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Ionic liquids (ILs) are well classified as designer solvents based on the ease of tailoring their properties through modifying the chemical structure of the cation and anion. However, while many structure–property relationships have been developed, these generally only identify the most dominant trends. Here, we have used machine learning on existing experimental data to construct robust models to produce meaningful predictions across a broad range of cation and anion chemical structures. Specifically, we used previously collated experimental data for the viscosity and conductivity of protic ILs [T. L. Greaves and C. J. Drummond, Chem. Rev. 115, 11379–11448 (2015)] as the inputs for multiple linear regression and neural network models. These were then used to predict the properties of all 1827 possible cation–anion combinations (excluding the input combinations). These models included the effect of water content of up to 5 wt. %. A selection of ten new protic ILs was then prepared, which validated the usefulness of the models. Overall, this work shows that relatively sparse data can be used productively to predict physicochemical properties of vast arrays of ILs. [ABSTRACT FROM AUTHOR]

Metal-Organic frameworks (MOFs) have been considered for various gas storage and separation applications. Theoretically, there are an infinite number of MOFs that can be created; however, a finite amount of resources are available to evaluate each one. Computational methods can be adapted to expedite the process of evaluation. In the context of CO2 capture, this paper investigates the method of screening MOFs using machine learning trained on molecular simulation data. New descriptors are introduced to aid this process. Using all descriptors, it is shown that machine learning can predict the CO2 adsorption, with an R2 of above 0.9. The introduced Effective Point Charge (EPoCh) descriptors, which assign values to frameworks' partial charges based on the expected CO2 uptake of an equivalent point charge in isolation, are shown to be the second most important group of descriptors, behind the Henry coefficient. Furthermore, the EPoCh descriptors are hundreds of thousands of times faster to obtain compared with the Henry coefficient, and they achieve similar results when identifying top candidates for CO2 capture using pseudo-classification predictions. Computational methods enable the rapid evaluation of the gas uptake capabilities of metal–organic frameworks. Here, MOFs are screened for their CO2 capture abilities using machine learning trained on molecular simulation data, with new effective point charge descriptors introduced to aid this process. [ABSTRACT FROM AUTHOR]

The emergence of liquid metal catalysts endows electrocatalytic processes with enhanced efficiencies by eliminating coking issues that rapidly deactivate conventional solid catalysts. Alloying them with trace amounts of redox-active metal additives can further improve their catalytic activity. Here we report a vanadium-liquid metal alloy with superior activity that can drive electrochemical reduction of CO2 into solid carbon. The reaction mechanism has been thoroughly investigated and discussed. In addition, artificial neural networks trained by machine learning have been demonstrated to be powerful in predicting performance of LM alloys with various compositions. [ABSTRACT FROM AUTHOR]

Nonequilibrium molecular dynamics simulations were performed for a family of hyperbranched polymers of the same molecular weight but with different chain lengths between branches. Microscopic structural properties including mean squared radius of gyration, distribution of beads from the center of mass and from the core and the interpenetration function of these systems were characterized. A relationship between the zero shear rate mean squared radius of gyration and the Wiener index was established. The molecular and bond alignment tensors were analyzed to characterize the flow birefringence of these hyperbranched polymers. The melt rheology was also studied and the crossover from the Newtonian to non-Newtonian behavior was captured for all polymer fluids in the considered range of strain rates. Rheological properties including the shear viscosity and normal stress coefficients obtained from constant pressure simulations were found to be the same as those from constant volume simulations except at high strain rates due to shear dilatancy. A linear dependence of zero shear rate viscosities on the number of spacer units was found. The stress optical rule was shown to be valid at low strain rates with the stress optical coefficient of approximately 3.2 independent of the topologies of polymers. [ABSTRACT FROM AUTHOR]

Hyperbranched polymer melts have been simulated using a coarse-grained model and nonequilibrium molecular dynamics (NEMD) techniques. In order to determine the shear-induced changes in the structural properties of hyperbranched polymers, various parameters were calculated at different strain rates. The radii of gyration which characterize the size of the polymer were evaluated. The tensor of gyration was analyzed and results indicate that hyperbranched polymer molecules have a prolate ellipsoid shape under shear. As hyperbranched polymers have compact, highly branched architecture and layers of beads have increasing densities which might lead to an unusual distribution of mass, the distribution of beads was also studied. The distribution of terminal beads was investigated to understand the spatial arrangement of these groups which is very important for hyperbranched polymer applications, especially in drug delivery. [ABSTRACT FROM AUTHOR]