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利用深度学习进行可预测的碳点设计。

Exploiting deep learning for predictable carbon dot design.

作者信息

Wang Xiao-Yuan, Chen Bin-Bin, Zhang Jie, Zhou Ze-Rui, Lv Jian, Geng Xiao-Peng, Qian Ruo-Can

机构信息

Key Laboratory for Advanced Materials School of Chemistry & Molecular Engineering, East China University of Science and Technology, Shanghai, 200237, P. R. China.

出版信息

Chem Commun (Camb). 2021 Jan 14;57(4):532-535. doi: 10.1039/d0cc07882d. Epub 2020 Dec 18.

DOI:10.1039/d0cc07882d
PMID:33336670
Abstract

In this study, we developed a deep convolution neural network (DCNN) model for predicting the optical properties of carbon dots (CDs), including spectral properties and fluorescence color under ultraviolet irradiation. These results demonstrate the powerful potential of DCNN for guiding the synthesis of CDs.

摘要

在本研究中，我们开发了一种深度卷积神经网络（DCNN）模型，用于预测碳点（CDs）的光学性质，包括光谱性质和紫外光照射下的荧光颜色。这些结果证明了DCNN在指导碳点合成方面的强大潜力。

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