aceMachine learning prediction of thermal sensitivity for energetic materials based on multi-source feature fusion
Context Thermal sensitivity, typically characterized by the thermal decomposition temperature (T d ), is a key parameter that reflects the thermal stability and safety of energetic materials, providing essential guidance for their engineering applications, storage, and transportation. However, traditional experimental determination methods suffer from long cycles, high costs, and significant hazards, rendering them unsuitable for efficient, large‑scale material screening. To address this bottleneck, this study s…
Combining molecular topological features with first-principles calculated features raised test-set accuracy for predicting decomposition temperature, offering a computational alternative to slow, costly, hazardous experiments for screening energetic materials.
Even the best fused-feature model retains a test-set RMSE of about 31.6 °C and an R2 below 0.8, so its predictions remain approximate rather than precise.
Evidence
- Peer-reviewedJournal of Molecular Modeling2026-10-09
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Truvace Impact Record TRV-2026-1685, v1: “Machine learning prediction of thermal sensitivity for energetic materials based on multi-source feature fusion.” Truvace, 2026-10-17. /record/TRV-2026-1685 (accessed at citation time). sha256 b688634a11dab81f…
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