ArticleJournal of molecular modeling2026
Machine learning prediction of thermal sensitivity for energetic materials based on multi-source feature fusion.
Article in Journal of molecular modeling, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
contextThermal sensitivity, typically characterized by the thermal decomposition temperature (T
methodsAll machine learning modeling and data processing were carried out in Python. Molecular topological features were generated using RDKit 2024.9.6. The crystal structures were obtained from the Cambridge Crystallographic Data Centre (CCDC), and subsequent first‑principles calculations were performed with the Cambridge Serial Total Energy Package (CASTEP) module in Materials Studio, employing the Perdew-Burke-Ernzerhof (PBE) functional within the generalized gradient approximation (GGA) with DFT‑D dispersion correction and norm‑conserving pseudopotentials. A plane‑wave cutoff energy of 830 eV was adopted, and geometry convergence criteria were set as follows: energy change < 5 × 10
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