Summary The long-standing performance-stability contradiction issue of high energy density materials (HEDMs) is of extremely complex and multi-parameter nature. Herein, machine learning was employed to handle 28 feature descriptors and 5 properties of detonation and stability of 153 HEDMs, wherein all 21,648 data used were obtained through high-throughput crystal-level quantum mechanics calculations on supercomputers. Among five models, namely, extreme gradient boosting regression tree (XGBoost), adaptive boosting, random forest, multi-layer perceptron, and kernel ridge regression, were respectively trained and evaluated by stratified sampling and 5-fold cross-validation method. Among them, XGBoost model produced the best scoring metrics in predicting the detonation velocity, detonation pressure, heat of explosion, decomposition temperature, and lattice energy of HEDMs, and XGBoost predictions agreed best with the 1,383 experimental data collected from massive literatures. Feature importance analysis was conducted to obtain data-driven insight into the causality of the performance-stability contradiction and delivered the optimal range of key features for more efficient rational design of advanced HEDMs.

Two new types of aluminized explosives TATB/ HMX/Al and LLM-105/Al were formulated and compared with the formerly reported TATB/Al explosives in terms of thermal stability, mechanical sensitivity, and detonation performance. Firstly, the heat of the explosion was measured and two formulations were selected as the investigated samples. Next the thermal stability was studied by simultaneous thermogravimetric analysis/differential scanning calorimetry (TGA/DSC) and thermal cook-off tests. Then the impact and friction sensitivity were measured, and finally the detonation performance was characterized by cylinder tests and particle velocity measurements of the detonation reaction zone. From the calorimetric data, the new types of explosives increase the heat of the explosion significantly. In terms of thermal stability, LLM-105/Al = 65/ 30 is more stable than TATB/HMX/Al = 50/15/30, but both of them are inferior to TATB/Al = 70/25. The impact sensitivity of the two new explosives is higher than that of TATB/Al = 70/25, and all of the samples are insensitive to friction. For detonation performance, both of the two new samples are superior to TATB/Al = 70/25, and LLM-105/Al = 65/30 exhibits the best performance that it has the highest Gurney energy, detonation velocity, and detonation pressure. Conclusions about how to use those aluminized explosives to generate optimal effects are drawn.

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