In an industrial crystallization process, crystal shape strongly influences end-product quality and functionality as well as downstream processing. Additionally, nucleation events, solvent effects and polymorph selection play critical roles in both the design and operation of a crystallization plant and the patentability of the product and process. Therefore, investigation of these issues with respect to a priori prediction is and will continue to be an important avenue of research.In this review, we discuss the state-of-the-art in modeling crystallization processes over a range of length scales relevant to nucleation through process design. We also identify opportunities for continued research and specific areas where significant advancements are needed.

The composites industry is increasingly using molecular dynamics (MD) simulations to inform its materials development decisions. As a result, there is growing awareness that simulated predictions require quantitative assessments of their quality in order to routinely provide reliable and actionable information. In the following, we develop a suite of uncertainty quantification (UQ) tools designed to assess simulation-based estimates of the glass transition temperature T g of polymer systems for aerospace applications. We consider contributions to this uncertainty arising from: (i) identification of asymptotic regimes in density versus temperature relations; (ii) fluctuations associated with limited time-averaging of dynamical noise; (iii) and finite-size effects associated with partial averaging over polymer-network configurations. We present a sequence of analyses by which we assess each of these contributions and quantify their net effect on estimates of T g. Importantly, these methods suggest more efficient workflows by indicating when multiple small simulations can be combined to yield estimates with uncertainties comparable to larger, more expensive simulations. We expect that related approaches will, in the future, be applicable to other physical quantities of interest as well as to a broader class of computational tools.

Recent advances in graphics processing unit (GPU) hardware and improved efficiencies of atomistic simulation programs allow for the screening of a large number of polymers to predict properties that require running and analyzing long molecular dynamics (MD) trajectories. This paper outlines a MD simulation workflow based on GPU MD simulation and the refined optimized potentials for liquid simulation (OPLS) OPLS3e force field to calculate glass transition temperatures (T gs) of 315 polymers for which Bicerano reported experimental values [BiceranoJ. Bicerano, J. Prediction of Polymer PropertiesMarcel Dekker Inc.New York1996]. Applying the workflow across this large set of polymers allowed for a comprehensive evaluation of the protocol performance and helped in understanding its merits and limitations. We observe a consistent trend between predicted T g values and empirical observation across several subsets of polymers. Thus, the protocol established in this work is promising for exploring targeted chemical spaces and aids in the evaluation of polymers for various applications, including composites, coatings, electrical casings, etc. During the stepwise cooling simulation for the calculation of T g, a subset of polymers clearly showed an ordered structure developing as the temperature decreased. Such polymers have a point of discontinuity on the specific volume vs temperature plot, which we associate with the melting temperature (T m). We demonstrate the distinction between crystallized and amorphous polymers by examining polyethylene. Linear polyethylene shows a discontinuity in the specific volume vs temperature plot, but we do not observe the discontinuity for branched polyethylene simulations.

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In this work, we demonstrate the use of the Green-Kubo integral of the heat flux autocorrelation function, incorporating long-range corrections to model the thermal conductivity versus temperature relationship of cross-linked polymers. The simulations were performed on a cross-linked epoxy made from DGEBA and a curing agent (diamino diphenyl sulfone) using a consistent valence force field (CVFF). A dendrimeric approach was utilized for building equilibrated cross-linked structures that allowed replication of the experimental dilatometric curve for the epoxy system. We demonstrate that the inclusion of a long-range correction within the Ewald/PPPM approach brings the results close to experimentally measured conductivity within an error of 10% while providing a good prediction of the relationship of thermal conductivity versus temperature. This method shows significant promise towards the computation of thermal conductivity from simulations even before synthesis of the polymer for purposes of materials by design.