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This study introduces a differential learning approach using message passing neural networks to predict relative thermal stability differences between energetic materials, addressing the challenge of inconsistent experimental measurements across laboratories. By focusing on relative rather than absolute decomposition temperatures, the method achieves over 85% accuracy in ranking compounds, significantly outperforming traditional regression techniques. The research also highlights bond dissociation enthalpy as a critical factor influencing thermal stability, enhancing our understanding of thermal decomposition chemistry.
Learning relative thermal stability differences can achieve over 85% accuracy, outperforming conventional methods in a field plagued by experimental variability.
Predicting thermal stability during handling and storage is essential for the design of safe and reliable energetic materials. However, experimental measurements vary significantly across laboratories due to differences in protocols and analysis methods, making it difficult to train reliable predictive models. We address this challenge through differential learning. Rather than predicting absolute decomposition temperatures, we instead train message passing neural networks to predict relative differences between pairs of molecules. This approach reduces sensitivity to systematic experimental errors and achieves>85% accuracy in ranking compounds by thermal stability, outperforming conventional regression methods on the same heterogeneous dataset. To understand what drives these predictions, we compare neural network models with interpretable alternatives built from descriptors derived from ab initio calculations and cheminformatics software. This analysis identifies bond dissociation enthalpy as a key determinant of thermal stability rankings, providing further insight into the complex chemistry of thermal decomposition. The differential learning framework generalizes across model architectures, from graph neural networks to classical descriptor-based approaches. Our results demonstrate that learning relative properties rather than absolute values offers a practical solution for modeling noisy experimental data, with direct applications in materials design where thermal stability predictions inform safety protocols.
