De novo multi-objective generation framework for energetic materials with trading off energy and stability

Energetic Materials (EMs) play important roles in military, civilian and aerospace fields. Energy and stability are the two most important but contradictory properties in practical application, thus leading to difficult challenges in developing new EMs with high comprehensive performance. Motivated by the challenge, we exploit a de novo design framework targeting multiple objectives by integrating deep learning generator, machine learning prediction models, Pareto front optimization and quantum mechanics (QM) validation. First, heat of explosion (Q) and bond dissociation energy (BDE) are calculated by high-precision QM for 778 explosives experimentally reported. With the reliable dataset, RNN coupled with transfer learning is exploited to generate a new massive search space with 2 × 105 potential energetic molecules. Q and BDE prediction models with high accuracy are further developed by data augmentation and improvements in feature representation and model architectures, to quickly and accurately evaluate these new energetic molecules. The modified 3D-GNN achieves an R2 = 0.95 for the Q prediction, while the XGBoost coupled with the feature complementarity and PADRE data augmentation performs best for the BDE prediction (R2 = 0.98). To screen energetic compounds with trade-off energy and stability from the vast new molecule space, the predicted values and uncertainties are simultaneously considered, and Pareto front-based multi-objective screening is conducted by using 2D P[I] metric. QM calculation confirms the superior performance of the top 60 candidates to CL-20 in Q. 25 promising energetic molecules with high energy and desired stability, as well as synthesis feasibility provide valuable candidates for experimental development. Also, the design strategy can be extended to other material fields.