Colloidal organic/inorganic metal-halide perovskite nanocrystals have recently emerged as a potential low-cost replacement for the semiconductor materials in commercial photovoltaics and light emitting diodes. However, unlike III-V and IV-VI semiconductor nanocrystals, studies of colloidal perovskite nanocrystals have yet to develop a fundamental and comprehensive understanding of nucleation and growth kinetics. Here, we introduce a modular and automated microfluidic platform for the systematic studies of room-temperature synthesized cesium-lead halide perovskite nanocrystals. With abundant data collection across the entirety of four orders of magnitude reaction time span, we comprehensively characterize nanocrystal growth within a modular microfluidic reactor. The developed high-throughput screening platform features a custom-designed three-port flow cell with translational capability for in situ spectral characterization of the in-flow synthesized perovskite nanocrystals along a tubular microreactor with an adjustable length, ranging from 3 cm to 196 cm. The translational flow cell allows for sampling of twenty unique residence times at a single equilibrated flow rate. The developed technique requires an average total liquid consumption of 20 μL per spectra and as little as 2 μL at the time of sampling. It may continuously sample up to 30 000 unique spectra per day in both single and multi-phase flow formats. Using the developed plug-and-play microfluidic platform, we study the growth of cesium lead trihalide perovskite nanocrystals through in situ monitoring of their absorption and emission band-gaps at residence times ranging from 100 ms to 17 min. The automated microfluidic platform enables a systematic study of the effect of mixing enhancement on the quality of the synthesized nanocrystals through a direct comparison between single- and multi-phase flow systems at similar reaction time scales. The improved mixing characteristics of the multi-phase flow format results in high-quality perovskite nanocrystals with kinetically tunable emission wavelength, ranging as much as 25 nm at equivalent residence times. Further application of this unique platform would allow rapid parameter optimization in the colloidal synthesis of a wide range of nanomaterials (e.g., metal or semiconductor), that is directly transferable to continuous manufacturing in a numbered-up platform with a similar characteristic length scale.

From the start of a synthetic chemist’s training, experiments are conducted based on recipes from textbooks and manuscripts that achieve clean reaction outcomes, allowing the scientist to develop practical skills and some chemical intuition. This procedure is often kept long into a researcher’s career, as new recipes are developed based on similar reaction protocols, and intuition-guided deviations are conducted through learning from failed experiments. However, when attempting to understand chemical systems of interest, it has been shown that model-based, algorithm-based, and miniaturized high-throughput techniques outperform human chemical intuition and achieve reaction optimization in a much more time- and material-efficient manner; this is covered in detail in this paper. As many synthetic chemists are not exposed to these techniques in undergraduate teaching, this leads to a disproportionate number of scientists that wish to optimize their reactions but are unable to use these methodologies or are simply unaware of their existence. This review highlights the basics, and the cutting-edge, of modern chemical reaction optimization as well as its relation to process scale-up and can thereby serve as a reference for inspired scientists for each of these techniques, detailing several of their respective applications.

In the fine chemicals industry, reaction screening and optimisation are essential to development of new products. However, this screening can be extremely time and labor intensive, especially when intuition is used. Machine learning offers a solution through iterative suggestions of new experiments based on past experimental data, but knowing which machine learning strategy to apply in a particular case is still difficult. Here, we develop chemically‐motivated virtual benchmarks for reaction optimisation and compare several strategies on these benchmarks. The benchmarks and strategies are encompassed in an open‐source framework named Summit. The results of our tests show that Bayesian optimisation strategies perform very well across the types of problems faced in chemical reaction optimisation, while many strategies commonly used in reaction optimisation fail to find optimal solutions.

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<p>In the fine chemicals industry, reaction screening and optimisation are essential to development of new products. However, this screening can be extremely time and labor intensive, especially when intuition is used. Machine learning offers a solution through iterative suggestions of new experiments based on past experimental data, but knowing which machine learning strategy to apply in a particular case is still difficult. Here, we develop chemically-motivated virtual benchmarks for reaction optimisation and compare several strategies on these benchmarks. The benchmarks and strategies are encompassed in an open source framework named Summit. The results of our tests show that Bayesian optimisation strategies perform very well across the types of problems faced in chemical reaction optimisation, while many strategies commonly used in reaction optimisation fail</p> <p>to find optimal solutions.</p>