Investigating 3,4-bis(3-nitrofurazan-4-yl)furoxan detonation with a rapidly tuned density functional tight binding model

Infrared spectra and electronic structural changes of DNTF under high pressure: experimental and theoretical studies

3,4-Bis(3-nitrofurazan-4-yl)furoxan (DNTF) is a novel energetic material with an excellent performance and has attracted considerable attention. Motivated by recent theories and experiments, we had carried out experimental and theoretical studies on the high-pressure responses of vibrational characteristics, in conjunction with structural and electronic characteristics. It is found that all observed infrared spectra peaks seem to shift towards higher frequencies. And the peaks attributed to N-Oc (coordinated oxygen atom) stretching vibrations become broader due to the decrease of lattice constants and the free region of DNTF crystals with the increase of pressure, where the a-direction is more sensitive to pressure. In addition, the non-covalent interaction between adjacent DNTF molecules in the same layer changes from the van der Waals interaction to the steric effect with the increase of pressure, and that between layers also changes from the van der Waals interaction to the π-π stacking interaction. More importantly, these results highlight that the increase of pressure may lead to the stability decrease and impact the sensitivity increase of DNTF. This study can deepen the understanding of the energetic material DNTF under high pressure and is of great significance for blasting and detonation applications of DNTF.

Chemical evolution in nitrogen shocked beyond the molecular stability limit

Evolution of nitrogen under shock compression up to 100 GPa is revisited via molecular dynamics simulations using a machine-learned interatomic potential. The model is shown to be capable of recovering the structure, dynamics, speciation, and kinetics in hot compressed liquid nitrogen predicted by first-principles molecular dynamics, as well as the measured principal shock Hugoniot and double shock experimental data, albeit without shock cooling. Our results indicate that a purely molecular dissociation description of nitrogen chemistry under shock compression provides an incomplete picture and that short oligomers form in non-negligible quantities. This suggests that classical models representing the shock dissociation of nitrogen as a transition to an atomic fluid need to be revised to include reversible polymerization effects.

Enhancing the accuracy of density functional tight binding models through ChIMES many-body interaction potentials

Semi-empirical quantum models such as Density Functional Tight Binding (DFTB) are attractive methods for obtaining quantum simulation data at longer time and length scales than possible with standard approaches. However, application of these models can require lengthy effort due to the lack of a systematic approach for their development. In this work, we discuss the use of the Chebyshev Interaction Model for Efficient Simulation (ChIMES) to create rapidly parameterized DFTB models, which exhibit strong transferability due to the inclusion of many-body interactions that might otherwise be inaccurate. We apply our modeling approach to silicon polymorphs and review previous work on titanium hydride. We also review the creation of a general purpose DFTB/ChIMES model for organic molecules and compounds that approaches hybrid functional and coupled cluster accuracy with two orders of magnitude fewer parameters than similar neural network approaches. In all cases, DFTB/ChIMES yields similar accuracy to the underlying quantum method with orders of magnitude improvement in computational cost. Our developments provide a way to create computationally efficient and highly accurate simulations over varying extreme thermodynamic conditions, where physical and chemical properties can be difficult to interrogate directly, and there is historically a significant reliance on theoretical approaches for interpretation and validation of experimental results.

High-Accuracy Semiempirical Quantum Models Based on a Minimal Training Set

A great need exists for computationally efficient quantum simulation approaches that can achieve an accuracy similar to high-level theories at a fraction of the computational cost. In this regard, we have leveraged a machine-learned interaction potential based on Chebyshev polynomials to improve density functional tight binding (DFTB) models for organic materials. The benefit of our approach is two-fold: (1) many-body interactions can be corrected for in a systematic and rapidly tunable process, and (2) high-level quantum accuracy for a broad range of compounds can be achieved with ∼0.3% of data required for one advanced deep learning potential. Our model exhibits both transferability and extensibility through comparison to quantum chemical results for organic clusters, solid carbon phases, and molecular crystal phase stability rankings. Our efforts thus allow for high-throughput physical and chemical predictions with up to coupled-cluster accuracy for systems that are computationally intractable with standard approaches.

Chemistry-mediated Ostwald ripening in carbon-rich C/O systems at extreme conditions

There is significant interest in establishing a capability for tailored synthesis of next-generation carbon-based nanomaterials due to their broad range of applications and high degree of tunability. High pressure (e.g., shockwave-driven) synthesis holds promise as an effective discovery method, but experimental challenges preclude elucidating the processes governing nanocarbon production from carbon-rich precursors that could otherwise guide efforts through the prohibitively expansive design space. Here we report findings from large scale atomistically-resolved simulations of carbon condensation from C/O mixtures subjected to extreme pressures and temperatures, made possible by machine-learned reactive interatomic potentials. We find that liquid nanocarbon formation follows classical growth kinetics driven by Ostwald ripening (i.e., growth of large clusters at the expense of shrinking small ones) and obeys dynamical scaling in a process mediated by carbon chemistry in the surrounding reactive fluid. The results provide direct insight into carbon condensation in a representative system and pave the way for its exploration in higher complexity organic materials. They also suggest that simulations using machine-learned interatomic potentials could eventually be employed as in-silico design tools for new nanomaterials.

Modelling the growth of carbon nanoclusters in shock experiments is computationally demanding. Here the authors employ a machine-learned reactive interatomic model to perform large-scale simulations of nanocarbon formation from prototypical shocked C/O-containing precursor.

Molecular Dynamics Simulations of the Thermal Decomposition of 3,4-Bis(3-nitrofurazan-4-yl)furoxan

When stimulated, for example, by a high temperature, the physical and chemical properties of energetic materials (EMs) may change, and, in turn, their overall performance is affected. Therefore, thermal stability is crucial for EMs, especially the thermal dynamic behavior. In the past decade, significant efforts have been made to study the thermal dynamic behavior of 3,4-bis(3-nitrofurazan-4-yl)furoxan (DNTF), one of the new high-energy-density materials (HEDMs). However, the thermal decomposition mechanism of DNTF is still not specific or comprehensive. In this study, the self-consistent-charge density-functional tight-binding method was combined with molecular dynamics (MD) simulations to reveal the differences in the thermal decomposition of DNTF under four heating conditions. The O-N (O) bond would fracture first during DNTF initial thermal decomposition at medium and low temperatures, thus triggering the cracking of the whole structure. At 2000 and 2500 K, NO₂ loss on outer ring I is the fastest initial thermal decomposition pathway, and it determines that the decomposition mechanism is different from that of a medium-low temperature. NO₂ is found to be the most active intermediate product; large molecular fragments, such as C₂N₂O, are found for the first time. Hopefully, these results could provide some insights into the decomposition mechanism of new HEDMs.

Kinetics of carbon condensation in detonation of high explosives: First-order phase transition theory perspective

The kinetics of carbon condensation, or carbon clustering, in detonation of carbon-rich high explosives is modeled by solving a system of rate equations for concentrations of carbon particles. Unlike previous efforts, the rate equations account not only for the aggregation of particles but also for their fragmentation in a thermodynamically consistent manner. Numerical simulations are performed, yielding the distribution of particle concentrations as a function of time. In addition to that, analytical expressions are obtained for all the distinct steps and regimes of the condensation kinetics, which facilitates the analysis of the numerical results and allows one to study the sensitivity of the kinetic behavior to the variation of system parameters. The latter is important because the numerical values of many parameters are not reliably known at present. The theory of the kinetics of first-order phase transitions is found adequate to describe the general kinetic trends of carbon condensation, as described by the rate equations. Such physical phenomena and processes as the coagulation, nucleation, growth, and Ostwald ripening are observed, and their dependence on various system parameters is studied and reported. It is believed that the present work will become useful when analyzing the present and future results for the kinetics of carbon condensation, obtained from experiments or atomistic simulations.