Towards predicting the solubility of CO2 and N2 in different polymers using a quasi-SMILES based QSPR approach
A A Toropov1, A P Toropova1, S Begum2
1a IRCCS, Istituto di Ricerche Farmacologiche Mario Negri , Milan , Italy.
Abstract:
The solubility of gases in various polymers plays an important role for the design of new polymeric materials. Quantitative structure-property relationship (QSPR) models were designed to predict the solubility of gases such as CO2 and N2 in polyethylene (PE), polypropylene (PP), polystyrene (PS), polyvinyl acetate (PVA) and poly (butylene succinate) (PBS) at different temperatures and pressures by using quasi-SMILES codes. The dataset of 315 systems was split randomly into training, calibration and validation sets; random split 1 led to 214 training (r(2) = 0.870 and RMSE = 0.019), 51 calibration (r(2) = 0.858 and RMSE = 0.020) and 50 validation (r(2) = 0.869 and RMSE = 0.017) sets. The suggested approach based on the quasi-SMILES, which are analogues of the traditional SMILES gives reasonable good predictions for solubility of CO2 and N2 in different polymers. The described methodology is universal for situations where the aim is to predict the response of an eclectic system upon a variety of physicochemical and/or biochemical conditions.
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