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.

Semi-Automated Creation of Density Functional Tight Binding Models through Leveraging Chebyshev Polynomial-Based Force Fields.

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 (DFT) 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 TiH2 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 reliable 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.

Submicrosecond Aggregation during Detonation Synthesis of Nanodiamond.

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.

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

We describe a machine learning approach to rapidly tune density functional tight binding models for the description of detonation chemistry in organic molecular materials. Resulting models enable simulations on the several 10s of ps scales characteristic to these processes, with "quantum-accuracy." We use this approach to investigate early shock chemistry in 3,4-bis(3-nitrofurazan-4-yl)furoxan, a hydrogen-free energetic material known to form onion-like nanocarbon particulates following detonation. We find that the ensuing chemistry is significantly characterized by the formation of large CxNyOz species, which are likely precursors to the experimentally observed carbon condensates. Beyond utility as a means of investigating detonation chemistry, the present approach can be used to generate quantum-based reference data for the development of full machine-learned interatomic potentials capable of simulation on even greater time and length scales, i.e., for applications where characteristic time scales exceed the reach of methods including Kohn-Sham density functional theory, which are commonly used for reference data generation.

Calculation of the detonation state of HN3 with quantum accuracy.

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.

Active learning for robust, high-complexity reactive atomistic simulations.

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.

Many-body reactive force field development for carbon condensation in C/O systems under extreme conditions.

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.

High Explosive Ignition through Chemically Activated Nanoscale Shear Bands.

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.

Ultrafast shock synthesis of nanocarbon from a liquid precursor.

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.

Application of the ChIMES Force Field to Nonreactive Molecular Systems: Water at Ambient Conditions.

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.

Development of a Multicenter Density Functional Tight Binding Model for Plutonium Surface Hydriding.

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.

ChIMES: A Force Matched Potential with Explicit Three-Body Interactions for Molten Carbon.

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.

Using Force Matching To Determine Reactive Force Fields for Water under Extreme Thermodynamic Conditions.

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.

Using force-matched potentials to improve the accuracy of density functional tight binding for reactive conditions.

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.

First-principles high-pressure unreacted equation of state and heat of formation of crystal 2,6-diamino-3, 5-dinitropyrazine-1-oxide (LLM-105).

We report dispersion-corrected density functional theoretical calculations of the unreacted equation of state (EOS) of crystal 2,6-diamino-3, 5-dinitropyrazine-1-oxide (LLM-105) under hydrostatic compression of up to 45 GPa. Convergence tests for k-points sampling in the Brillouin zone show that a 3 × 1 × 2 mesh is required to reproduce the X-ray crystal structure at ambient conditions, and we confirm our finding with a separate supercell calculation. Our high-pressure EOS yields a bulk modulus of 19.2 GPa, and indicates a tendency towards anisotropic compression along the b lattice vector due to molecular orientations within the lattice. We find that the electronic energy band gap decreases from a semiconductor type of 1.3 eV at 0 GPa to quasi-metallic type of 0.6 eV at 45 GPa. The extensive intermolecular hydrogen bonds involving the oxide (-NO) and dioxide (-NO2) interactions with the amine (-NH2) group showed enhanced interactions with increasing pressure that should be discernible in the mid IR spectral region. We do not find evidence for structural phase transitions or chemically induced transformations within the pressure range of our study. The gas phase heat of formation is calculated at the G4 level of theory to be 22.48 kcal/mol, while we obtain 25.92 kcal/mol using the ccCA-PS3 method. Density functional theory calculations of the crystal and the gas phases provided an estimate for the heat of sublimation of 32.4 kcal/mol. We thus determine the room-temperature solid heat of formation of LLM-105 to be -9.9 or -6.5 kcal/mol based on the G4 or ccCA-PS3 methods, respectively.

Experimental measurement of speeds of sound in dense supercritical carbon monoxide and development of a high-pressure, high-temperature equation of state.

We report the adiabatic sound speeds for supercritical 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 ß-solid phase boundary; results signal the relative magnitude of short-range rotational disorder under conditions that span existing phase boundary measurements.

Ultrafast detonation of hydrazoic acid (HN3).

The fastest self-sustained chemical reactions in nature occur during detonation of energetic materials where reactions are thought to occur on nanosecond or longer time scales in carbon-containing materials. Here we perform the first atomistic simulation of an azide energetic material, HN3, from the beginning to the end of the chemical evolution and find that the time scale for complete decomposition is a mere 10 ps, orders of magnitude shorter than that of secondary explosives and approaching the fundamental limiting time scale for chemistry; i.e., vibrational time scale. We study several consequences of the short time scale including a state of vibrational disequilibrium induced by the fast transformations.