Machine learning accelerated carbon neutrality research using big data—from predictive models to interatomic potentials

Wu, Lingjun; Xu, Zhenming; Wang, ZiXuan; Chen, ZiJian; Huang, Zhichao; Peng, Chao; Pei, Xiangdong; Li, Xiangguo; Mailoa, Jonathan P.; Hsieh, Chang‐Yu; Wu, Tao; Yu, Xue‐Feng; Zhao, Haitao

doi:10.1007/s11431-022-2095-7

Cited by 1 publication

(1 citation statement)

References 126 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In recent years, data-driven material research which benefits from interdisciplinary integration of material science and artificial intelligence (AI), greatly accelerates the pace of developing novel materials with desired properties. [1][2][3][4][5][6][7][8] Compared to the time-consuming and labor-intensive conventional trial-and-error procedures widely used in optimizing material synthesis, data-driven material research being able to process high-throughput data significantly decreases the workload. Especially, it becomes more powerful when multiple factors, e.g., the elements, compositions, etc., are taking into consideration.…”

Section: Introductionmentioning

confidence: 99%

Data‐Driven Fine Element Tuning of Halide Double Perovskite for Enhanced Photoluminescence

Wu,

Chen,

Yuan

et al. 2023

Advanced Optical Materials

Self Cite

View full text Add to dashboard Cite

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.

show abstract