Publication date: 15th December 2025
With the maturation of the perovskite field, which has expanded dramatically over the past two decades, the challenge of identifying new materials has become increasingly demanding. Since their discovery in the 19th century, when naturally formed perovskites attracted limited interest, synthetic perovskites have gained remarkable attention in the mid-1990s. They now span a broad range of properties, including tunable bandgap,[1] defect tolerance,[2] mechanical flexibility[3] and strong light absorption.[4]
Among these materials, layered perovskites[5–7] stand out for their hybrid organic-inorganic architecture, which enable fine control over emission,[8, 9] mechanical robustness[10] and ambient stability.[11] As the number of studied compositions and structural variants continues to grow, the search for innovative materials becomes increasingly complex.
To address this challenge we introduce an automated synthesis framework built upon efficient low-temperature synthesis[12–14] previously developed in our lab. We coupled this strategy with the creation of digital twins for real-time experiments and data collection. This combined strategy enables faster feedback loops and enhanced throughput compared to traditional trial and error workflows.
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