TY - JOUR
T1 - Machine Learning-Assisted Carbon Dot Synthesis
T2 - Prediction of Emission Color and Wavelength
AU - Senanayake, Ravithree D.
AU - Yao, Xiaoxiao
AU - Froehlich, Clarice E.
AU - Cahill, Meghan S.
AU - Sheldon, Trever R.
AU - McIntire, Mary
AU - Haynes, Christy L.
AU - Hernandez, Rigoberto
N1 - Publisher Copyright:
© 2022 American Chemical Society. All rights reserved.
PY - 2022/12/12
Y1 - 2022/12/12
N2 - Carbon dots (CDs) have attracted great attention in a range of applications due to their bright photoluminescence, high photostability, and good biocompatibility. However, it is challenging to design CDs with specific emission properties because the syntheses involve many parameters, and it is not clear how each parameter influences the CD properties. To help bridge this gap, machine learning, specifically an artificial neural network, is employed in this work to characterize the impact of synthesis parameters on and make predictions for the emission color and wavelength for CDs. The machine reveals that the choice of reaction method, purification method, and solvent relate more closely to CD emission characteristics than the reaction temperature or time, which are frequently tuned in experiments. After considering multiple models, the best performing machine learning classification model achieved an accuracy of 94% in predicting relative to actual color. In addition, hybrid (two-stage) models incorporating both color classification and an artificial neural network k-ensemble model for wavelength prediction through regression performed significantly better than either a standard artificial neural network or a single-stage artificial neural network k-ensemble regression model. The accuracy of the model predictions was evaluated against CD emission wavelengths measured from experiments, and the minimum mean average error is 25.8 nm. Overall, the models developed in this work can effectively predict the photoluminescence emission of CDs and help design CDs with targeted optical properties.
AB - Carbon dots (CDs) have attracted great attention in a range of applications due to their bright photoluminescence, high photostability, and good biocompatibility. However, it is challenging to design CDs with specific emission properties because the syntheses involve many parameters, and it is not clear how each parameter influences the CD properties. To help bridge this gap, machine learning, specifically an artificial neural network, is employed in this work to characterize the impact of synthesis parameters on and make predictions for the emission color and wavelength for CDs. The machine reveals that the choice of reaction method, purification method, and solvent relate more closely to CD emission characteristics than the reaction temperature or time, which are frequently tuned in experiments. After considering multiple models, the best performing machine learning classification model achieved an accuracy of 94% in predicting relative to actual color. In addition, hybrid (two-stage) models incorporating both color classification and an artificial neural network k-ensemble model for wavelength prediction through regression performed significantly better than either a standard artificial neural network or a single-stage artificial neural network k-ensemble regression model. The accuracy of the model predictions was evaluated against CD emission wavelengths measured from experiments, and the minimum mean average error is 25.8 nm. Overall, the models developed in this work can effectively predict the photoluminescence emission of CDs and help design CDs with targeted optical properties.
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U2 - 10.1021/acs.jcim.2c01007
DO - 10.1021/acs.jcim.2c01007
M3 - Article
C2 - 36394850
AN - SCOPUS:85142529690
SN - 1549-9596
VL - 62
SP - 5918
EP - 5928
JO - Journal of Chemical Information and Modeling
JF - Journal of Chemical Information and Modeling
IS - 23
ER -