Assisted Energetic Material Property Prediction through Advanced Transfer Learning with Graph Neural Networks

In this study, we explore the use of transfer learning to predict the properties of energetic materials using a force-field-inspired transformer graph neural network (FFiTrNet). We began by pretraining the model on a large data set of CHNOF compounds and then fine-tuning it on a smaller data set of experimental enthalpy of formation data for energetic materials. Our results show that transfer learning significantly enhances the accuracy of predicting the enthalpy of formation, with a reduction in mean absolute error and root-mean-square error compared to direct training on the smaller data set. Furthermore, we demonstrate the effectiveness of transfer learning in predicting other properties of energetic materials, highlighting its potential to improve the predictive capabilities of machine learning models for a range of energetic materials properties. The result is the most accurate among the state-of-the-art models for predicting energetic material properties. The data set used in the fine-tuning enriches the database of energetic materials' properties, making this valuable data publicly available for future research.