Effect of Dynamic Surface Polarization on the Oxidative Stability of Solvents in Nonaqueous Li-O2_2 Batteries

Polarization-induced renormalization of the frontier energy levels of interacting molecules and surfaces can cause significant shifts in the excitation and transport behavior of electrons. This phenomenon is crucial in determining the oxidative stability of nonaqeous electrolytes in high energy density electrochemical systems such as the Li-O$_2$ battery. On the basis of partially self-consistent first-principles ScGW0 calculations, we systematically study how the electronic energy levels of four commonly used solvent molecules, namely dimethylsulfoxide (DMSO), dimethoxyethane (DME), tetrahydrofuran (THF) and acetonitrile (ACN), renormalize when physisorbed on the different stable surfaces of Li$_2$O$_2$, the main discharge product. Using band level alignment arguments, we propose that the difference between the solvent's highest occupied molecular orbital (HOMO) level and the surface's valence band maximum (VBM) is a refined metric of oxidative stability. This metric and a previously used descriptor, solvent's gas phase HOMO level, agree quite well for physisorbed cases on pristine surfaces where ACN is oxidatively most stable followed by DME, THF and DMSO. However, this effect is intrinsically linked to the surface chemistry of solvent's interaction with the surfaces states and defects, and depends strongly on their nature. We conclusively show that the propensity of solvent molecules to oxidize will be significantly higher on Li$_2$O$_2$ surfaces with defects as compared to pristine surfaces. This suggests that the oxidatively stability of solvent is dynamic and is a strong function of surface electronic properties. Thus, while gas phase HOMO levels could be used for preliminary solvent candidate screening, a more refined picture of solvent stability requires mapping out the solvent stability as a function of the state of the surface under the operating conditions.Comment: 10 Pages, 8 Figure

Maximal predictability approach for identifying the right descriptors for electrocatalytic reactions

Density Functional Theory (DFT) calculations are being routinely used to identify new material candidates that approach activity near fundamental limits imposed by thermodynamics or scaling relations. DFT calculations have finite uncertainty and this raises an issue related to the ability to delineate materials that possess high activity. With the development of error estimation capabilities in DFT, there is an urgent need to propagate uncertainty through activity prediction models. In this work, we demonstrate a rigorous approach to propagate uncertainty within thermodynamic activity models. This maps the calculated activity into a probability distribution, and can be used to calculate the expectation value of the distribution, termed as the expected activity. We prove that the ability to distinguish materials increases with reducing uncertainty. We define a quantity, prediction efficiency, which provides a precise measure of the ability to distinguish the activity of materials for a reaction scheme over an activity range. We demonstrate the framework for 4 important electrochemical reactions, hydrogen evolution, chlorine evolution, oxygen reduction and oxygen evolution. We argue that future studies should utilize the expected activity and prediction efficiency to improve the likelihood of identifying material candidates that can possess high activity.Comment: 17 pages, 6 figures; 17 pages of Supporting Informatio

Quantifying Confidence in DFT Predicted Surface Pourbaix Diagrams of Transition Metal Electrode-Electrolyte Interfaces

Density Functional Theory (DFT) calculations have been widely used to predict the activity of catalysts based on the free energies of reaction intermediates. The incorporation of the state of the catalyst surface under the electrochemical operating conditions while constructing the free energy diagram is crucial, without which even trends in activity predictions could be imprecisely captured. Surface Pourbaix diagrams indicate the surface state as a function of the pH and the potential. In this work, we utilize error-estimation capabilities within the BEEF-vdW exchange correlation functional as an ensemble approach to propagate the uncertainty associated with the adsorption energetics in the construction of Pourbaix diagrams. Within this approach, surface-transition phase boundaries are no longer sharp and are therefore associated with a finite width. We determine the surface phase diagram for several transition metals under reaction conditions and electrode potentials relevant for the Oxygen Reduction Reaction (ORR). We observe that our surface phase predictions for most predominant species are in good agreement with cyclic voltammetry experiments and prior DFT studies. We use the OH$^*$ intermediate for comparing adsorption characteristics on Pt(111), Pt(100), Pd(111), Ir(111), Rh(111), and Ru(0001) since it has been shown to have a higher prediction efficiency relative to O$^*$, and find the trend Ru>Rh>Ir>Pt>Pd for (111) metal facets, where Ru binds OH$^*$ the strongest. We robustly predict the likely surface phase as a function of reaction conditions by associating c-values to quantifying the confidence in predictions within the Pourbaix diagram. We define a confidence quantifying metric using which certain experimentally observed surface phases and peak assignments can be better rationalized.Comment: 21 pages, 8 figures and Supporting Informatio