Perovskites have seen significant research interest in the last decade. As ternary and quaternary compounds, their chemical space is exceptionally large, yet perovskite development has been limited to a restricted set of chemical constituents often discovered through trial and error. Here, we report a high-throughput experimental framework for the discovery of new perovskite single crystals. We use machine learning (ML) to guide the sequence of ever-improved robotic synthetic trials. We perform high-throughput syntheses of perovskite single crystals with a protein crystallization robot and characterize the outcomes with the aid of convolutional neural network-based image recognition. We then use an ML model to predict the optimal conditions for the synthesis of a new perovskite single crystal, enabling us to report the first synthesis of (3-PLA)2PbCl4.This material exhibits strong blue emission, illustrating the applicability of the method in identifying new optoelectronic materials.
Machine-learning-accelerated perovskite crystallization
Author(s): J. Kirman, A. Johnston, D. A. Kuntz, M. Askerka. Y. Gao, P. Todorovic, D. Ma, G. G. Prive, E. H. Sargent
Link: Journal Article
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