The predictions of photophysical parameters are of crucial practical importance for the development of functional organic fluorescent materials, whereas the expense of quantum mechanical calculations and the relatively low universality of QSAR models have challenged the task. New avenues opened up by machine learning (ML), we establish a database of solvated organic fluorescent dyes and develop highly efficient ML models for the predictions of maximum emission/absorption wavelength and photoluminescence quantum yields, providing a reliable and efficient approach to high-throughput screenings. Various combinations of ML algorithms and molecular fingerprints were investigated. For emission wavelengths, TD-DFT accuracy was achieved under real-world conditions. Reliable identification of strong fluorescent materials was also demonstrated. We show that the easily obtainable consensus fingerprint inputs combined with proper ML algorithms enables efficient re-training based on additional datapoints whereby systematic improvements of our ML models can be achieved.

光物理参数的预测对于功能性有机荧光材料的开发具有至关重要的现实意义，而量子力学计算的费用和QSAR模型相对较低的通用性却对这项任务提出了挑战。机器学习（ML）开辟了新途径，我们建立了溶剂化有机荧光染料数据库，并开发了用于预测最大发射/吸收波长和光致发光量子产率的高效ML模型，为高通量提供了可靠而有效的方法放映。研究了机器学习算法和分子指纹的各种组合。对于发射波长，在实际条件下达到了TD-DFT精度。还证实了强荧光材料的可靠鉴定。