Accelerated discovery of perovskite solid solutions through automated materials synthesis and characterization

Mojan Omidvar, Hangfeng Zhang, Achintha Avin Ihalage, Theo Graves Saunders, Henry Giddens, Michael Forrester, Sajad Haq and Yang Hao ()
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Mojan Omidvar: Queen Mary University of London
Hangfeng Zhang: Queen Mary University of London
Achintha Avin Ihalage: Queen Mary University of London
Theo Graves Saunders: Queen Mary University of London
Henry Giddens: Queen Mary University of London
Michael Forrester: Cody Technology Park
Sajad Haq: Cody Technology Park
Yang Hao: Queen Mary University of London

Nature Communications, 2024, vol. 15, issue 1, 1-13

Abstract: Abstract Accelerating perovskite solid solution discovery and sustainable synthesis is crucial for addressing challenges in wireless communication and biosensors. However, the vast array of chemical compositions and their dependence on factors such as crystal structure, and sintering temperature require time-consuming manual processes. To overcome these constraints, we introduce an automated materials discovery approach encompassing machine learning (ML) assisted material screening, robotic synthesis, and high-throughput characterization. Our proposed platform for rapid sintering and dielectric analysis streamlines the characterization of perovskites and the discovery of disordered materials. The setup has been successfully validated, demonstrating processing materials within minutes, in stark contrast to conventional procedures that can take hours or days. Following setup validation with established samples, we showcase synthesizing single-phase solid solutions within the barium family, such as (BaxSr1-x)CeO3, identified through ML-guided chemistry.

Date: 2024
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DOI: 10.1038/s41467-024-50884-y

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