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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]

Recent advances have enabled machine learning methodologies developed for large datasets to be applied to the small experimental datasets typically available for chemical systems. Such advances typically involve a data‐based approach to transfer learning, where a portion of the experimental data for the property of interest is used to fine‐tune a model that is pre‐trained on computationally generated data. This transfer learning approach does not work for very small experimental datasets, where there are only enough data for model validation. Here, we develop a physics‐informed transfer learning strategy to train a directed‐message passing neural network (D‐MPNN) model, enabling extrapolation outside of the training domain. We demonstrate this approach by training a D‐MPNN model on interpolated vapor pressures and validate the model on an out‐of‐sample test set of energetic molecule vapor pressures, achieving accuracy comparable to those of experiments. [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]

Deep learning has shown great potential for generating molecules with desired properties. But the cost and time required to obtain relevant property data have limited study to only a few classes of materials for which extensive data have already been collected. We develop a deep learning method that combines a generative model with a property prediction model to fuse small data of one class of molecules with larger data in another class. Common low‐level physicochemical properties are jointly embedded into a latent space that can be used to design molecules in the smaller class. The chemical space around the molecules in the training set is explored through local gradient ascent optimization. Based on nine molecules from the original training set, nine new molecules are found to have improved properties while remaining structurally similar to the training molecules thereby easing requirements for entirely new synthesis routes. Validation is performed using an equilibrium thermochemistry code to verify the molecules and target properties. A specific example targeting the Chapman‐Jouguet velocity and small data for nitrogen‐rich molecules is shown. Despite the relative lack of nitrogen‐rich molecule data, the results demonstrate that fusing and joint embedding with plentiful low nitrogen molecular data can produce higher generative performance than using the scarce data alone. [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]

Clathrate hydrates are solid crystalline structures most commonly formed from solutions that have nucleated to form a mixed solid composed of water and gas. Understanding the mechanism of clathrate hydrate nucleation is essential to grasp the fundamental chemistry of these complex structures and their applications. Molecular dynamics (MD) simulation is an ideal method to study nucleation at the molecular level because the size of the critical nucleus and formation rate occur on the nano scale. Various analysis methods for nucleation have been developed through MD to analyze nucleation. In particular, the mean first-passage time (MFPT) and survival probability (SP) methods have proven to be effective in procuring the nucleation rate and critical nucleus size for monatomic systems. This study assesses the MFPT and SP methods, previously used for monatomic systems, when applied to analyzing clathrate hydrate nucleation. Because clathrate hydrate nucleation is relatively difficult to observe in MD simulations (due to its high free energy barrier), these methods have yet to be applied to clathrate hydrate systems. In this study, we have analyzed the nucleation rate and critical nucleus size of methane hydrate using MFPT and SP methods from data generated by MD simulations at 255 K and 50 MPa. MFPT was modified for clathrate hydrate from the original version by adding the maximum likelihood estimate and growth effect term. The nucleation rates calculated by MFPT and SP methods are within 5%, and the critical nucleus size estimated by the MFPT method was 50% higher, than values obtained through other more rigorous but computationally expensive estimates. These methods can also be extended to the analysis of other clathrate hydrates. [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 current knowledge and description of guest molecules within clathrate hydrates only accounts for occupancy within regular polyhedral water cages. Experimental measurements and simulations, examining the tert-butylamine+H2+H2O hydrate system, now suggest that H2 can also be incorporated within hydrate crystal structures by occupying interstitial sites, that is, locations other than the interior of regular polyhedral water cages. Specifically, H2 is found within the shared heptagonal faces of the large (43596273) cage and in cavities formed from the disruption of smaller (4454) water cages. The ability of H2 to occupy these interstitial sites and fluctuate position in the crystal lattice demonstrates the dynamic behavior of H2 in solids and reveals new insight into guest-guest and guest-host interactions in clathrate hydrates, with potential implications in increasing overall energy storage properties. [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.

Strain energy is a fundamental measure of the steric and configurational properties of organic molecules. The ability to estimate strain energy through quantum chemical simulations requires at minimum the knowledge of an initial set of nuclear coordinates. In general, such knowledge is not categorically known when screening or generating large numbers of molecule candidates in the context of molecular design. We present a machine learning approach to predict hydrocarbon strain energies using Benson group equivalents. A featurization strategy is crafted by concatenating the molecule group equivalent counts with easily computable molecular fingerprints. The data are obtained from electronic structure calculations we performed on a set of 166 previously synthesized strained hydrocarbons. These data are provided and include gas phase enthalpies of formation and associated optimized atomic coordinates. The strain energy prediction accuracy of several statistical learning methods is evaluated, and their respective merits and limitations are discussed.

To better understand the self-assembly of small molecules and nanoparticles adsorbed at interfaces, we have performed extensive Monte Carlo simulations of a simple lattice model based on the seven hard "tetrominoes", connected shapes that occupy four lattice sites. The equations of state of the pure fluids and all of the binary mixtures are determined over a wide range of density, and a large selection of multicomponent mixtures are also studied at selected conditions. Calculations are performed in the grand canonical ensemble and are analogous to real systems in which molecules or nanoparticles reversibly adsorb to a surface or interface from a bulk reservoir. The model studied is athermal; objects in these simulations avoid overlap but otherwise do not interact. As a result, all of the behavior observed is entropically driven. The one-component fluids all exhibit marked self-ordering tendencies at higher densities, with quite complex structures formed in some cases. Significant clustering of objects with the same rotational state (orientation) is also observed in some of the pure fluids. In all of the binary mixtures, the two species are fully miscible at large scales, but exhibit strong species-specific clustering (segregation) at small scales. This behavior persists in multicomponent mixtures; even in seven-component mixtures of all the shapes there is significant association between objects of the same shape. To better understand these phenomena, we calculate the second virial coefficients of the tetrominoes and related quantities, extract thermodynamic volume of mixing data from the simulations of binary mixtures, and determine Henry's law solubilities for each shape in a variety of solvents. The overall picture obtained is one in which complementarity of both the shapes of individual objects and the characteristic structures of different fluids are important in determining the overall behavior of a fluid of a given composition, with sometimes counterintuitive results. Finally, we note that no sharp phase transitions are observed but that this appears to be due to the small size of the objects considered. It is likely that complex phase behavior may be found in systems of larger polyominoes.

The use of evolutionary strategy optimizations in fitting empirical potentials against first-principles data is considered. Empirical potentials can involve a large number of interdependent quantities, the number varying with the complexity of the potential, and the optimization of these presents a challenging numerical problem. Evolutionary strategies are a general class of optimization methods that mimic natural selection by stochastically evolving a population of trial solutions according to rules that select for high values of some fitness function. In this work we apply a variety of evolutionary optimization methods to a representative "parametrization problem" in order to determine which such methods are well-suited to such applications. Prior work on the design of evolutionary strategies has generally focused on finding the extrema of relatively simple mathematical functions, and the findings of such studies may not be transferable to chemical applications of very high dimensionality. The test problem consists of parametrization of the Feuston-Garofalini all-atom potential developed for simulation of silicic acid oligomerization in aqueous solution (Feuston, B. P.; Garofalini, S. H. J. Phys. Chem. 1990, 94, 5351). "Meta-optimization" of the evolutionary method is first considered by fitting this potential against itself, using a wide variety of population sizes, recombination algorithms, mutation-size control methods, and selection methods. Simulated annealing is also considered as an alternative approach. Optimal choices of population size, recombination operator, mutation size control approach, and selection method are discussed, as well as the quantity of data required for the parametrization. It is clear from comparisons of multiple independent optimizations that, even when fitting this potential against itself, there are a considerable number of local extrema in the fitness function. Evolutionary methods are found to be competitive with simulated annealing and are more easily parallelized. Finally, the potential is reparametrized against reference data taken from a Car-Parrinello Molecular Dynamics trajectory of several relevant silicate species in aqueous solution, again using several variant algorithms.