A reactive molecular dynamics model for uranium/hydrogen containing systems.

Uranium-based materials are valuable assets in the energy, medical, and military industries. However, understanding their sensitivity to hydrogen embrittlement is particularly challenging due to the toxicity of uranium and the computationally expensive nature of quantum-based methods generally required to study such processes. In this regard, we have developed a Chebyshev Interaction Model for Efficient Simulation (ChIMES) that can be employed to compute energies and forces of U and UH3 bulk structures with vacancies and hydrogen interstitials with accuracy similar to that of Density Functional Theory (DFT) while yielding linear scaling and orders of magnitude improvement in computational efficiency. We show that the bulk structural parameters, uranium and hydrogen vacancy formation energies, and diffusion barriers predicted by the ChIMES potential are in strong agreement with the reference DFT data. We then use ChIMES to conduct molecular dynamics simulations of the temperature-dependent diffusion of a hydrogen interstitial and determine the corresponding diffusion activation energy. Our model has particular significance in studies of actinides and other high-Z materials, where there is a strong need for computationally efficient methods to bridge length and time scales between experiments and quantum theory.

United atom and coarse grained models for crosslinked polydimethylsiloxane with applications to the rheology of silicone fluids.

Siloxane systems consisting primarily of polydimethylsiloxane (PDMS) are versatile, multifaceted materials that play a key role in diverse applications. However, open questions exist regarding the correlation between their varied atomic-level properties and observed macroscale features. To this effect, we have created a systematic workflow to determine coarse-grained simulation models for crosslinked PDMS in order to further elucidate the effects of network changes on the system's rheological properties below the gel point. Our approach leverages a fine-grained united atom model for linear PDMS, which we extend to include crosslinking terms, and applies iterative Boltzmann inversion to obtain a coarse-grain "bead-spring-type" model. We then perform extensive molecular dynamics simulations to explore the effect of crosslinking on the rheology of silicone fluids, where we compute systematic increases in both density and shear viscosity that compare favorably to experiments that we conduct here. The kinematic viscosity of partially crosslinked fluids follows an empirical linear relationship that is surprisingly consistent with Rouse theory, which was originally derived for systems comprised of a uniform distribution of linear chains. The models developed here serve to enable quantitative bottom-up predictions for curing- and age-induced effects on macroscale rheological properties, allowing for accurate prediction of material properties based on fundamental chemical data.

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.

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.

Elucidating the Initial Steps in α-Uranium Hydriding Using First-Principles Calculations.

Hydrogen embrittlement of uranium, which arises due to the formation of a structurally weak pyrophoric hydride, poses a major safety risk in material applications. Previous experiments have shown that hydriding begins on the top or near the surface (i.e., subsurface) of α-uranium. However, the fundamental molecular-level mechanism of this process remains unknown. In this work, starting from pristine α-U bulk and surfaces, we present a systematic investigation of possible mechanisms for the formation of metal hydride. Specifically, we address this problem by examining the individual steps of hydrogen embrittlement, including surface adsorption, subsurface absorption, and the interlayer diffusion of atomic hydrogen. Furthermore, by examining these processes across different facets, we highlight the importance of both (1) hydrogen monolayer coverage and (2) applied tensile strain on hydriding kinetics. Taken together, by studying previously overlooked phenomena, this study provides foundational insights into the initial steps of this overall complex process. We anticipate that this work will guide near-term future development of multiscale kinetic models for uranium hydriding and subsequently identify potential strategies to mitigate this undesired process.

Polymer degradation through chemical change: a quantum-based test of inferred reactions in irradiated polydimethylsiloxane.

Chemical reaction schemes are key conceptual tools for interpreting the results of experiments and simulations, but often carry implicit assumptions that remain largely unverified for complicated systems. Established schemes for chemical damage through crosslinking in irradiated silicone polymers comprised of polydimethylsiloxane (PDMS) date to the 1950's and correlate small-molecule off-gassing with specific crosslink features. In this regard, we use a somewhat reductionist model to develop a general conditional probability and correlation analysis approach that tests these types of causal connections between proposed experimental observables to reexamine this chemistry through quantum-based molecular dynamics (QMD) simulations. Analysis of the QMD simulations suggests that the established reaction schemes are qualitatively reasonable, but lack strong causal connections under a broad set of conditions that would enable making direct quantitative connections between off-gassing and crosslinking. Further assessment of the QMD data uncovers a strong (but nonideal) quantitative connection between exceptionally hard-to-measure chain scission events and the formation of silanol (Si-OH) groups. Our analysis indicates that conventional notions of radiation damage to PDMS should be further qualified and not necessarily used ad hoc. In addition, our efforts enable independent quantum-based tests that can inform confidence in assumed connections between experimental observables without the burden of fully elucidating entire reaction networks.

Davis Computational Spectroscopy Workflow-From Structure to Spectra.

We describe an automated workflow that connects a series of atomic simulation tools to investigate the relationship between atomic structure, lattice dynamics, materials properties, and inelastic neutron scattering (INS) spectra. Starting from the atomic simulation environment (ASE) as an interface, we demonstrate the use of a selection of calculators, including density functional theory (DFT) and density functional tight binding (DFTB), to optimize the structures and calculate interatomic force constants. We present the use of our workflow to compute the phonon frequencies and eigenvectors, which are required to accurately simulate the INS spectra in crystalline solids like diamond and graphite as well as molecular solids like rubrene. We have also implemented a machine-learning force field based on Chebyshev polynomials called the Chebyshev interaction model for efficient simulation (ChIMES) to improve the accuracy of the DFTB simulations. We then explore the transferability of our DFTB/ChIMES models by comparing simulations derived from different training sets. We show that DFTB/ChIMES demonstrates ∼100× reduction in computational expense while retaining most of the accuracy of DFT as well as yielding high accuracy for different materials outside of our training sets. The DFTB/ChIMES method within the workflow expands the possibilities to use simulations to accurately predict materials properties of increasingly complex structures that would be unfeasible with ab initio methods.

A first-principles study of hydrogen surface coverage on δ-Pu (100), (111), and (110) surfaces.

Hydriding corrosion of plutonium leads to surface cracking, pitting, and ultimately structural failure. Laboratory experiments demonstrate that hydriding begins on the surface or near the subsurface of plutonium. However, there has not yet been a systematic evaluation of hydrogen surface coverage on plutonium. In this work, we compute the surface energies of the low facet surfaces of face-centered cubic δ-Pu. The adsorption free energies of expected hydrogen structures at low and high coverage are presented along with the likely progression for filling sites as the H2 partial pressure increases. Implications for near-equilibrium pressure hydride nucleation and non-equilibrium millibar pressure hydriding are discussed.

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.

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.

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.

Effects of pressure on the structure and lattice dynamics of ammonium perchlorate: A combined experimental and theoretical study.

Ammonium perchlorate NH4ClO4 (AP) was studied using synchrotron angle-dispersive X-ray powder diffraction (XRPD) and Raman spectroscopy. A diamond-anvil cell was used to compress AP up to 50 GPa at room temperature (RT). Density functional theory (DFT) calculations were performed to provide further insight and comparison to the experimental data. A high-pressure barite-type structure (Phase II) forms at ≈4 GPa and appears stable up to 40 GPa. Refined atomic coordinates for Phase II are provided, and details for the Phase I → II transition mechanics are outlined. Pressure-dependent enthalpies computed for DFT-optimized crystal structures confirm the Phase I → II transition sequence, and the interpolated transition pressure is in excellent agreement with the experiment. Evidence for additional (underlying) structural modifications include a marked decrease in the Phase II b'-axis compressibility starting at 15 GPa and an unambiguous stress relaxation in the normalized stress-strain response at 36 GPa. Above 47 GPa, XRD Bragg peaks begin to decrease in amplitude and broaden. The apparent loss of crystalline long-range order likely signals the onset of amorphization. Three isostructural modifications were discovered within Phase II via Raman spectroscopy. A revised RT isothermal phase diagram is discussed based on the findings of this study.

Nitrogen oxides as a chemistry trap in detonating oxygen-rich materials.

Despite decades of research, the chemical processes and states of matter that govern the behavior of energetic materials under detonation conditions are not well understood, including the molecular-level processes that determine decomposition kinetics and energy release. Oxygen content is often employed as a simple and intuitive guide to the development and practical use of explosives, but its effect on detonation chemistry remains little studied, especially for the case of oxygen overabundance. To this end, we have conducted quantum molecular dynamics (QMD) simulations of zero oxygen balance and oxygen-rich mixtures of hydrogen peroxide and nitromethane under detonation-like conditions to near-equilibrium time scales. We find excellent agreement between our extrapolated chemical equilibrium properties and those from thermochemical models for the zero oxygen balance mixture. In contrast, for the oxygen-rich mixture, we observe the formation of nitrogen oxide intermediates, particularly nitrate ions (NO3), that effectively act as an oxygen/nitrogen "trap" by precluding the formation of the equilibrium products N2 and CO2. Our results could have implications for the design and modeling of oxygen-rich energetics in common military and industrial use.

A density functional tight binding model with an extended basis set and three-body repulsion for hydrogen under extreme thermodynamic conditions.

We present a new DFTB-p3b density functional tight binding model for hydrogen at extremely high pressures and temperatures, which includes a polarizable basis set (p) and a three-body environmentally dependent repulsive potential (3b). We find that use of an extended basis set is necessary under dissociated liquid conditions to account for the substantial p-orbital character of the electronic states around the Fermi energy. The repulsive energy is determined through comparison to cold curve pressures computed from density functional theory (DFT) for the hexagonal close-packed solid, as well as pressures from thermally equilibrated DFT-MD simulations of the liquid phase. In particular, we observe improved agreement in our DFTB-p3b model with previous theoretical and experimental results for the shock Hugoniot of hydrogen up to 100 GPa and 25000 K, compared to a standard DFTB model using pairwise interactions and an s-orbital basis set, only. The DFTB-p3b approach discussed here provides a general method to extend the DFTB method for a wide variety of materials over a significantly larger range of thermodynamic conditions than previously possible.

Graphene and nano-diamond synthesis in expansions of molten liquid carbon.

Despite their widespread use in high-pressure experiments, little is known about the physical and chemical properties of carbon-containing materials as they expand and cool to ambient conditions. As a result, interpretation of experiments can rely on use of unconstrained models with poor accuracy for the ensuing equation of state properties and final chemical products. To this end, we use quantum simulations to study the free expansion and cooling of carbon from metallic liquid states achieved during shock compression. Expansions from three different sets of shock conditions yielded of a variety of chain and ring structures. We then quantify the relative amounts of graphite-like and diamond-like particles formed during cooling and equilibration. We observe that for all cases, graphene sheets are the majority product formed with more extreme initial conditions producing increasingly larger amounts of diamond particles. Our results can address key needs for future meso-scale models of experiments, where knowledge of material properties and chemical end products can have a pronounced effect on interpreting experimental observables.

Prebiotic chemistry within a simple impacting icy mixture.

We present results of prebiotic organic synthesis in shock compressed mixtures of simple ices from quantum molecular dynamics (MD) simulations extended to close to equilibrium time scales. Given the likelihood of an inhospitable prebiotic atmosphere on early Earth, it is possible that impact processes of comets or other icy bodies were a source of prebiotic chemical compounds on the primitive planet. We observe that moderate shock pressures and temperatures within a CO2-rich icy mixture (36 GPa and 2800 K) produce a number of nitrogen containing heterocycles, which dissociate to form functionalized aromatic hydrocarbons upon expansion and cooling to ambient conditions. In contrast, higher shock conditions (48-60 GPa, 3700-4800 K) resulted in the synthesis of long carbon-chain molecules, CH4, and formaldehyde. All shock compression simulations at these conditions have produced significant quantities of simple C-N bonded compounds such as HCN, HNC, and HNCO upon expansion and cooling to ambient conditions. Our results elucidate a mechanism for impact synthesis of prebiotic molecules at realistic impact conditions that is independent of external constraints such as the presence of a catalyst, illuminating UV radiation, or pre-existing conditions on a planet.

Ultrafast shock initiation of exothermic chemistry in hydrogen peroxide.

We report observations of shock compressed, unreacted hydrogen peroxide at pressures up to the von Neumann pressure for a steady detonation wave, using ultrafast laser-driven shock wave methods. At higher laser drive energy we find evidence of exothermic chemical reactivity occurring in less than 100 ps after the arrival of the shock wave in the sample. The results are consistent with our MD simulations and analysis and suggest that reactivity in hydrogen peroxide is initiated on a sub-100 ps time scale under conditions found just subsequent to the lead shock in a steady detonation wave.

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.