Computer-aided design and property prediction of novel insensitive high-energy heterocycle-substituted derivatives of cage NNNAHP

Khan, Raza Ullah; Zhu, Weihua

doi:10.1007/s00894-020-04513-2

Cited by 3 publications

(1 citation statement)

References 55 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Theorists often design completely new HEDMs based on their chemical intuition. [ 7,8 ] Moreover, modern algorithms search for materials with the desired properties by computational scanning of the chemical space and predict unexpected compounds with improved characteristics, [ 9,10 ] Computationally predicted HEDMs are very attractive because of their performance, stability, sensitivity, and environmental safety. For example, dioxatetrazinane is predicted to be a powerful carbon‐free HEDM, yielding only molecular nitrogen and water as the detonation products.…”

Section: Introductionmentioning

confidence: 99%

Probing of Neural Networks as a Bridge from Ab Initio Relevant Characteristics to Differential Scanning Calorimetry Measurements of High‐Energy Compounds

Bondarev

Katin²,

Merinov

et al. 2021

Physica Rapid Research Ltrs

View full text Add to dashboard Cite

The relationships between the theoretical values calculated using density functional theory and experimental data derived from the differential scanning calorimetry of high‐energy organic compounds are studied. The theoretical values are the number of atoms and bonds of different types and their lengths, minimum eigenfrequencies, atomization energies, ionization potentials, electron affinities, and frontier orbital energies. The experimental data are the amounts of releasing heat (the first peaks higher than 1 kJ g−1) and corresponding temperatures. Neural networks and regression, factor, discriminant, and cluster analysis are applied to find the dependencies between theoretical values and experimental data. It is found that the heat amount cannot be predicted in the general cases, whereas the corresponding temperature can be predicted with a neural network with an accuracy of ≈30 °C. Cluster and discriminant analysis provides the way for the classification of high‐energy compounds into three groups. Some of these groups require particular rules for the prediction of experimental data from the theoretical values.

show abstract

Section: Introductionmentioning

confidence: 99%