Machine-Learning-Accelerated Perovskite Crystallization
| Autor: | Yuan Gao, Douglas A. Kuntz, Dongxin Ma, Mikhail Askerka, Andrew Johnston, Gilbert G. Privé, Edward H. Sargent, Petar Todorović, Jeffrey Kirman |
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| Rok vydání: | 2020 |
| Předmět: |
Materials science
business.industry 02 engineering and technology 010402 general chemistry 021001 nanoscience & nanotechnology Machine learning computer.software_genre 01 natural sciences Convolutional neural network Chemical space Blue emission 0104 chemical sciences law.invention law Optoelectronic materials General Materials Science Artificial intelligence Crystallization 0210 nano-technology Ternary operation business computer Single crystal Perovskite (structure) |
| Zdroj: | Matter. 2:938-947 |
| ISSN: | 2590-2385 |
| Popis: | Summary 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. |
| Databáze: | OpenAIRE |
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