| Abstract
| - Models predicting fullerene solubility in 96 solvents at 298 K were developed using multiple linear regressionand feed-forward computational neural networks (CNN). The data set consisted of a diverse set of solventswith solubilities ranging from −3.00 to 2.12 log (solubility) where solubility = (1 × 104)(mole fraction ofC60 in saturated solution). Each solvent was represented by calculated molecular structure descriptors. Apool of the best linear models, as determined by rms error, was developed, and a CNN model was developedfor each of the linear models. The best CNN model was chosen based on the lowest value of a specifiedcost 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 rmserror of 0.253. The 10-compound external prediction set had an rms error of 0.346 log solubility units.
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