Pushing the boundaries for fuel discovery with a multiview features fusion approach

Abstract

Global warming poses a serious challenge to the human environment, prompting us to rapidly develop new environmentally friendly fuels. However, the time and cost required to determine the physical properties of fuels are constrained by the related industries. In this paper, we propose a multiview features fusion method based on neural networks. This method uses the eight graph neural networks models as the basis of the multichannel network coordinator to extract the compound's molecular feature; also the functional groups in the compound are embedded with molecule weight as functional groups feature, and finally, combining the molecular view with the functional groups view for the prediction of compound flash point (FP). We used a data set consisting of 2200 hydrocarbons and oxygenated compounds for model training and testing. The results show that the model performance is improved in both after feature fusion. Finally, the ablation experiments demonstrate that the use of this method is effective and provides a new idea for fast and accurate screening of fuels. The Attentive FP-FG model was the most effective, with a mean absolute error of 4.395 K, root mean square error of 5.854 K, and R-squared score (R²) of 0.986.