Prediction of Plasticizer Property Based on an Improved Genetic Algorithm.

ABSTRACT: Different plasticizers have obvious differences in plasticizing properties. As one of the important indicators for evaluating plasticization performance, the substitution factor (SF) has great significance for product cost accounting. In this research, a genetic algorithm with "variable mutation probability" was developed to screen the key molecular descriptors of plasticizers that are highly correlated with the SF, and a SF prediction model was established based on these filtered molecular descriptors. The results show that the improved genetic algorithm greatly improved the prediction accuracy in different regression models. The coefficient of determination (R²) for the test set and the cross-validation both reached 0.92, which is at least 0.15 higher than the R<

SUBMITTER: Zhang Y 

PROVIDER: S-EPMC9607559 | biostudies-literature | 2022 Oct

REPOSITORIES: biostudies-literature