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Anti-Kasha System by Design: A New Gateway for Cell Differentiation Through Machine Learning
preprintsubmitted on 22.05.2020, 05:51 and posted on 26.05.2020, 05:06 by Junyi Gong, Peifa Wei, Junkai Liu, Yuncong Chen, zheng zhao, Weijun Zhao, Chao Ma, Jacky W. Y. Lam, Kam Sing Wong, Ying Li, Ben Zhong Tang
Kasha’s rule, which claimed that all emissions of excitons are from the lowest excited state and independent of excitation energy, makes the utility of high energy excitons difficult and severely hinder the widespread application of organic photoluminescent materials in real-world. For decades, scientists try to break it to unleash the power of high energy excitons but get minimal progress, no rational guiding principles, and few applications. So far, anti-Kasha’s rule is still a purely academic concept. In this contribution, we proposed a designing principle for pure organic anti-Kasha’s rule system and synthesized a series of compounds by following this designing rule which are all display evident S 2 emission in dilute solutions as proposed. Besides, we introduced a convolutional neural network as an assistant to apply the anti-Kasha’s rule luminogens to cell differentiations with high accuracy (~98.3%), which provide a new direction of applications of anti-Kasha system.