Sensitivity of Energetic Materials: Theoretical Relationships to Detonation Performance and Molecular Structure

Mathieu, Didier

doi:10.1021/acs.iecr.7b02021

Cited by 102 publications

(96 citation statements)

References 72 publications

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“…While we were able to obtain good learning curves from just the Huang & Massa dataset, to ensure their accuracy we supplemented the Huang & Massa data with 309 additional molecules from the dataset given in the supplementary information of Mathieu et al, which includes detonation velocity and detonation pressure values calculated from the Kamlet-Jacobs equations. 37 A few of the molecules are found in both datasets, but most are unique, yielding a total of ≈ 400 unique molecules. We assume detonation velocity is equivalent to shock velocity in Huang & Massa data.…”

Section: Learning Curvesmentioning

confidence: 99%

Applying machine learning techniques to predict the properties of energetic materials

Elton

Boukouvalas

Butrico

et al. 2018

Sci Rep

196

169

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We present a proof of concept that machine learning techniques can be used to predict the properties of CNOHF energetic molecules from their molecular structures. We focus on a small but diverse dataset consisting of 109 molecular structures spread across ten compound classes. Up until now, candidate molecules for energetic materials have been screened using predictions from expensive quantum simulations and thermochemical codes. We present a comprehensive comparison of machine learning models and several molecular featurization methods - sum over bonds, custom descriptors, Coulomb matrices, Bag of Bonds, and fingerprints. The best featurization was sum over bonds (bond counting), and the best model was kernel ridge regression. Despite having a small data set, we obtain acceptable errors and Pearson correlations for the prediction of detonation pressure, detonation velocity, explosive energy, heat of formation, density, and other properties out of sample. By including another dataset with ≈300 additional molecules in our training we show how the error can be pushed lower, although the convergence with number of molecules is slow. Our work paves the way for future applications of machine learning in this domain, including automated lead generation and interpreting machine learning models to obtain novel chemical insights.

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Section: Learning Curvesmentioning

confidence: 99%

Applying machine learning techniques to predict the properties of energetic materials

Elton

Boukouvalas

Butrico

et al. 2018

Sci Rep

196

169

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“…Attempts to correlate impact sensitivity of explosives especially within a certain class (eg, nitromaromatics) with molecular properties have been pursued for different groups. [16][17][18][19][20][21][22] In spite of some skepticism, results of these efforts often have been successful. [23] Given that development of new energetic materials is lengthy and expensive because it relies on experimentation, the possibility of using these correlations to eliminate at early stages of development a poor candidate due to sensitivity issues, for instance, is highly desirable.…”

Section: Introductionmentioning

confidence: 99%

“…[41][42][43][44][45] Regarding structural factors of the molecules, Mathieu observed that there is a relationship between the sensitivity of nitramines and their energy of dissociation and molecular energy per atom. [16] Politzer and Murray also established quantitative relationships between their sensitivities and some characteristics of the molecular surface electrostatic potentials that reflect the dominant positive central regions, a distinct property of an energetic molecule in contrast with a typical organic molecule. [25,29,46,47] Another approach investigated the relationship between impact sensitivities of nitramines and energy transfer rates.…”

Section: Introductionmentioning

confidence: 99%

On the molecular origin of the sensitivity to impact of cyclic nitramines

Oliveira

Borges

2018

Int J of Quantum Chemistry

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Nitramine explosives can combine relative insensitivity to initiation and great energy content. In this work, based on a previous approach developed for nitroaromatic explosives, we propose four mathematical models to correlate impact sensitivity, given by the h50 value, to molecular charge properties. Fourteen cyclic nitramines were studied using Density Functional Theory (DFT). Six molecules of the set have measured h50 values, which were used to evaluate the sensitivity models. Converged DFT charge densities of the molecules were partitioned and analyzed according to the distributed multipole analysis (DMA) atom‐centered method. The sensitivity models were based on the DMA electric multipole values. The electron withdrawing role of the nitro group and the strong polarization of the charges of the nitrogen atom in the amine group were clearly identified. The influence of the electronic properties on the sensitivity of the explosives was characterized by including in the sensitivity models the charge values of the nitro or the nitramine groups and electron delocalization, the latter quantified by the DMA quadrupole values of the ring atoms. Inclusion of electron delocalization effects can improve the prediction of h50 values for two out of the five strained‐ring nitramines in the set. The charge values of the nitramine groups are the most important molecular property affecting the impact sensitivity. The h50 values of eight nitramine explosives of the set not available experimentally were computed.

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“…In one earlier work [91], they chose five featurizations: custom descriptor set (CDS, a vector including 21 customized parameters, like raw counts of carbon and nitrogen), sum over bond (SoB, a vector that contains how many different bonds are presented), Coulomb matrix (CM, coordinates and nuclear charges were transformed to the Coulomb matrix eigenvalue spectra representation, which is invariant of the transformation and rotation of the molecular structure), bag of bonds (BoB, a bag containing the number of occurrence of different bonds), and fingerprinting (it transfers molecular graphs into vector form).The kernel ridge regression (KRR), ridge regression (RR), support vector regression (SVR), random forest (RF), and k-nearest neighbors (KNN) were selected as the ML models. In a later work [94], they also adopted LASSO regression, Gaussian process regression (GPR), and neural network (NN) to develop prediction models using the same dataset, plus additional 309 molecules from another reference [96].…”

Section: Design Of High Energetic Materialsmentioning

confidence: 99%

Machine-Learning-Assisted De Novo Design of Organic Molecules and Polymers: Opportunities and Challenges

et al. 2020

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Organic molecules and polymers have a broad range of applications in biomedical, chemical, and materials science fields. Traditional design approaches for organic molecules and polymers are mainly experimentally-driven, guided by experience, intuition, and conceptual insights. Though they have been successfully applied to discover many important materials, these methods are facing significant challenges due to the tremendous demand of new materials and vast design space of organic molecules and polymers. Accelerated and inverse materials design is an ideal solution to these challenges. With advancements in high-throughput computation, artificial intelligence (especially machining learning, ML), and the growth of materials databases, ML-assisted materials design is emerging as a promising tool to flourish breakthroughs in many areas of materials science and engineering. To date, using ML-assisted approaches, the quantitative structure property/activity relation for material property prediction can be established more accurately and efficiently. In addition, materials design can be revolutionized and accelerated much faster than ever, through ML-enabled molecular generation and inverse molecular design. In this perspective, we review the recent progresses in ML-guided design of organic molecules and polymers, highlight several successful examples, and examine future opportunities in biomedical, chemical, and materials science fields. We further discuss the relevant challenges to solve in order to fully realize the potential of ML-assisted materials design for organic molecules and polymers. In particular, this study summarizes publicly available materials databases, feature representations for organic molecules, open-source tools for feature generation, methods for molecular generation, and ML models for prediction of material properties, which serve as a tutorial for researchers who have little experience with ML before and want to apply ML for various applications. Last but not least, it draws insights into the current limitations of ML-guided design of organic molecules and polymers. We anticipate that ML-assisted materials design for organic molecules and polymers will be the driving force in the near future, to meet the tremendous demand of new materials with tailored properties in different fields.

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Sensitivity of Energetic Materials: Theoretical Relationships to Detonation Performance and Molecular Structure

Cited by 102 publications

References 72 publications

Applying machine learning techniques to predict the properties of energetic materials

Applying machine learning techniques to predict the properties of energetic materials

On the molecular origin of the sensitivity to impact of cyclic nitramines

Machine-Learning-Assisted De Novo Design of Organic Molecules and Polymers: Opportunities and Challenges

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