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Data-driven machine learning techniques can be useful for the rapid evaluation of material properties in extreme environments, particularly in cases where direct access to the materials is not possible. Such problems occur in high-throughput material screening and material design approaches where many candidates may not be amenable to direct experimental examination. In this paper, we perform an exhaustive examination of the applicability of machine learning for the estimation of isothermal shock compression properties, specifically the shock Hugoniot, for diverse material systems. A comprehensive analysis is conducted where effects of scarce data, variances in source data, feature choices, and model choices are systematically explored. New modeling strategies are introduced based on feature engineering, including a feature augmentation approach, to mitigate the effects of scarce data. The findings show significant promise of machine learning techniques for design and discovery of materials suited for shock compression applications. [ABSTRACT FROM AUTHOR]

We describe the development of a density-dependent transferable coarse-grain model of crystalline hexahydro-1,3,5-trinitro-s-triazine (RDX) that can be used with the energy conserving dissipative particle dynamics method. The model is an extension of a recently reported one-site model of RDX that was developed by using a force-matching method. The density-dependent forces in that original model are provided through an interpolation scheme that poorly conserves energy. The development of the new model presented in this work first involved a multi-objective procedure to improve the structural and thermodynamic properties of the previous model, followed by the inclusion of the density dependency via a conservative form of the force field that conserves energy. The new model accurately predicts the density, structure, pressure-volume isotherm, bulk modulus, and elastic constants of the RDX crystal at ambient pressure and exhibits transferability to a liquid phase at melt conditions. [ABSTRACT FROM AUTHOR]

Methane clathrate hydrate nucleation and growth is investigated via analysis of molecular dynamics simulations using a new order parameter. This order parameter (OP), named the Mutually Coordinated Guest (MCG) OP, quantifies the appearance and connectivity of molecular clusters composed of guests separated by water clusters. It is the first two-component OP used for quantifying hydrate nucleation and growth. The algorithm for calculating the MCG OP is described in detail. Its physical motivation and advantages compared to existing methods are discussed. [ABSTRACT FROM AUTHOR]

We present new capabilities for investigation of microstructure in energetic material response for both explicit large‐scale and multiscale simulations. We demonstrate the computational capabilities by studying the effect of porosity on the reactive shock response of a coarse‐grain (CG) model of the energetic material cyclotrimethylene trinitramine (RDX), the non‐reactive equation of state for a porous representative volume element (RVE) of CG RDX, and utilization of available supercomputing resources for speculative sampling to accelerate hierarchical multiscale simulations. Small amounts of porosity (up to 4 %) are shown to have significant effect on the initiation of reactive CG RDX using large‐scale reactive dissipative particle dynamics simulations. Non‐reactive RVEs are shown to undergo a porosity‐dependent pore collapse at hydrostatic conditions, and an existing automation framework is shown to be easily modified for the incorporation of microstructure while retaining reliable convergence properties. A novel predictive sampling method based on use of kernel density estimators is shown to effectively accelerate time‐to‐solution in a multiscale simulation, scaling with free CPU cores, while making no assumptions about the underlying physics for the data being analyzed. These multidisciplinary studies of distinct yet connected problems combine to provide methodological insights for high‐fidelity modeling of reactive systems with microstructure. [ABSTRACT FROM AUTHOR]

Nucleation from solution is a ubiquitousphenomenon with relevanceto myriad scientific disciplines, including pharmaceuticals, biomineralization,and disease. One prominent example is the nucleation of clathratehydrates, multicomponent crystalline inclusion compounds relevantto the energy industry where they block pipelines and also constitutea potential vast energy resource. Despite their importance, the molecularmechanism of incipient hydrate formation remains unknown. Herein,we employ advanced molecular simulation tools (pBhistogram, equilibrium path sampling) to provide a statistical-mechanicalbasis for extracting physical insight into the molecular steps bywhich clathrates form. Through testing the Mutually Coordinated Guest(MCG) order parameter, we demonstrate that both guest (methane) andhost (water) structuring are crucial to accurately describe the nucleationof hydrates and determine a critical nucleus size of MCG-1 = 16 at255 K and 500 bar. Equipped with a validated (and novel) reactioncoordinate, subsequent equilibrium path sampling simulations yieldthe free energy barrier and nucleation rate. The resulting quantitativenucleation process is described by the MCG clustering mechanism. Thisconstitutes a significant advance in the field of hydrates research,as the fitness of a molecular descriptor has never been statisticallyverified. More broadly, this work has significance to a wide rangeof multicomponent nucleation contexts wherein the formation mechanismdepends on contributions from both solute and solvent. [ABSTRACT FROM AUTHOR]

The article discusses some of the Web sites on legal research available for a reference librarian to use and how to best use such resources for library patrons. State government Web pages for Mississippi include the state Web site at www.mississippi.gov, the legislature's Web site at billstatus.ls.state.ms.us, and the judiciary site at www.mssc.state.ms.us. Examples of educational Web sites include the Cornell Legal Information Institute, the University of Richmond Constitution Funder and the Mississippi College School of Law Mississippi Resources page.