Quantitatively determining melting properties for energetic compounds via knowledge-infused molecular graphs and interpretable deep learning

The melting properties of energetic compounds are critical to their performances, but challenges persist in understanding the molecular features and design strategies that drive these properties. Integrating domain knowledge into data-driven approaches for predicting melting properties enhances the generation of comprehensive insights and enables the construction of interpretable prediction models. For this purpose, a knowledge-infused molecular graphs (KIMGs) were devised to describe the characters of energetic compounds, by which prediction models were developed in conjunction with the message passing neural networks. A melting-point dataset composed of around 30,000 melt-castable energetic compounds was constructed, and a collection of 29 key descriptors relevant to melting behaviors is integrated in KIMGs. The model achieved the best mean absolute error of 10.93 K for the melting point prediction. The model interpretability from both molecular graphs and feature importances offer insights for understanding the complex interplay that determine the melting properties of energetic compounds. This work enables researchers not only to predict melting behaviors with enhanced accuracy but also applicable to establishing other quantitively structure-property relationships. An interpretable machine learning model for quantitatively determining the melting behaviors of energetic materials has been developed, using knowledge-infused molecular graphs.