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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.

Organic materials with a high index of refraction (RI) are attracting considerable interest due to their potential application in optic and optoelectronic devices. However, most of these applications require an RI value of 1.7 or larger, while typical carbon-based polymers only exhibit values in the range of 1.3-1.5. This paper introduces an efficient computational protocol for the accurate prediction of RI values in polymers to facilitate in silico studies that can guide the discovery and design of next-generation high-RI materials. Our protocol is based on the Lorentz-Lorenz equation and is parametrized by the polarizability and number density values of a given candidate compound. In the proposed scheme, we compute the former using first-principles electronic structure theory and the latter using an approximation based on van der Waals volumes. The critical parameter in the number density approximation is the packing fraction of the bulk polymer, for which we have devised a machine learning model. We demonstrate the performance of the proposed RI protocol by testing its predictions against the experimentally known RI values of 112 optical polymers. Our approach to combine first-principles and data modeling emerges as both a successful and a highly economical path to determining the RI values for a wide range of organic polymers.

Bacterial infection remains an important risk factor after orthopedic surgery. The present paper reports the synthesis of hydroxyapatite-silver (HA-Ag) and carbon nanotube-silver (CNT-Ag) composites via spark plasma sintering (SPS) route. The retention of the initial phases after SPS was confirmed by phase analysis using X-ray diffraction and Raman spectroscopy. Energy dispersive spectrum analysis showed that Ag was distributed uniformly in the CNT/HA matrix. The breakage of CNTs into spheroid particles at higher temperatures (1700) is attributed to the Rayleigh instability criterion. Mechanical properties (hardness and elastic modulus) of the samples were evaluated using nanoindentation testing. Ag reinforcement resulted in the enhancement of hardness (by ~15%) and elastic modulus (~5%) of HA samples, whereas Ag reinforcement in CNT, Ag addition does not have much effect on hardness (0.3 GPa) and elastic modulus (5 GPa). The antibacterial tests performed using Escherichia coli and Staphylococcus epidermidis showed significant decrease (by ~65-86%) in the number of adhered bacteria in HA/CNT composites reinforced with 5% Ag nanoparticles. Thus, Ag-reinforced HA/CNT can serve as potential antibacterial biocomposites.

Computational pipeline for the accelerated discovery of organic materials with high refractive index via high-throughput screening and machine learning.