结晶
碘化物
钙钛矿(结构)
三吡啶
Crystal(编程语言)
卤化物
化学
晶体生长
化学工程
材料科学
无机化学
结晶学
计算机科学
有机化学
程序设计语言
工程类
金属
作者
Noor 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
标识
DOI:10.1021/acs.cgd.2c00522
摘要
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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