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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 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 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., Comment: 42 pages, 8 figures, 6 tables. In review for a special issue in J. Chem. Phys

Carbon nanoallotropes are important nanomaterials with unusual properties and promising applications. High pressure synthesis has the potential to open new avenues for controlling and designing their physical and chemical characteristics for a broad range of uses but it remains little understood due to persistent conceptual and experimental challenges, in addition to fundamental physics and chemistry questions that are still unresolved after many decades. Here we demonstrate sub-nanosecond nanocarbon synthesis through the application of laser-induced shock-waves to a prototypical organic carbon-rich liquid precursor—liquid carbon monoxide. Overlapping large-scale molecular dynamics simulations capture the atomistic details of the nanoparticles’ formation and evolution in a reactive environment and identify classical evaporation-condensation as the mechanism governing their growth on these time scales., Carbon nanomaterials have widespread application but fundamental aspects of their formation are still unclear. Here the authors explore the shock-induced synthesis of carbon nanoallotropes from liquid CO by time-resolved reflectivity and computations identifying the growth mechanism at the sub-nanosecond timescale

There exists a great need for computationally efficient quantum simulation approaches that can achieve an accuracy similar to high-level theories while exhibiting a wide degree of transferability. In this regard, we have leveraged a machine-learned force field based on Chebyshev polynomials to determine 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 (ANI- 1x). In addition, the total number of data points in our training set is less than one half of that used for a recent DFTB-neural network model (trained on a separate dataset). Validation tests of our DFTB model against energy and vibrational data for gas-phase molecules for additional quantum datasets shows strong agreement with reference data from either hybrid density-functional theory, coupled-cluster calculations, or experiments. Preliminary testing on graphite and diamond successfully reproduce condensed phase structures. The models developed in this work, in principle, can retain most of the accuracy of quantum-based methods at any level of theory with relatively small training sets. Our efforts can thus allow for high throughput physical and chemical predictions with up to coupled-cluster accuracy for materials that are computationally intractable with standard approaches.

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.

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.

Thermochemical models of detonation are widely used to estimate energy delivery, but they are based on the assumption that the carbon-rich condensates (soot) formed during detonation are very similar to bulk carbon. We present an analytic equation of state (EOS) based on experimental detonation data for soot formed during the detonation of triaminotrinitrobenzene (TATB)-based high explosives. X-ray photoelectron spectra of several detonation soots are used to determine the elemental nitrogen abundance, with surprisingly high values for TATB. The proposed TATB soot EOS is highly compressible at low pressures and shares some features of glassy carbon, exhibiting graphite- and diamond-like behavior as a function of pressure. We demonstrate the influence of formed soot on detonation performance, including a lowering of the detonation velocity at typical charge densities, and a more compressive product Hugoniot at overdriven conditions. The soot model improves the accuracy of thermochemical calculations for TATB-based explosives across a wide range of states. Detonation velocity predictions for HMX (cyclotetramethylene-tetranitramine)-TATB blends with 80% or more TATB content, as well predictions for 1,3-diamino-2,4,6-trinitrobenzene (DATB) and 3-nitro-1,2,4-triazol-5-one (NTO), which share some features with TATB, are also improved.

Detonation nanodiamond (DND) is known to form aggregates that significantly reduce their unique nanoscale properties and require postprocessing to separate. How and when DND aggregates is an important question that has not been answered experimentally and could provide the foundation for approaches to limit aggregation. To answer this question, time-resolved small-angle X-ray scattering was performed during the detonation of high-explosives that are expected to condense particulates in the diamond, graphite, and liquid regions of the carbon phase diagram. DND aggregation into low fractal dimension structures could be observed as early as 0.1 μs, along with a separate scattering population also observed from an explosive that produces primarily graphitic products. A counterexample is the case of a high-explosive that produces nano-onions, where no hierarchical scattering was observed for at least 10 μs behind the detonation front. These results suggest that DND aggregation occurs on time scales comparable to particle formation.

Transit through the carbon liquid phase has significant consequences for the subsequent formation of solid nanocarbon detonation products. We report dynamic measurements of liquid carbon condensation and solidification into nano-onions over ∽200 ns by analysis of time-resolved, small-angle X-ray scattering data acquired during detonation of a hydrogen-free explosive, DNTF (3,4-bis(3-nitrofurazan-4-yl)furoxan). Further, thermochemical modeling predicts a direct liquid to solid graphite phase transition for DNTF products ~200 ns post-detonation. Solid detonation products were collected and characterized by high-resolution electron microscopy to confirm the abundance of carbon nano-onions with an average diameter of ∽10 nm, matching the dynamic measurements. We analyze other carbon-rich explosives by similar methods to systematically explore different regions of the carbon phase diagram traversed during detonation. Our results suggest a potential pathway to the efficient production of carbon nano-onions, while offering insight into the phase transformation kinetics of liquid carbon under extreme pressures and temperatures., Detonation of high explosives can produce many nanocarbon allotropes and morphologies, but the mechanism of formation is challenging to explore. Here the authors observe, by time-resolved small-angle X-ray scattering, a transient liquid phase that precedes the formation of carbon onions.

Characterization of the initial morphology of detonation nanodiamond (DND) has been the focus of many research studies that aim to develop a fundamental understanding of carbon condensation under e...

Carbon rich materials lacking sufficient oxygen to undergo complete combustion have long been known to produce nanocarbon condensates of utility to industries spanning nanomedicine to quantum computing, when subject to strong shockwaves. However, the associated extreme conditions (e.g. 1000s of K and 10s of GPa) and rapid system evolution (e.g. 10s of ps) has precluded a clear understanding of early time phenomena giving way to carbon condensate formation. The semi-quantum density functional theory tight binding (DFTB) simulation method is ideal for studying chemistry on these timescales, offering much of the predictive power of density functional theory (DFT) at a fraction of the computational cost. However available parameterizations are not designed for application to organic molecular materials under extreme conditions.Here, we describe a new machine learning (ML) approach for rapidly tuning DFTB models to simulate molecular materials under extreme conditions and demonstrate its application to modeling of 3,4-bis(3-nitrofurazan-4-yl)furoxan (i.e. DNTF), which has recently been shown to produce liquid carbon nanodroplets upon detonation that subsequently solidify into graphitic nano-onions. We investigate early shockwave-driven decomposition chemistry to determine (1) major chemical kinetics steps, (2) the DNTF shock equation of state, and (3) implications of (1) and (2) for the DNTF nanocarbon formation mechanisms. We find evolution to be characterized by release of CO2, N2, and CO, as well as large CxNyOz species that are likely to be precursors to the experimentally observed carbon nano onions. Moreover, we find O purification (i.e. via CO2 elimination) more rapid than that of N (i.e. via N2 elimination), consistent with the experimentally observed N-containing species entrainment within the carbon condensates. Ultimately, we find the present developed ML-driven DFTB tuning approach well suited to the study of chemistry under extreme conditions, by providing a means of achieving long-timescale simulation with DFT-accuracy.

Density Functional Tight Binding (DFTB) is an attractive method for accelerated quantum simulations of condensed matter due to its enhanced computational efficiency over standard Density Functional Theory approaches. However, DFTB models can be challenging to determine for individual systems of interest, especially for metallic and interfacial systems where different bonding arrangements can lead to significant changes in electronic states. In this regard, we have created a rapid-screening approach for determining systematically improvable DFTB interaction potentials that can yield transferable models for a variety of conditions. Our method leverages a recent reactive molecular dynamics force field where many-body interactions are represented by linear combinations of Chebyshev polynomials. This allows for the efficient creation of multi-center representations with relative ease, requiring only a small investment in initial DFT calculations. We have focused our workflow on TiH$_2$ as a model system and show that a relatively small training set based on unit-cell sized calculations yields a model accurate for both bulk and surface properties. Our approach is easy to implement and can yield accurate DFTB models over a broad range of 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., Comment: 45 pages, 7 tables, 7 figures. New version contains updated figures and light editorial changes. This version has been submitted as a revision to J Chem. Theory Comput

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The phonon Boltzmann transport equation (BTE) with the relaxation time approximation (RTA) scattering model is used to calculate transient temperature profiles in graphene, and the results are compared to analogous molecular dynamics (MD) simulations. For the BTE calculations, the phonon dispersion relation and frequency-dependent scattering rates are obtained from a combination of MD data and semi-empirical power-law expressions for the normal and Umklapp phonon lifetimes. The dimensions and initial temperature conditions of graphene are varied to study the size and temperature dependence of thermal transport physics at the mesoscopic scale. Good quantitative agreement to within 5% is found between the BTE and MD results, over a wide range of temperatures and lengthscales of the temperature variation in the graphene sheet. Small differences are attributed to the inaccuracy of the RTA as applied to graphene, and to neglecting four-phonon scattering in the BTE simulations. The present results may further understanding in applications such as the transient heating of nanoelectronics.

Machine learned reactive force fields based on polynomial expansions have been shown to be highly effective for describing simulations involving reactive materials. Nevertheless, the highly flexible nature of these models can give rise to a large number of candidate parameters for complicated systems. In these cases, reliable parameterization requires a well-formed training set, which can be difficult to achieve through standard iterative fitting methods. Here we present an active learning approach based on cluster analysis and Shannon information theory to enable semi-automated generation of informative training sets and robust machine learned force fields. Use of this tool is demonstrated for development of a model based on linear combinations of Chebyshev polynomials explicitly describing up to four-body interactions, for a chemically and structurally diverse system of C/O under extreme conditions. We show that this flexible training repository management approach enables development of models exhibiting excellent agreement with Kohn–Sham density functional theory (DFT) in terms of structure, dynamics, and speciation.

Shock initiation and detonation of high explosives is considered to be controlled through hot spots, which are local regions of elevated temperature that accelerate chemical reactions. Using classical molecular dynamics, we predict the formation of nanoscale shear bands through plastic failure in shocked 1,3,5-triamino-2,4,6-trinitrobenzene high explosive crystal. By scale bridging with quantum-based molecular dynamics, we show that shear bands exhibit lower reaction barriers. While shear bands quickly cool, they remain chemically activated and support increased reaction rates without the local heating typically evoked by the hot spot paradigm. We describe this phenomenon as chemical activation through shear banding.

We demonstrate development of the Chebyshev Interaction Model for Efficient Simulation (ChIMES) for molecular systems through application to water under ambient conditions (298 K, 1 g/cm3). These models, which are comprised of linear combinations of Chebyshev polynomials explicitly describing two- and three-body interactions, are largely fit by force matching to Kohn-Sham Density Functional Theory (DFT). Protocols for selecting user-specified parameters and inclusion of stress tensor data are investigated, and structural and dynamical property prediction for resulting models is benchmarked against DFT. We show that the present ChIMES force fields yield excellent agreement with DFT without the need for additional terms such as those for Coulomb interactions. Overall, we show that tractable parametrization and subsequent accuracy of the present models make ChIMES an ideal candidate for extension of DFT dynamics to larger system sizes and longer time scales.

We detail the creation of a multicenter density functional tight binding (DFTB) model for hydrogen on δ-plutonium, using a framework of new Slater-Koster interaction parameters and a repulsive energy based on the Chebyshev Interaction Model for Efficient Simulation (ChIMES), where two- and three-center atomic interactions are represented by linear combinations of Chebyshev polynomials. We find that our DFTB/ChIMES model yields a total electron density of states for bulk δ-Pu that compares well to that from Density Functional Theory, as well as to a grid of energy calculations representing approximate H2 dissociation paths on the δ-Pu (100) surface. We then perform molecular dynamics simulations and minimum energy pathway calculations to determine the energetics of surface dissociation and subsurface diffusion on the (100) and (111) surfaces. Our approach allows for the efficient creation of multicenter repulsive energies with a relatively small investment in initial DFT calculations. Our efforts are particularly pertinent to studies that rely on quantum calculations for interpretation and validation, such as experimental determination of chemical reactivity both on surfaces and in condensed phases.

High-rate strength behavior plays an important role in the shock initiation of high explosives, with plastic deformation serving to localize heat into hot spots and as a mechanochemical means to enhance reactivity. Recent simulations predict that detonation-like shocks produce highly reactive nanoscale shear bands in the layered crystalline explosive TATB (1,3,5-triamino-2,4,6-trinitrobenzene), but the thresholds leading to this response are poorly understood. We utilize molecular dynamics to simulate the high-rate compressive stress–strain response of TATB, with a focus on understanding flow behavior. The dependence of strength on pressure and loading axis (crystal orientation) is explored. The deformation mechanisms fall broadly into two categories, with compression along crystal layers activating a buckling/twinning mode and compression normal to the layers producing nanoscale shear bands. Despite the complexity of the underlying mechanisms, the crystal exhibits relatively straightforward stress–strain curves. Most of the crystal orientations studied show rapid strain softening following the onset of yielding, which settles to a steady flow state. Trajectories are analyzed using five metrics for local states and structural order, but most of these metrics yield similar distributions for these deformation mechanisms. On the other hand, a recently proposed measure of intramolecular strain energy is found to most cleanly distinguish between these mechanisms, while also providing a plausible connection with mechanochemically accelerated decomposition kinetics. Localization of intramolecular strain energy is found to depend strongly on crystal orientation and pressure.

We have developed a transferable reactive force field for C/O systems under extreme temperature and pressure conditions based on the many-body Chebyshev Interaction Model for Efficient Simulation (ChIMES). The resulting model is shown to recover much of the accuracy of DFT for prediction of structure, dynamics and chemistry when applied to dissociative systems at 1:1 and 1:2 C:O ratios, as well as molten carbon. Our C/O modeling approach exhibits a 104 increase in efficiency and linear system size scalability over standard quantum molecular dynamics methods, allowing simulation of significantly larger systems than previously possible. Furthermore, we show that system sizes of at least 500 atoms are required to observe the formation of experimentally predicted molten carbon condensates under oxygen-deficient conditions, indicative of possible system size effects in quantum simulations of these types of systems. Overall, we find the present ChIMES model to be well suited for modeling chemical processes and cluster formation at pressures and temperatures typical of shock waves. We expect that the present C/O modeling paradigm can serve as a template for the development of a high pressure --high temperature organic chemistry force-field.

We present methods for the creation of semi-empirical quantum approaches and reactive force fields through force matching to quantum simulation data for materials under reactive conditions. Our methodologies overcome the extreme computational cost of standard Kohn–Sham Density Functional Theory (DFT) by mapping DFT computed simulation data onto functional forms with linear dependence on their parameters. This allows for quick parameterization of our models by avoiding the nonlinear fitting bottlenecks associated with most molecular dynamics model development. We illustrate our approach with two different systems: (i) determination of density functional tight binding models for aqueous glycine dimerization, and (ii) determination of the Chebyshev Interactional Model for Efficient Simulation (ChIMES) reactive force field for metallic liquid carbon. In each case, we observe that our approach is easy to parametrize and yields a model that is orders of magnitude faster than DFT while largely retaining its accuracy. Overall, our methods have potential use for studying complex long time and length scale chemical reactivity at extreme conditions, where there is a significant need for computationally efficient atomistic simulations methods to aid in the interpretation and design of experiments.

HN3 is a unique liquid energetic material that exhibits ultrafast detonation chemistry and a transition to metallic states during detonation. We combine the Chebyshev interaction model for efficient simulation (ChIMES) many-body reactive force field and the extended-Lagrangian multiscale shock technique molecular dynamics method to calculate the detonation properties of HN3 with the accuracy of Kohn-Sham density-functional theory. ChIMES is based on a Chebyshev polynomial expansion and can accurately reproduce density-functional theory molecular dynamics (DFT-MD) simulations for a wide range of unreactive and decomposition conditions of liquid HN3. We show that addition of random displacement configurations and the energies of gas-phase equilibrium products in the training set allows ChIMES to efficiently explore the complex potential energy surface. Schemes for selecting force field parameters and the inclusion of stress tensor and energy data in the training set are examined. Structural and dynamical properties and chemistry predictions for the resulting models are benchmarked against DFT-MD. We demonstrate that the inclusion of explicit four-body energy terms is necessary to capture the potential energy surface across a wide range of conditions. Our results generally retain the accuracy of DFT-MD while yielding a high degree of computational efficiency, allowing simulations to approach orders of magnitude larger time and spatial scales. The techniques and recipes for MD model creation we present allow for direct simulation of nanosecond shock compression experiments and calculation of the detonation properties of materials with the accuracy of Kohn-Sham density-functional theory.

We report gas-phase enthalpies of formation for the set of energetic molecules NTO, DADE, LLM-105, TNT, RDX, TATB, HMX, and PETN using the G2, G3, G4, and ccCA-PS3 quantum composite methods. Calculations for HMX and PETN hitherto represent the largest molecules attempted with these methods. G3 and G4 calculations are typically close to one another, with a larger difference found between these methods and ccCA-PS3. Although there is significant uncertainty in experimental values, the mean absolute deviation between the average experimental value and calculations are 12, 6, 7, and 3 kcal/mol for G2, G3, G4, and ccCA-PS3, respectively.

Machine learned reactive force fields based on polynomial expansions have been shown to be highly effective for describing simulations involving reactive materials. Nevertheless, the highly flexible nature of these models can give rise to a large number of candidate parameters for complicated systems. In these cases, reliable parameterization requires a well-formed training set, which can be difficult to achieve through standard iterative fitting methods. Here, we present an active learning approach based on cluster analysis and inspired by Shannon information theory to enable semi-automated generation of informative training sets and robust machine learned force fields. The use of this tool is demonstrated for development of a model based on linear combinations of Chebyshev polynomials explicitly describing up to four-body interactions, for a chemically and structurally diverse system of C/O under extreme conditions. We show that this flexible training database management approach enables development of models exhibiting excellent agreement with Kohn-Sham density functional theory in terms of structure, dynamics, and speciation.

We describe the development of a reactive force field for C/O systems under extreme temperatures and pressures, based on the many-body Chebyshev Interaction Model for Efficient Simulation (ChIMES). The resulting model, which targets carbon condensation under thermodynamic conditions of 6500 K and 2.5 g cm-3, affords a balance between model accuracy, complexity, and training set generation expense. We show that the model recovers much of the accuracy of density functional theory for the prediction of structure, dynamics, and chemistry when applied to dissociative condensed phase systems at 1:1 and 1:2 C:O ratios, as well as molten carbon. Our C/O modeling approach exhibits a 104 increase in efficiency for the same system size (i.e., 128 atoms) and a linear system size scalability over standard quantum molecular dynamics methods, allowing the simulation of significantly larger systems than previously possible. We find that the model captures the condensed-phase reaction-coupled formation of carbon clusters implied by recent experiments, and that this process is susceptible to strong finite size effects. Overall, we find the present ChIMES model to be well suited for studying chemical processes and cluster formation at pressures and temperatures typical of shock waves. We expect that the present C/O modeling paradigm can serve as a template for the development of a broader high pressure-high temperature force-field for condensed phase chemistry in organic materials.

We present laser-driven shock Hugoniot measurements of single-crystal (SC) 1,3,5-triamino-2,4,6-trinitrobenzene (TATB) between 15 and 83 GPa, spanning pressures below and well above the Chapman–Jouguet pressure of ∼28 GPa for TATB formulations (TATB grains mixed with plastic binders at 5–10 wt. %). The new SC data are generally ∼3% more compressible than previously published data on neat and formulated TATB measured in gas-gun and explosive-driven experiments. An exception is at compressions in the density of ∼1.5 ( ∼30–40 GPa), where our new SC data exhibit significantly lower pressures than previous results on overdriven TATB formulations, suggesting that our SC samples remain largely unreacted below 35 GPa over the short nanosecond-time scales inherent to our laser-driven experiments. These novel equation-of-state measurements are a critical step toward understanding TATB in its most fundamental form and improving predictive modeling of TATB-based explosives.

Experiments were performed at -55°C to measure the reacted state of LX-17 (92.5% TATB and 7.5% Kel-F by weight) using a double shock technique with two flyer materials (with known properties) mounted on a projectile that send an initial shock through the material close to the Chapman-Jouguet (CJ) state followed by a second shock at a higher magnitude into the detonated material. Information on the reacted state is obtained by measuring the relative timing and magnitude of the first and second shock waves. The LX-17 detonation reaction zone profiles plus the arrival times and amplitudes of reflected shocks in LX-17 detonation reaction products were measured using Photonic Doppler Velocimetry (PDV) probes and an aluminum foil coated LiF window. A discussion of this work will include a comparison to prior work at ambient temperature, the experimental parameters, velocimetry profiles, data interpretation, reactive CHEETAH and Ignition and Growth modeling, as well as detail on possible future experiments.

We show that force matching can be used to determine accurate empirical repulsive energies for the density functional tight binding method (DFTB) for chemical reactivity in condensed phases. Our approach yields improved results over previous parametrizations for molten liquid carbon and a phenolic polymer under combustion conditions. The method we present here allows for predictions of chemical properties over longer time periods than accessible via Kohn-Sham density functional theory while retaining its accuracy.

We present a new force field and development scheme for atomistic simulations of materials under extreme conditions. These models, which explicitly include two- and three-body interactions, are generated by fitting linear combinations of Chebyshev polynomials through force matching to trajectories from Kohn–Sham density functional theory (DFT). We apply our method to liquid carbon near the diamond/graphite/liquid triple point and at higher densities and temperatures, where metallization and many-body effects may be substantial. We show that explicit inclusion of three-body interaction terms allows our model to yield improved descriptions of both dynamic and structural properties over previous empirical potential efforts, while exhibiting transferability to nearby state points. The simplicity of our functional form and subsequent efficiency of parameter determination allow for extension of DFT to experimental time and length scales while retaining most of its accuracy.

We report results from ultrafast compression experiments conducted on β-HMX single crystals. Results consist of nominally 12 picosecond time-resolved wave profile data, (ultrafast time domain interferometry –TDI measurements), that were analyzed to determine high-velocity wave speeds as a function of piston velocity. TDI results are used to validate calculations of anisotropic stress-strain behavior of shocked loaded energetic materials. Our previous results derived using a 350 ps duration compression drive revealed anisotropic elastic wave response in single crystal β-HMX from (110) and (010) impact planes. Here we present results using a 1.05 ns duration compression drive with a 950 ps interferometry window to extend knowledge of the anisotropic dynamic response of β-HMX within eight microns of the initial impact plane. We observe two distinct wave profiles from (010) and three wave profiles from (010) impact planes. The (110) impact plane wave speeds typically exceed (010) impact plane wave speeds at t...

Many of the safety properties of solid energetic materials are related to microstructural features. The mechanisms coupling microstructural features to safety, however, are difficult to directly measure. Grain-scale simulation is a rapidly expanding area which promises to improve our understanding of energetic material safety. In this chapter, we review two approaches to grain-scale simulation. The first is multi-crystal simulations, which emphasize the role of multi-crystal interactions in determining the response of the material. The second is single-crystal simulations, which emphasize a more detailed treatment of the chemical and physical processes underlying energetic material safety.

We present a method for the creation of classical force fields for water under dissociative thermodynamic conditions by force matching to molecular dynamics trajectories from Kohn–Sham density functional theory (DFT). We apply our method to liquid water under dissociative conditions, where molecular lifetimes are less than 1 ps, and superionic water, where hydrogen ions diffuse at liquid-like rates through an oxygen lattice. We find that, in general, our new models are capable of accurately reproducing the structural and dynamic properties computed from DFT, as well as the molecular concentrations and lifetimes. Overall, our force-matching approach presents a relatively simple way to create classical reactive force fields for a single thermodynamic state point that largely retains the accuracy of DFT while having the potential to access experimental time and length scales.

Experimental determination of the isothermal high-pressure equation of state (EOS) of composites is not feasible by using conventional diffraction techniques. To overcome this issue in the case of polymer bonded explosives (PBXs), composites made of an energetic material and a polymeric binder, we have expanded the applicability of the optical microscopy and interferometry technique previously developed in our group. To accommodate representative samples of a PBX with large grains, we modified the diamond culets of a diamond anvil cell to include etched micrometer-scale pits. This enabled us to measure the isothermal EOS of a PBX, namely, LX-17, up to 8 GPa. The results are compared with the EOSs of the constituent materials and previously published shock measurements. The technique employed in this study is not limited to PBXs and could be potentially used for the EOS determination of other materials ranging from composites to alloys and granular materials.Experimental determination of the isothermal high-pressure equation of state (EOS) of composites is not feasible by using conventional diffraction techniques. To overcome this issue in the case of polymer bonded explosives (PBXs), composites made of an energetic material and a polymeric binder, we have expanded the applicability of the optical microscopy and interferometry technique previously developed in our group. To accommodate representative samples of a PBX with large grains, we modified the diamond culets of a diamond anvil cell to include etched micrometer-scale pits. This enabled us to measure the isothermal EOS of a PBX, namely, LX-17, up to 8 GPa. The results are compared with the EOSs of the constituent materials and previously published shock measurements. The technique employed in this study is not limited to PBXs and could be potentially used for the EOS determination of other materials ranging from composites to alloys and granular materials.

Determining the unreacted equation of state of 1,3,5-triamino-2,4,6-trinitrobenzene (TATB) is challenging because it exhibits low crystal symmetry and low X-ray scattering strength. Here, we present the first high-pressure single-crystal X-ray diffraction (SXD) study of this material. Our SXD results reveal a previously unknown transition to a monoclinic phase above 4 GPa. No abrupt change of the volume occurs but the compressibility changes. Concomitant first principles evolutionary crystal structure prediction USPEX calculations confirm this transition and show that it involves a pressure-induced in-plane shift of the layers of TATB molecules with respect to the ambient-pressure phase.

We report the adiabatic sound speeds for super- critical fluid carbon monoxide along two isotherms, from 0.17 to 2.13 GPa at 297 K and from 0.31 to 3.2 GPa at 600 K. The carbon monoxide was confined in a resistively heated diamond-anvil cell, and the sound speed measurements were conducted in situ using a recently reported variant of the photoacoustic light scattering effect. The measured sound speeds were then used to parametrize a single site dipolar exponential-6 intermolecular potential for carbon monoxide. PρT thermodynamic states, sound speeds, and shock Hugoniots were calculated using the newly parametrized intermolecular potential and compared to previously reported experimental results. Additionally, we generated an analytical equation of state for carbon monoxide by fitting to a grid of calculated PρT states over a range of 0.1−10 GPa and 150−2000 K. A 2% mean variation was found between computed high- pressure solid-phase densities and measured dataa surprising result for a spherical interaction potential. We further computed a rotationally dependent fluid to β-solid phase boundary; results signal the relative magnitude of short-range rotational disorder under conditions that span existing phase boundary measurements.

We report here on development of a density functional tight binding (DFTB) simulation approach for carbon under extreme pressures and temperatures that includes an expanded basis set and an environmentally dependent repulsive energy. We find that including d-orbital interactions in the DFTB Hamiltonian improves determination of the electronic states at high pressure–temperature conditions, compared to standard DFTB implementations that utilize s- and p-orbitals only for carbon. We then determine a three-body repulsive energy through fitting to diamond, BC8, and simple cubic cold compression curve data, as well pressures from metallic liquid configurations from density functional theory (DFT) simulations. Our new model (DFTB-p3b) yields approximately 2 orders of magnitude increase in computational efficiency over standard DFT while retaining its accuracy for condensed phases of carbon under a wide range of conditions, including the metallic liquid phase at conditions up to 2000 GPa and 30 000 K. Our result...

We report density functional theoretical calculations of the equation of state (EOS) of 1,3,5-triamino-2,4,6-trinitrobenzene (TATB) under hydrostatic compression of up to 250 GPa. Our results show increasing bond equivalency between the intramolecular and intermolecular hydrogen bonds of the amino and nitro groups in the region 30 < P < 70 GPa, beyond which the difference between the two bond distances remains constant. This approximate bond equivalency is manifested by a rapid decrease of the intermolecular −NO···HN– distance along the b lattice direction from 2.6 A at the zero pressure equilibrium geometry to 1.72 A at 67 GPa and by a decrease of the intramolecular −NO···HN– bond from 1.65 to 1.57 A for the same pressure region. The strengthening of intermolecular hydrogen bonding with increased pressure is in accordance with recent infrared spectroscopic measurements of decreasing activity of NH2 vibrational modes with increasing pressure up to 40 GPa.

We report herein on simulations of carbon under pressures up to 2000 GPa and 30 000 K using the density functional tight binding method (DFTB) with a parameter set we have specifically designed for these conditions. The DFTB method can provide a high throughput simulation capability compared to Kohn–Sham density functional theory while retaining most of its accuracy. We fit the DFTB repulsive energy to measured and computed diamond isothermal compression data and show that this yields accurate compression curves for diamond, graphite, and the BC8 phase, as well as material properties for all three phases. We then show that our new repulsive energy yields predictions of the Hugoniot of diamond shock compressed to the conducting liquid that are within the range of different experimental measurements. Our results provide a straightforward method by which DFTB can be extended to studies of covalently bonded materials under extremely high pressures and temperatures such as the interiors of planets and other la...

Overdriven double shock experiments provide a measurement of the properties of the reaction product states of the 1-3-5-triamino-2-4-6trinitrobenzene-based explosive LX-17. These experiments used two flyer materials mounted on the end of a projectile to send an initial shock through the LX-17, followed by a second shock of a higher magnitude into the detonation products. Here, the experimental results are compared to 2D reactive flow modeling. A reactive flow model that describes only the kinetics of the LX-17 decomposition fails to accurately reproduce the decay of the first shock or the curvature or strength of the second shock. A new model is proposed in which the carbon condensate produced in the reaction zone is controlled by a kinetic rate. This allows the carbon condensate to be initially out of chemical equilibrium with the product gas. This new model reproduces the initial detonation peak and decay and matches the curvature of the second shock; however, it still over-predicts the strength of the second shock.

Delivery of prebiotic compounds to early Earth from an impacting comet is thought to be an unlikely mechanism for the origins of life because of unfavourable chemical conditions on the planet and the high heat from impact. In contrast, we find that impact-induced shock compression of cometary ices followed by expansion to ambient conditions can produce complexes that resemble the amino acid glycine. Our ab initio molecular dynamics simulations show that shock waves drive the synthesis of transient C-N bonded oligomers at extreme pressures and temperatures. On post impact quenching to lower pressures, the oligomers break apart to form a metastable glycine-containing complex. We show that impact from cometary ice could possibly yield amino acids by a synthetic route independent of the pre-existing atmospheric conditions and materials on the planet.