Accelerating Molecular Design of Cage Energetic Materials with Zero Oxygen Balance through Large-Scale Database Search

Design and computational screening of high-energy, low-sensitivity bistetrazole-based energetic molecules

Bistetrazole-based compounds are novel high-nitrogen energetic molecules that have garnered attention in recent years. They possess a higher energy density and lower sensitivity, and are less challenging to synthesize than complex cage-like molecules. This study employed a molecular auto-generation mechanism to generate 35 322 bistetrazole-based molecules with 20 bridgeheads and 29 side substituents. A combination of quantum chemical calculations and machine learning models was used to sequentially screen the molecules based on their oxygen balance index, synthesis difficulty, density, and detonation pressure, thus rapidly narrowing the search scope. Three bistetrazole-based energetic molecules with high potential were identified. The theoretical enthalpy of the formation of the designed molecules was as high as 854.76 kJ mol-1 and their detonation velocity reached 9.58 km s-1. Further calculations also demonstrated that these molecules have better macroscopic stability than trinitrotoluene, making them promising candidates for practical applications in developing energetic materials.

High precision deep-learning model combined with high-throughput screening to discover fused [5,5] biheterocyclic energetic materials with excellent comprehensive properties

Finding novel energetic materials with good comprehensive performance has always been challenging because of the low efficiency in conventional trial and error experimental procedure. In this paper, we established a deep learning model with high prediction accuracy using embedded features in Directed Message Passing Neural Networks. The model combined with high-throughput screening was shown to facilitate rapid discovery of fused [5,5] biheterocyclic energetic materials with high energy and excellent thermal stability. Density Functional Theory (DFT) calculations proved that the performances of the targeting molecules are consistent with the predicted results from the deep learning model. Furthermore, 6,7-trinitro-3H-pyrrolo[1,2-b][1,2,4]triazo-5-amine with both good detonation properties and thermal stability was screened out, whose crystal structure and intermolecular interactions were also analyzed.

Accelerating the Design of High-Energy-Density Hydrocarbon Fuels by Learning from the Data

In the ZINC20 database, with the aid of maximum substructure searches, common substructures were obtained from molecules with high-strain-energy and combustion heat values, and further provided domain knowledge on how to design high-energy-density hydrocarbon (HEDH) fuels. Notably, quadricyclane and syntin could be topologically assembled through these substructures, and the corresponding assembled schemes guided the design of 20 fuel molecules (ZD-1 to ZD-20). The fuel properties of the molecules were evaluated by using group-contribution methods and density functional theory (DFT) calculations, where ZD-6 stood out due to the high volumetric net heat of combustion, high specific impulse, low melting point, and acceptable flash point. Based on the neural network model for evaluating the synthetic complexity (SCScore), the estimated value of ZD-6 was close to that of syntin, indicating that the synthetic complexity of ZD-6 was comparable to that of syntin. This work not only provides ZD-6 as a potential HEDH fuel, but also illustrates the superiority of learning design strategies from the data in increasing the understanding of structure and performance relationships and accelerating the development of novel HEDH fuels.

Force field-inspired transformer network assisted crystal density prediction for energetic materials

Machine learning has great potential in predicting chemical information with greater precision than traditional methods. Graph neural networks (GNNs) have become increasingly popular in recent years, as they can automatically learn the features of the molecule from the graph, significantly reducing the time needed to find and build molecular descriptors. However, the application of machine learning to energetic materials property prediction is still in the initial stage due to insufficient data. In this work, we first curated a dataset of 12,072 compounds containing CHON elements, which are traditionally regarded as main composition elements of energetic materials, from the Cambridge Structural Database, then we implemented a refinement to our force field-inspired neural network (FFiNet), through the adoption of a Transformer encoder, resulting in force field-inspired Transformer network (FFiTrNet). After the improvement, our model outperforms other machine learning-based and GNNs-based models and shows its powerful predictive capabilities especially for high-density materials. Our model also shows its capability in predicting the crystal density of potential energetic materials dataset (i.e. Huang & Massa dataset), which will be helpful in practical high-throughput screening of energetic materials.

Approaches to 1,4-Disubstituted Cubane Derivatives as Energetic Materials: Design, Theoretical Studies and Synthesis

Novel 1,4-disubstituted cubane derivatives have been designed and selected ones have been successfully synthesized and characterized by various analytical and spectroscopic techniques, including single-crystal X-ray analysis. A detailed computational study at B3LYP/6-311++G(d,p) level of theory revealed that all newly designed 1,4-disubstituted cubane derivatives possess higher densities, higher density-specific impulse and superior ballistic properties when compared to conventional fuels, for example, RP-1. These compounds also exhibit acceptable kinetic and thermodynamic stabilities which were evaluated in terms of their HOMO-LUMO energy gap and bond dissociation energies, respectively, and are superior to TEX and many other compounds containing explosophoric groups. These results provide novel insights into the possible application of cubane-based energetic materials.

Alleviating the stability–performance contradiction of cage-like high-energy-density materials by a backbone-collapse and branch-heterolysis competition mechanism

Key role of cage-like conformations in alleviating the stability–performance contradiction of HEDMs.