Dimensional Control over Metal Halide Perovskite Crystallization Guided by Active Learning

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 two-dimensional
(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 discuss
possible reaction schemes that may selectively promote the formation
of morph-Pb-I phases with different dimensionalities. 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.