High-throughput design offers a promising way to expedite the de novo design of novel energetic molecules, but achieving this goal necessitates accurate methods for property prediction and efficient schemes for molecular screening. Two approaches for generating energetic molecules are proposed, based on a generative model and a fragment docking scheme, respectively. A high-throughput computation (HTC) workflow based on quantum chemistry is developed for energetic molecule design. Machine learning models are established for predicting crystal density, enthalpy of formation, R–NO₂ bond dissociation energy, detonation velocity, detonation pressure, detonation heat, detonation volume and detonation temperature, yielding coefficients of determination (R²) of 0.928, 0.948, 0.984, 0.989, 0.986, 0.986, 0.990 and 0.995, respectively. Thereby, an easy-to-use platform named Energetic Materials Studio (EM-Studio) integrates all the methods and models. Therein, five modules, EM-Generator, EM-QC, EM-DB, EM-ML and EM-Visualizer, work for molecule generation, HTC-aided molecule design, data management, machine learning prediction, and human–computer interaction, respectively. The effectiveness and capabilities of EM-Studio in HTC- and AI-aided molecular design are demonstrated through two cases of fused-ring energetic molecules.

Hexanitrobenzene (HNB) and 1,3,5-triamino-2,4,6-trinitrobenzene (TATB) as two distinctive energetic nitrobenzene compounds are encoded by machine learning.

Energetic molecular crystals are widely applied for military and civilian purposes, and molecular forcefields (FF) are indispensable for treating the microscopic issues therein. This article reviews the three types of molecular FFs that are applied widely for describing energetic crystals—classic FFs, consistent FFs, and reactive FFs (ReaxFF). The basic principle of each type of FF is briefed and compared, with the application introduced, predicting polymorph, morphology, thermodynamics, vibration spectra, thermal property, mechanics, and reactivity. Finally, the advantages and disadvantages of these FFs are summarized, and some directions of future development are suggested.

Density prediction is of great significance for molecular design of energetic materials, since detonation velocity linearly with density and detonation pressure increases with the density squared. However, the accuracy and generalization of former reported prediction models need further improvement, because most of them are derived from small data sets and few molecular descriptors. As shown in this paper, for molecules presenting brick-like shape or containing more hydrogen-bond donors the predicted densities have large negative deviations from experimental values. Thus, a molecular morphology descriptor η and a hydrogen-bond descriptor H_b are introduced as correction items to build 3 new QSPR models. Besides, 3694 nitro compounds are adopted as data set by this work. The accuracies are obviously improved, and the generalizations are verified by an independent test set. At the level of B3PW91/6-31G(d,p), the effective ratios (ERs) of the 3 Equations, for Δρ < 5%, are 92.7%, 91.8%, and 93.3%; for Δρ < 2%, the values are 53.5%, 51.3%, and 54.7%. At the level of B3LYP/6-31G**, for Δρ < 5%, the values are 92.3%, 91.4% and 92.9%; for Δρ < 2%, the values are 53.7%, 51.4% and 53.2%.