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• W2017131386 abstract "Models predicting fullerene solubility in 96 solvents at 298 K were developed using multiple linear regression and feed-forward computational neural networks (CNN). The data set consisted of a diverse set of solvents with solubilities ranging from -3.00 to 2.12 log (solubility) where solubility = (1 x 10(4))(mole fraction of C60 in saturated solution). Each solvent was represented by calculated molecular structure descriptors. A pool of the best linear models, as determined by rms error, was developed, and a CNN model was developed for each of the linear models. The best CNN model was chosen based on the lowest value of a specified cost function and had an architecture of 9-3-1. The 76-compound training set for this model had a root-mean-square error of 0.255 log solubility units, while the 10-compound cross-validation set had an rms error of 0.253. The 10-compound external prediction set had an rms error of 0.346 log solubility units." @default.
• W2017131386 created "2016-06-24" @default.
• W2017131386 creator A5043569279 @default.
• W2017131386 creator A5064557777 @default.
• W2017131386 date "2001-01-16" @default.
• W2017131386 modified "2023-09-23" @default.
• W2017131386 title "Prediction of C₆₀ Solubilities from Solvent Molecular Structures" @default.
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• W2017131386 doi "https://doi.org/10.1021/ci000140s" @default.
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