posted on 2022-08-01, 18:04authored byNoor Titan Putri Hartono, Mansoor Ani Najeeb, Zhi Li, Philip W. Nega, Clare A. Fleming, Xiaohe Sun, Emory M. Chan, Antonio Abate, Alexander J. Norquist, Joshua Schrier, Tonio Buonassisi
Additives in the precursor solution can promote lead-halide
perovskite
(LHP) crystallization. We present a systematic exploration of nine
(9) bipyridine- and terpyridine-based additives selected from 29 candidates
using high-throughput single-crystal growth. To combat selection bias
and generate hypotheses for future experimental cycles of learning,
we featurize candidate additives using Mordred descriptors and compare
similarity metrics. A previously unreported additive, 6,6′-dimethyl-2,2′-dipyridyl,
is shown to work particularly well (the highest top 10th percentile is ∼3.8 mm, in comparison to ∼1.9 mm without
additive) in improving the crystallization of prototypical methylammonium
lead iodide (MAPbI3). Our strategy of machine-learning-guided
high-throughput experimentation is generally applicable to other crystal
growth problems.