RESUMEN
Redox potentials depend on the nature of the solvent/electrolyte through the solvation energies of the ionic solute species. For concentrated electrolytes, ion solvation may deviate significantly from the Born model predictions due to ion pairing and correlation effects. Recently, Ghorai and Matyushov [J. Phys. Chem. B 124, 3754-3769 (2020)] predicted, on the basis of linear response theory, an anomalous trend in the solvation energies of room temperature ionic liquids, with deviations of hundreds of kJ/mol from the Born model for certain size solutes/ions. In this work, we computationally evaluate ionic solvation energies in the prototypical ionic liquid, 1-butyl-3-methylimidazolium tetrafluoroborate (BMIM/BF4), to further explore this behavior and benchmark several of the approximations utilized in the solvation energy predictions. For comparison, we additionally compute solvation energies within acetonitrile and molten NaCl salt to illustrate the limiting behavior of purely dipolar and ionic solvents. We find that the overscreening effect, which results from the inherent charge oscillations of the ionic liquid, is substantially reduced in magnitude due to screening from the dipoles of the molecular ions. Therefore, for the molten NaCl salt, for which the ions do not have permanent dipoles, modulation of ionic solvation energies from the overscreening effect is most significant. The conclusion is that ionic liquids do indeed exhibit unique solvation behavior due to peak(s) in the electrical susceptibility caused by the ion shell structure; redox potential shifts for BMIM/BF4 are of more modest order â¼0.1 V, but may be larger for other ionic liquids that approach molten salt behavior.
RESUMEN
High carbon emissions have shown a strong correlation with rising global temperatures as the world's climate undergoes a dramatic shift. Work to mitigate the potential damage using materials such as metal-organic frameworks (MOFs), covalent organic frameworks (COFs), and polymer membranes (PMs) has proven successful in small-scale approaches; however, research is still being performed to enhance the capabilities of these materials for use at an industrial scale. One strategy for increasing performance is to embed these materials with CO2-philic molecules, which enhance selective binding over other gases. Calixarenes are promising candidates due to their large chalice shape, which allows for the possibility to bind multiple CO2 molecules per site. In this study, a dataset including 40 functionalized calixarene structures and one unfunctionalized (bare) calixarene was constructed with an automated, high-throughput structure generation through directed modifications to a molecular scaffold. A conformational search based on molecular mechanics allowed the faster determination of optimal binding energies for a vast array of chemical functional groups with less computational effort. Density functional theory and symmetry-adapted perturbation theory calculations were performed for the exploration of their interactions with CO2. Our work has identified new organic cages with increased CO2-philicity. In four cases, CO2 binding is stronger than 9.0 kcal/mol and very close to the targets set by previous studies. The nature of the noncovalent interactions for these cases is analyzed and discussed. Conclusions from this study can aid synthetic efforts for the next generation of functional materials.
RESUMEN
An amendment to this paper has been published and can be accessed via a link at the top of the paper.
RESUMEN
Machine learning and high-throughput computational screening have been valuable tools in accelerated first-principles screening for the discovery of the next generation of functionalized molecules and materials. The application of machine learning for chemical applications requires the conversion of molecular structures to a machine-readable format known as a molecular representation. The choice of such representations impacts the performance and outcomes of chemical machine learning methods. Herein, we present a new concise molecular representation derived from persistent homology, an applied branch of mathematics. We have demonstrated its applicability in a high-throughput computational screening of a large molecular database (GDB-9) with more than 133,000 organic molecules. Our target is to identify novel molecules that selectively interact with CO2. The methodology and performance of the novel molecular fingerprinting method is presented and the new chemically-driven persistence image representation is used to screen the GDB-9 database to suggest molecules and/or functional groups with enhanced properties.