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Anti-Kasha System by Design: A New Gateway for Cell Differentiation Through Machine Learning

submitted 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.


National Natural Science Foundation 51903052

Shanghai Pujiang Project 19PJ1400700

Zhejiang Provincial Natural Science Foundation of China LR17F050001

the National Science Foundation of China 21788102, 21805002, 61735016, 61975172

Research Grants Council of Hong Kong 16305518, N-HKUST609/19, A-HKUST605/16, C6009-17G

Innovation and Technology Commission, ITC-CNERC14SC01, ITCPD/17-9

Science and Technology Plan of Shenzhen, JCY20180507183832744


Email Address of Submitting Author


Hong Kong University of Science and Technology



ORCID For Submitting Author


Declaration of Conflict of Interest

There's no conflict of interests to be declare.

Version Notes

First commit.