Exploiting deep learning for predictable carbon dot design
Xiaoyuan Wang, Binbin Chen, Jie Zhang, Ze‐Rui Zhou, Jian Lv, Xiao-Peng Geng, Ruo‐Can Qian
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.
Topics & Concepts
UltravioletDeep learningCarbon fibersSpectral propertiesFluorescenceConvolution (computer science)Convolutional neural networkMaterials scienceComputer scienceLayer (electronics)IrradiationOptoelectronicsNanotechnologyArtificial intelligenceArtificial neural networkOpticsPhysicsAlgorithmComposite numberAstrophysicsNuclear physicsCarbon and Quantum Dots ApplicationsElectrochemical sensors and biosensorsAdvanced Nanomaterials in Catalysis