Element tuning of targeted materials and obtaining the optimal synthesis recipe are major goals for many material scientists. However, this is often limited by conventional trial-and-error procedures, which are time-consuming and labor-intensive. In this work, fine element tuning of halide double perovskite Cs2NaxAg1-xInyBi1-yCl6 is conducted by performing a data-driven investigation combining high-throughput experiments with machine learning (ML). A positive correlation between the more accessible R value in emission RGB values (the intensities of the red/green/blue primary colors) and photoluminescence intensity is revealed, and over a thousand R values of the Cs2NaxAg1-xInyBi1-yCl6 crystals synthesized with different additives and element compositions are collected. More importantly, the volume ratios of Na+/Ag+ (VNa: VAg) and Bi3+/In3+ (VBi: VIn) with the corresponding R values are correlated through ML, and the synergistic regulation of the two ion pairs is revealed. A possible correlation between R and XRD is also proposed. Finally, different emission intensities of LED beads coated with Cs2NaxAg1-xInyBi1-yCl6 synthesized using parameters obtained from ML are demonstrated, and an emission enhancement of ≈50 times is observed between the brightest and dimmest LEDs. This work illustrates that data-driven investigation helps guide material synthesis and will significantly reduce the workload for developing novel materials, especially for complex compositions.
- double halide perovskites
- synthesis design
ASJC Scopus subject areas
- Electronic, Optical and Magnetic Materials
- Atomic and Molecular Physics, and Optics