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Differential Learning for Robust Prediction of Thermal Stability with Application to Energetic Materials

arXiv · AI, language, vision and robotics · article · Aug 24, 2026 · UTC

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 e

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