Gong Junyi, Deng Ziwei, Xie Huilin, Qiu Zijie, Zhao Zheng, Tang Ben Zhong
School of Science and Engineering, Shenzhen Institute of Aggregate Science and Technology, The Chinese University of Hong Kong, Shenzhen (CUHK-SZ), 2001 Longxiang Road, Longgang District, Shenzhen, Guangdong, 518172, P. R. China.
Faculty of Chemistry, Shenzhen MSU-BIT University, Longgang, Shenzhen, Guangdong, 518172, P. R. China.
Adv Sci (Weinh). 2025 Jan;12(3):e2411345. doi: 10.1002/advs.202411345. Epub 2024 Nov 22.
This work presents a novel methodology for elucidating the characteristics of aggregation-induced emission (AIE) systems through the application of data science techniques. A new set of chemical fingerprints specifically tailored to the photophysics of AIE systems is developed. The fingerprints are readily interpretable and have demonstrated promising efficacy in addressing influences related to the photophysics of organic light-emitting materials, achieving high accuracy and precision in the regression of emission transition energy (mean absolute error (MAE) ∼ 0.13eV) and the classification of optical features and excited state dynamics mechanisms (F1score ∼ 0.94). Furthermore, a conditional variational autoencoder and integrated gradient analysis are employed to examine the trained neural network model, thereby gaining insights into the relationship between the structural features encapsulated in the fingerprints and the macroscopic photophysical properties. This methodology promotes a more profound and thorough comprehension of the characteristics of AIE and guides the development strategies for AIE systems. It offers a solid and overarching framework for the theoretical analysis involved in the design of AIE-generating compounds and elucidates the optical phenomena associated with these compounds.
这项工作提出了一种新颖的方法,通过应用数据科学技术来阐明聚集诱导发光(AIE)系统的特性。开发了一套专门针对AIE系统光物理特性的新化学指纹。这些指纹易于解释,并在解决与有机发光材料光物理相关的影响方面显示出有前景的效果,在发射跃迁能量回归(平均绝对误差(MAE)约为0.13eV)以及光学特征和激发态动力学机制分类(F1分数约为0.94)方面实现了高精度和高精准度。此外,使用条件变分自编码器和积分梯度分析来检查训练后的神经网络模型,从而深入了解指纹中封装的结构特征与宏观光物理性质之间的关系。这种方法促进了对AIE特性更深入和全面的理解,并指导了AIE系统的开发策略。它为AIE生成化合物设计中涉及的理论分析提供了一个坚实且全面的框架,并阐明了与这些化合物相关的光学现象。