Accelerated discovery of perovskite solid solutions through automated materials synthesis and characterization.
basic_science · Level V
Where this comes from
- Record sourced from PubMed, PMID 39095463.
- Also identified by DOI 10.1038/s41467-024-50884-y and PMC identifier 11297172.
- Licence recorded as CC BY.
- The licence permits redistribution, so the abstract is shown in full and the full text is available from the publisher.
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 (Ba<sub>x</sub>Sr<sub>1-x</sub>)CeO<sub>3</sub>, identified through ML-guided chemistry.