QSPR modeling of detonation parameters and sensitivity of some energetic materials: DFT vs. PM3 calculations

Design and exploration of 5-nitro-3-trinitromethyl-1H-1,2,4-triazole and its derivatives as energetic materials

According to the fact that 5-nitro-3-trinitromethyl-1H-1,2,4 triazole (NTNMT) is a successful, good explosive, energetic groups such as -CH3, -NH2, -NHNO2, -NO2, -ONO2, -NF2, -CN, -NC, -N3 groups were introduced into NTNMT and their oxygen balance was at about zero. The energetic properties, detonation performance, and sensitivity were studied at the B3LYP/6-31G** level of density functional theory to seek for possible high energy density compounds. The effects of substituent groups on heat of formation (HOF), density ρ, detonation velocity D, detonation pressure P, detonation energy Q, and sensitivity (evaluated using oxygen balance OB, the nitro group charges -QNO2, and bond dissociation energies BDE were studied and discussed. The order of contribution of the substituent groups to ρ, D, and P was -NF2 > -ONO2 > -NO2 > -NHNO2 > -N3 > -NH2 > -NC > -CN > -CH3; while to HOF is -N3 > -NC > -CN > -NO2 > -NF2 > -ONO2 > -NH2 > -NHNO2 > -CH3. The trigger bonds in the pyrolysis process for NTNMT derivatives may be N-NO2, N-NH2, N-NHNO2, C-NO2, or O-NO2 varying with the attachment of different substituents. Results show that NTNMT-NHNO2, -NH2, -CN, and -NC derivatives have high detonation performance and good stability. In a word, the oxygen balance at about zero strategy in this work offers new routes for the improvement in properties and stabilities of energetic materials. In the present paper, several 5-nitro-3-trinitromethyl-1H-1,2,4 triazole (NTNMT) derivatives were designed. Their energetic properties, detonation performance, and sensitivity were studied at the B3LYP/6-31G** level of density functional theory (DFT) to seek for possible high energy density compounds (HEDCs). The different substituents have some changes in the influence on heat of formation (HOF), density ρ, detonation velocity D, detonation pressure P, detonation energy Q, and sensitivity. In a word, the oxygen balance at about zero strategy in this work offers new routes for the improvement in properties and stabilities of energetic materials.

Density Prediction Models for Energetic Compounds Merely Using Molecular Topology

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.

Applying machine learning to balance performance and stability of high energy density materials

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.

Machine-Learning Assisted Screening of Energetic Materials

In this work, machine learning (ML), materials informatics (MI), and thermochemical data are combined to screen potential candidates of energetic materials. To directly characterize energetic performance, the heat of explosion ΔH_e is used as the target property. The critical descriptors of cohesive energy, averaged over all constituent elements and the oxygen balance, are found by forward stepwise selection from a large number of possible descriptors. With them and a theoretically labeled ΔH_e training data set, a satisfactory surrogate ML model is trained. The ML model is applied to large databases ICSD and PubChem to predict ΔH_e. At the gross-level filtering by the ML model, 2732 molecular candidates based on carbon, hydrogen, nitrogen, and oxygen (CHNO) with high ΔH_e values are predicted. Afterward, a fine-level thermochemical screening is carried out on the 2732 materials, resulting in 262 candidates with TNT equivalent power index P_e(TNT) greater than 1.5. Raising P_e(TNT) further to larger than 1.8, 29 potential candidates are found from the 2732 materials, all are new to the current reservoir of well-known energetic materials.