by active learning

04 October 2021, Version 1
This content is a preprint and has not undergone peer review at the time of posting.


Metal halide perovskite (MHP) derivatives, a promising class of optoelectronic materials, have been synthesized with a range of dimensionalities that govern their optoelectronic properties and determine their applications. We demonstrate a data-driven approach combining active learning and high-throughput experimentation to discover, control, and understand the formation of phases with different dimensionalities in the morpholinium (morph) lead iodide system. Using a robot-assisted workflow, we synthesized and characterized two novel MHP derivatives that have distinct optical properties: a one-dimensional (1D) morphPbI3 phase ([C4H10NO][PbI3]) and a 2D (morph)2PbI4 phase ([C4H10NO]2[PbI4]). To efficiently acquire the data needed to construct a machine learning (ML) model of the reaction conditions where the 1D and 2D phases are formed, data acquisition was guided by a diverse-mini-batch-sampling active learning algorithm, using prediction confidence as a stopping criterion. Querying the ML model uncovered the reaction parameters that have the most significant effects on dimensionality control. Based on these insights, we propose a reaction scheme that rationalizes the formation of different dimensional MHP derivatives in the morph-Pb-I system. The data-driven approach presented here, including the use of additives to manipulate dimensionality, will be valuable for controlling the crystallization of a range of materials over large reaction-composition spaces.


metal halide perovskite
high-throughput experimentation
machine learning
active learning


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