Next-generation energetic materials: Innovations in detonation pressure prediction

The precise prediction of detonation pressure is paramount for understanding and optimizing the performance and safety of energetic materials. This critical parameter directly dictates blast intensity, shock wave propagation, and the resulting structural impacts, making its accurate estimation vital for both evaluating existing explosives and designing novel energetic compounds. This review critically examines the evolution of detonation pressure prediction methodologies, detailing their fundamental principles, data requirements, predictive accuracy, and practical constraints. We begin by exploring foundational empirical correlations that offer rapid estimations based on compositional and density data. Subsequently, we delve into contemporary statistical and machine-learning models that leverage molecular descriptors and material characteristics to enhance predictive power and facilitate the rapid screening of potential energetic materials. The review analyzes the merits and drawbacks of these diverse approaches, considering factors such as interpretability, computational demands, and reliability across various energetic material classes, including non-ideal and metalized formulations. Special emphasis is placed on the increasing significance of data-driven techniques in accelerating materials discovery and performance enhancement. The review concludes with forward-looking perspectives on research trajectories, advocating for the synergistic integration of physics-based principles with machine-learning strategies to expedite the development of safer and more effective energetic materials.