A Simple Approach for Predicting the Density of High Nitrogen Organic Compounds as Materials for Providing Clean Products and Enormous Energy Release

Keshavarz, Mohammad Hossein; Ebadpour, Reza; Jafari, Mohammad

doi:10.22211/cejem/124210

Cited by 3 publications

(7 citation statements)

References 58 publications

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“…Additionally, it is interesting to compare the results of the presented approach with other prediction methods, which take into account exact chemical structures. For this purpose, we have chosen a few papers by Keshavarz et al, where the d c , , Δ H f , , D , and P values are predicted for a wide number of energetic materials. The results are presented graphically in Figure , and the corresponding statistical analysis data are listed in Table .…”

Section: Resultsmentioning

confidence: 99%

Magic of Numbers: A Guide for Preliminary Estimation of the Detonation Performance of C–H–N–O Explosives Based on Empirical Formulas

Bondarchuk

2021

Ind. Eng. Chem. Res.

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In this paper, we report a comprehensive analytical study of the factors influencing the detonation properties of C–H–N–O explosives. Besides the commonly applied parameters, namely, solid-state enthalpy of formation (ΔH f) and crystal density (d c), for which simple zeroth-order additive models based on the atomic increments are developed in this work, we also consider compositional factor being an intrinsic characteristic of each single empirical formula. Using a wide number of reference molecules (320 for ΔH f and 360 for d c), we have developed empirical equations, which provide rather good correlation coefficients R 2 = 0.90 and 0.80 for ΔH f and d c, respectively. Knowing these two equations and empirical formula, one can predict the detonation properties of a C–H–N–O explosive using a pocket calculator. Of course, such an approach, which completely neglects chemical structure, can be applied mainly for structurally similar compounds. However, having significant differences between the predicted detonation properties of two compositions, the account of their exact structures cannot reorder the predicted values. Thus, this paper can be used as a simple guide for molecular engineering and explosive structure enhancement. For this purpose, we provide a list of all compositions with the predicted properties up to C30H30N30O30 in the Supporting Information. To demonstrate how it works, we have applied the developed approach along with quantum-chemical calculations to model chemical structures outperforming ε-hexanitrohexaazaisowurtzitane (the most powerful explosive) in detonation performance.

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Section: Resultsmentioning

confidence: 99%

Magic of Numbers: A Guide for Preliminary Estimation of the Detonation Performance of C–H–N–O Explosives Based on Empirical Formulas

Bondarchuk

2021

Ind. Eng. Chem. Res.

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“…68 The processes of pre-DFT treatment, post-DFT treatment, and data collection are automatically performed by using a high-throughput software package named Energetic Molecular Design Engine developed by our team. 69 The test set is constructed by deliberately choosing the energetic compounds commonly studied, 9,10,18,20,21 including CL-20, RDX, HMX, TATB, and so forth, in consideration that using the most familiar compounds as the independent test set could more convectively express the effectiveness of the models. To ensure the independence of the test set, a Tanimoto similarity analysis is performed on the data set, and the statistical results for Tanimoto similarities between samples in the test sets and their nearest neighbors in the training/ validation set are shown in Figure 5.…”

Section: Journal Of Chemical Information and Modelingmentioning

confidence: 99%

“…46 However, we do not employ such information here because it is often not readily available in applications Categorical Accuracy. Most of the reported density prediction models for energetic compounds are trained based on data sets of specific categories, such as nitrate esters, 9 nitroarene, 10 nitramines, 20 high nitrogen compounds, 18 and ionic compounds, 21 and each model is only applicable to a limited group of compounds. Although the data sets used in the current work include almost all categories of nitro compounds collected from CCDC, whether the resulting models are universally applicable to all nitro compounds require accuracy evaluations in terms of a diverse category of compounds.…”

Section: Journal Of Chemical Information and Modelingmentioning

confidence: 99%

“…An accurate density prediction model is crucial to screen high-density compounds from numerous unlabeled compounds, which is helpful to develop potential excellent energetic compounds. It has been proved that the main problem lies in density prediction where the inter- and intramolecular interactions are not easy to be acquired, especially for an unknown crystal structure. − …”

Section: Introductionmentioning

confidence: 99%

“…Therefore, it is still technically difficult to predict the density of energetic compounds by using the CSP method . That is why most of the density prediction methods focus on establishing direct quantitative structure–property relationships (QSPRs) between the molecular structure and density. − …”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Density Prediction Models for Energetic Compounds Merely Using Molecular Topology

Yang

Chen

Wang

et al. 2021

J. Chem. Inf. Model.

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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.

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The prediction of crystal densities of a big data set using 1D and 2D structure features

Li,

Kong,

Luan

et al. 2024

Struct Chem

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A Simple Approach for Predicting the Density of High Nitrogen Organic Compounds as Materials for Providing Clean Products and Enormous Energy Release

Cited by 3 publications

References 58 publications

Magic of Numbers: A Guide for Preliminary Estimation of the Detonation Performance of C–H–N–O Explosives Based on Empirical Formulas

Magic of Numbers: A Guide for Preliminary Estimation of the Detonation Performance of C–H–N–O Explosives Based on Empirical Formulas

Density Prediction Models for Energetic Compounds Merely Using Molecular Topology

The prediction of crystal densities of a big data set using 1D and 2D structure features

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