A New Functional Group Selection Method for Group Contribution Models and Its Application in the Design of Electronics Cooling Fluids

This paper presents a new method to identify functional groups in molecular structures. This method relies on a deterministic optimization model that decomposes each molecular structure into the smallest number of nonoverlapping functional groups while ensuring each group holds the maximum amount of information. The group selection method is applied to construct group contribution (GC) models to predict eight pure component properties. The proposed GC models are built on a large data set and enable property prediction of silicon-containing compounds. We rely on the minimization of an information criterion in order to select a model that prevents overfitting and generalizes well. The resulting GC models demonstrate good predictive power and reliability on both the training set and cross-validation calculations. The GC models are subsequently embedded in a molecular design framework to design organosilicon coolants systems. Our calculations suggest that the molecular designs identified in this work outperform current commercial coolants and nonorganosilicon coolants by a considerable margin in terms of heat transfer efficiency and environmental properties.