The fundamental core of chemistry
is to create new substances,
and numerous complex reactions may be involved in chemical conversions.
Nevertheless, clarifying the mechanisms of these complex reactions
remains challenging, thereby causing insufficiencies in the fundamentals
to guide new substance creation. This work proposes and emphasizes
a strategy of sequential molecular dynamics simulations (SMDSs) toward
complex chemical reactions. The strategy is successfully demonstrated
by clarifying a complex graphitization process of 1,3,5-triamino-2,4,6-trinitrobenzene
(TATB), whose mechanism has not been imaged by a single simulation
alone. We conducted SMDSs with a molecular reactive force field, ReaxFF,
to resemble the cook-off of TATB, i.e., a sequence of heating, expansion,
and cooling acting on TATB. Graphitization is found to sequentially
undergo TATB molecular decay, clustering, cluster enlargement to C
sheets (sheeting), and layered stacking of C sheets, along with phase
separation. Moreover, the structures graphitized from TATB can be
imaged only when simulations are conducted in the sequence of heating,
expansion, and cooling, in accordance with the actual conditions of
cooking TATB. This successful exemplification shows that a large number
of complex reaction mechanisms can be revealed using the SMDS strategy
and computation ability promotion, in combination with the clarified
experimental conditions. This strategy exhibits considerable potential
for future use.
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.
The solid phase thermal decomposition and nanocrystal effect are extremely important to understand the ignition, combustion, reaction growth and buildup to detonation under shock wave action.
Multiple types of external stimuli
are usually loaded on an energetic
material (EM) simultaneously, and thus they have a coupled effect
on its decay. Meanwhile, the structures of the EM essentially influence
the decay. Thereby, the coupling effects of these stimuli and structures
should be considered in assessing the decay and further the safety
of EMs. Nevertheless, it is still difficult to clarify the atomistic/molecular
details of the coupling effects on the decay and safety mechanisms.
In the present work, we perform reactive molecular dynamics simulations
in combination with the multiscale shock technique to reveal a shock–preheating–dislocation
coupling effect on the decay mechanism of an energetic representative,
1,3,5-trinitro-1,3,5-triazinane (RDX). That is, three factors including
shock velocity, preheating temperature, and edge dislocation are accounted
as variables for the simulations. Increasing shock velocity and preheating
temperature and presenting edge dislocation in crystal both promote
the RDX decay. Preheating enhances the shock sensitivity as ascertained
experimentally, and the sensitivity enhancement is caused by the elevated
potential energy of the RDX molecules because of preheating. Moreover,
interestingly, two different shock–preheating–dislocation
couplings can possess an equivalent effect on the RDX decay, as they
can lead to almost same evolutions of major chemical species, temperature,
pressure, and potential energy. All these findings are expected to
deepen the insight into the response mechanisms for the EMs against
external stimuli, particularly in the case of multiple factors coupled.
Density prediction is of great significance for molecular design of energetic materials, since detonation velocity linearly with density and detonation pressure increases with the density squared. However, the accuracy and generalization of former reported prediction models need further improvement, because most of them are derived from small data sets and few molecular descriptors. As shown in this paper, for molecules presenting brick-like shape or containing more hydrogen-bond donors the predicted densities have large negative deviations from experimental values. Thus, a molecular morphology descriptor η and a hydrogen-bond descriptor Hb are introduced as correction items to build 3 new QSPR models. Besides, 3694 nitro compounds are adopted as data set by this work. The accuracies are obviously improved, and the generalizations are verified by an independent test set. At the level of B3PW91/6-31G(d,p), the effective ratios (ERs) of the 3 Equations, for Δρ < 5%, are 92.7%, 91.8%, and 93.3%; for Δρ < 2%, the values are 53.5%, 51.3%, and 54.7%. At the level of B3LYP/6-31G**, for Δρ < 5%, the values are 92.3%, 91.4% and 92.9%; for Δρ < 2%, the values are 53.7%, 51.4% and 53.2%.
Energetic materials (EMs) are a group of special energy materials, and it is generally full of safety risk and generally costs much to create new EMs. Thus, the machine learning...
The matrix assembly cluster source (MACS) represents a bridge between conventional instruments for cluster beam deposition (CBD) and the level of industrial production. The method is based on Ar + ion sputtering of a pre-condensed ArM matrix (where M, is typically a metal such as Ag). Each Ar + ion produces a collision cascade and thus the formation of metal clusters is in the matrix, which are then sputtered out. Here we present an experimental and computational investigation of the cluster emission process, specifically its dependence on the Ar + ion angle of incidence and the cluster emission angle. We find the incidence angle strongly influences the emerging cluster flux, which is assigned to the spatial location of the deposited primary ion energy relative to the cluster into the matrix. We also found an approximately constant angle between the incident ion beam and the peak in the emitted cluster distribution, with value between 99° and 109°.
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