Dimensional Control over Metal Halide Perovskite Crystallization Guided by Active Learning

Li, Zhi; Nega, Philip W.; Nellikkal, Mansoor Najeeb; Dun, Chaochao; Zeller, Matthias; Urban, Jeffrey J.; Saidi, Wissam A.; Schrier, Joshua; Norquist, Alexander J.; Chan, Emory M.

doi:10.1021/acs.chemmater.1c03564

Dimensional Control over Metal Halide Perovskite Crystallization Guided by Active Learning

Journal Article · Wed Jan 12 00:00:00 EST 2022 · Chemistry of Materials

DOI:https://doi.org/10.1021/acs.chemmater.1c03564· OSTI ID:1854567

Li, Zhi ^[1]; Nega, Philip W. ^[1]; ^[2]; ^[1]; ^[3]; ^[1]; ^[4]; ^[5]; Norquist, Alexander J. ^[2]; ^[1]

Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States). Molecular Foundry
Haverford College, PA (United States)
Purdue Univ., West Lafayette, IN (United States)
Univ. of Pittsburgh, PA (United States)
Department of Chemistry, Fordham University, 441 E. Fordham Road, The Bronx, New York 10458, United States

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.

View Accepted Manuscript (DOE)

Research Organization:: Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States)

Sponsoring Organization:: National Science Foundation (NSF); USDOE Office of Science (SC), Basic Energy Sciences (BES)

Grant/Contract Number:: AC02-05CH11231

OSTI ID:: 1854567

Journal Information:: Chemistry of Materials, Journal Name: Chemistry of Materials Journal Issue: 2 Vol. 34; ISSN 0897-4756

Publisher:: American Chemical Society (ACS)Copyright Statement

Country of Publication:: United States

Language:: English

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Dimensional Control over Metal Halide Perovskite Crystallization Guided by Active Learning

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