Active Learning for Generalizable Detonation Performance Prediction of Energetic Materials

ArXi:2604.08744v1 Announce Type: cross The discovery of new energetic materials is critical for advancing technologies from defense to private industry. However, experimental approaches remain slow and expensive while computational alternatives require accurate material property inputs that are often costly to obtain, limiting their ability to efficiently predict detonation performance across a vast chemical space.