Interpretable and Physicochemical-Intuitive Deep Learning Approach for the Design of Thermal Resistance of Energetic Compounds

Thermal resistance of energetic materials
is critical due to its
impact on safety and sustainability. However, developing predictive
models remains challenging because of data scarcity and limited insights
into quantitative structure–property relationships. In this
work, a deep learning framework, named EM-thermo, was proposed to
address these challenges. A data set comprising 5029 CHNO compounds,
including 976 energetic compounds, was constructed to facilitate this
study. EM-thermo employs molecular graphs and direct message-passing
neural networks to capture structural features and predict thermal
resistance. Using transfer learning, the model achieves an accuracy
of approximately 97% for predicting the thermal-resistance property
(decomposition temperatures above 573.15 K) in energetic compounds.
The involvement of molecular descriptors improved model prediction.
These findings suggest that EM-thermo is effective for correlating
thermal resistance from the atom and covalent bond level, offering
a promising tool for advancing molecular design and discovery in the
field of energetic compounds.