Abstract
Biomass conversion technologies yield unique products for which laboratory characterization of structure and properties is an ongoing challenge. In this study, Hansen solubility parameters are estimated using regularized regression as a platform for adaptable group contribution. A training set of small molecules and a set of biomass conversion molecules are parameterized using simple contributing groups. Regularized regression is then applied as a tool to reduce model complexity. This allows for flexible development of contributing groups, which are then down-selected to those which are most important, while avoiding overfitting. The model is built upon experimental data and uses only python and its science/data analysis libraries. The model also shows good agreement with other published work designed for “pencil and paper” estimation of solubility parameters. The combination of regularized regression with adaptable group contribution has potential for applications in prediction of other molecular properties for which group contribution methods are commonly employed.
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