| Summary: | 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.
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