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

• Published in: Nature communications Vol. 15; no. 1; pp. 6554 - 13
• Main Authors: Omidvar, Mojan, Zhang, Hangfeng, Ihalage, Achintha Avin, Saunders, Theo Graves, Giddens, Henry, Forrester, Michael, Haq, Sajad, Hao, Yang
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 02.08.2024; Nature Publishing Group; Nature Portfolio
• Subjects: 119/118; 639/301/1005/1007; 639/301/930/1032; 639/301/930/12; Automation; Barium; Biosensors; Chemical composition; Chemical synthesis; Crystal structure; Dielectric properties; Humanities and Social Sciences; Machine learning; Material properties; multidisciplinary; Perovskites; Phase relationships; Science; Science (multidisciplinary); Sintering; Solid solutions; Structural analysis; Temperature dependence; Temperature requirements; Thermal analysis; Wireless communications
• ISSN: 2041-1723; 2041-1723
• DOI: 10.1038/s41467-024-50884-y

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 (Ba x Sr 1-x )CeO 3 , identified through ML-guided chemistry. Accelerated discovery of perovskite solid solutions is achieved through automated synthesis and dielectric characterisation, exploring sintering and phase relationships for ML-predicted compositions with two successful syntheses and optimizing material properties.