Optimized Multi-Metal Sensitized Phosphor for Enhanced Red Up-conversion Luminescence by Machine Learning.

In this research, machine learning including genetic algorithm (GA) and support vector machine (SVM) algorithm is used to solve the "low up-conversion luminescence (UCL) intensity" problem in order to find the optimal phosphor with enhanced red UCL emis-sion using of multi-element K/Li/Mn metal modulation. Compared with the first genera-tion of phosphors, the best phosphors' fluorescence intensity occurs in the third genera-tion optimized by GA, with a stronger brightness (4.91-fold), a higher relative quantum yield (6.40-fold), and an enhanced tissue penetration depth (by 5 mm). The single and multiple dopants effect on the upconversion intensity of K+Li+Mn sensitizers are also studied: the intensity increases first and then decreases with the increase of Yb/Er/K+Li+Mn content, and the optimized K+Li+Mn concentration is 6.03%. In order to confirm the stability of the brightness optimization by GA, a batch of phosphors were synthesized with the same element proportion, and the similarity of fluorescence intensity of two batches of phosphors was evaluated by SVM algorithm with classification accura-cy index. Finally, the optimized phosphor was used for bio-imaging and phosphor-LED.