The fundamental core of chemistry is to create new substances, and numerous complex reactions may be involved in chemical conversions. Nevertheless, clarifying the mechanisms of these complex reactions remains challenging, thereby causing insufficiencies in the fundamentals to guide new substance creation. This work proposes and emphasizes a strategy of sequential molecular dynamics simulations (SMDSs) toward complex chemical reactions. The strategy is successfully demonstrated by clarifying a complex graphitization process of 1,3,5-triamino-2,4,6-trinitrobenzene (TATB), whose mechanism has not been imaged by a single simulation alone. We conducted SMDSs with a molecular reactive force field, ReaxFF, to resemble the cook-off of TATB, i.e., a sequence of heating, expansion, and cooling acting on TATB. Graphitization is found to sequentially undergo TATB molecular decay, clustering, cluster enlargement to C sheets (sheeting), and layered stacking of C sheets, along with phase separation. Moreover, the structures graphitized from TATB can be imaged only when simulations are conducted in the sequence of heating, expansion, and cooling, in accordance with the actual conditions of cooking TATB. This successful exemplification shows that a large number of complex reaction mechanisms can be revealed using the SMDS strategy and computation ability promotion, in combination with the clarified experimental conditions. This strategy exhibits considerable potential for future use.

Newly developed high-throughput methods for property predictions make the process of materials design faster and more efficient. Density is an important physical property for energetic compounds to assess detonation velocity and detonation pressure, but the time cost of recent density prediction models is still high owing to the time-consuming processes to calculate molecular descriptors. To improve the screening efficiency of potential energetic compounds, new methods for density prediction with more accuracy and less time cost are urgently needed, and a possible solution is to establish direct mappings between the molecular structure and density. We propose three machine learning (ML) models, support vector machine (SVM), random forest (RF), and Graph neural network (GNN), using molecular topology as the only known input. The widely applied quantitative structure–property relationship based on the density functional theory (DFT–QSPR) is adopted as the benchmark to evaluate the accuracies of the models. All these four models are trained and tested by using the same data set enclosing over 2000 reported nitro compounds searched out from the Cambridge Structural Database. The proportions of compounds with prediction error less than 5% are evaluated by using the independent test set, and the values for the models of SVM, RF, DFT–QSPR, and GNN are 48, 63, 85, and 88%, respectively. The results show that, for the models of SVM and RF, fingerprint bit vectors alone are not facilitated to obtain good QSPRs. Mapping between the molecular structure and density can be well established by using GNN and molecular topology, and its accuracy is slightly better than that of the time-consuming DFT–QSPR method. The GNN-based model has higher accuracy and lower computational resource cost than the widely accepted DFT–QSPR model, so it is more suitable for high-throughput screening of energetic compounds.

The solid phase thermal decomposition and nanocrystal effect are extremely important to understand the ignition, combustion, reaction growth and buildup to detonation under shock wave action.

Multiple types of external stimuli are usually loaded on an energetic material (EM) simultaneously, and thus they have a coupled effect on its decay. Meanwhile, the structures of the EM essentially influence the decay. Thereby, the coupling effects of these stimuli and structures should be considered in assessing the decay and further the safety of EMs. Nevertheless, it is still difficult to clarify the atomistic/molecular details of the coupling effects on the decay and safety mechanisms. In the present work, we perform reactive molecular dynamics simulations in combination with the multiscale shock technique to reveal a shock–preheating–dislocation coupling effect on the decay mechanism of an energetic representative, 1,3,5-trinitro-1,3,5-triazinane (RDX). That is, three factors including shock velocity, preheating temperature, and edge dislocation are accounted as variables for the simulations. Increasing shock velocity and preheating temperature and presenting edge dislocation in crystal both promote the RDX decay. Preheating enhances the shock sensitivity as ascertained experimentally, and the sensitivity enhancement is caused by the elevated potential energy of the RDX molecules because of preheating. Moreover, interestingly, two different shock–preheating–dislocation couplings can possess an equivalent effect on the RDX decay, as they can lead to almost same evolutions of major chemical species, temperature, pressure, and potential energy. All these findings are expected to deepen the insight into the response mechanisms for the EMs against external stimuli, particularly in the case of multiple factors coupled.

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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 Hb 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%.

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Energetic materials (EMs) are a group of special energy materials, and it is generally full of safety risk and generally costs much to create new EMs. Thus, the machine learning...