Challenges for density functional theory: calculation of CO adsorption on electrocatalytically relevant metals.

Density Functional Theory (DFT) is currently the most tractable choice of theoretical model used to understand the mechanistic pathways for electrocatalytic processes such as CO2 or CO reduction. Here, we assess the performance of two DFT functionals designed specifically to describe surface interactions, RTPSS and RPBE, as well as two popular meta-GGA functionals, SCAN and B97M-rV, that have not been a priori optimized for better interfacial properties. We assess all four functionals against available experimental data for prediction of bulk and bare surface properties on four electrocatalytically relevant metals, Au, Ag, Cu, and Pt, and for binding CO to surfaces of these metals. To partially mitigate issues such as thermal and anharmonic corrections associated with comparing computations with experiments, molecular benchmarks against high level quantum chemistry are reported for CO complexes with Au, Ag, Cu and Pt atoms, as well as the CO-water complex and the water dimer. Overall, we find that the surface modified RPBE functional performs reliably for many of the benchmarks examined here, and the meta-GGA functionals also show promising results. Specifically B97M-rV predicts the correct site preference for CO binding on Ag and Au (the only functional tested here to do so), while RTPSS performs well for surface relaxations and binding of CO on Pt and Cu.

Electronic structure calculations permit identification of the driving forces behind frequency shifts in transition metal monocarbonyls.

We report the adiabatic energy decomposition analysis (EDA) of density functional theory (DFT) results, shedding light on the physical content of binding energies and carbon monoxide (CO) frequency (υCO) shifts in select first-row transition metal monocarbonyls (MCOs; M = Ti-, V-, Cr-, Co-, Ni-, Cu-, V, Cr, Mn, Ni, Cu, Zn, Cr+, Mn+, Fe+, Cu+, and Zn+). This approach allows for the direct decomposition of υCO, in contrast to previous studies of these systems. Neutral, anionic, and cationic systems are compared, and our results indicate that the relative importance of electrostatic interactions, intramolecular orbital polarization, and charge transfer can vary significantly with the charge and electron configuration of the metal participating in binding. Various anomalous systems are also discussed and incorporated into a general model of MCO binding. Electrostatic interactions and orbital polarization are found to promote blue shifts in υCO, while charge transfer effects encourage υCO red-shifting; previously reported values of υCO are found to be a result of a complex but quantifiable interplay between these physical components. Our computations indicate that CuCO- and ZnCO possess triplet ground states, and also that CrCO- exhibits a non-linear geometry, all in contrast to previous computational results. Advantages and limitations of this model as an approximation to more complicated systems, like those implicated in heterogeneous catalysis, are discussed. We also report benchmark results for MCO geometries, binding energies, and harmonic CO frequencies, and discuss the validity of single-reference wave function and DFT approaches to the study of these transition metal systems.

Equilibria and Rate Phenomena from Atomistic to Mesoscale: Simulation Studies of Magnetite.

Batteries are dynamic devices composed of multiple components that operate far from equilibrium and may operate under extreme stress and varying loads. Studies of isolated battery components are valuable to the fundamental understanding of the physical processes occurring within each constituent element. When the components are integrated into a full device and operated under realistic conditions, it can be difficult to decouple the physical processes that occur across multiple interfaces and multiple length scales. Thus, the physical processes studied in isolated components may change in a full battery setup or may be irrelevant to performance. Simulation studies on many length scales play a key role in the analysis of experiments and in the elucidation of the relevant physical processes impacting performance. In this Account, we aim to highlight the use of modeling on multiple length scales to identify rate limiting phenomena in lithium-ion batteries. To illustrate the utility of modeling, we examine lithium-ion batteries with nanostructured magnetite, Fe3O4, as the positive electrode active material against a solid Li0 negative electrode. Due to continuous operation away from equilibrium, batteries exhibit highly nonideal behavior, and a model that aims to reproduce behavior under realistic operating conditions must be able to capture the physics occurring on the length scales relevant to the performance of the system. It is our experience that limiting behavior in lithium-ion batteries can be observed on the atomic scale and up through the electrode scale and thus, predictive models must be capable of integrating and communicating physics across multiple length scales. Magnetite is studied as an electrode material for lithium-ion batteries, but it is found to suffer from slow solid-state transport of lithium, slow reaction kinetics, and poor cycling. Magnetite (Fe3O4) is a material capable of undergoing multiple electron transfers (MET), and can accept up to eight lithium per formula unit (Li8Fe3O4). Magnetite, (Fe8a3+)[Fe3+Fe2+]16dO4,32e2-, has a close-packed inverse spinel structure and undergoes intercalation, structural rearrangement, and conversion reactions upon full lithiation. (1) To overcome solid-state transport resistances, magnetite can be nanostructured to decrease Li+ diffusion lengths, and this has been shown to increase capacity. Additionally, unique architectures incorporating both carbon and Fe3O4 have shown to alleviate transport and cycling issues in the material. (2) Here, we solely address traditional composite electrodes, in which Fe3O4 is synthesized as nanoparticles and combined with additives to fabricate the electrode. In the case of nanoparticulate magnetite, it has been found that the electrode fabrication process results in the formation of micrometer-sized agglomerates of the Fe3O4 nanoparticles, introducing a secondary structural motif. The agglomerates may form in one or more fabrication processes, and their elimination may not be straightforward or warranted. Here, we highlight the impact of these secondary formations on the performance of the Fe3O4 lithium-ion battery. We illustrate how simulations can be used to design experiments, prioritize research efforts, and predict performance.

Critical Role of Thermal Fluctuations for CO Binding on Electrocatalytic Metal Surfaces.

This work considers the evaluation of density functional theory (DFT) when comparing against experimental observations of CO binding trends on the strong binding Pt(111) and intermediate binding Cu(111) and for weak binding Ag(111) and Au(111) surfaces important in electrocatalysis. By introducing thermal fluctuations using appropriate statistical mechanical NVT and NPT ensembles, we find that the RPBE and B97M-rV DFT functionals yield qualitatively better metal surface strain trends and CO enthalpies of binding for Cu(111) and Pt(111) than found at 0 K, thereby correcting the overbinding by 0.2 to 0.3 eV to yield better agreement with the enthalpies determined from experiment. The importance of dispersion effects are manifest for the weak CO binding Ag(111) and Au(111) surfaces at finite temperatures in which the RPBE functional does not bind CO at all, while the B97M-rV functional shows that the CO-metal interactions are a mixture of chemisorbed and physisorbed species with binding enthalpies that are within ∼0.05 eV of experiment. Across all M(111) surfaces, we show that the B97M-rV functional consistently predicts the correct atop site preference for all metals due to thermally induced surface distortions that preferentially favor the undercoordinated site. This study demonstrates the need to fully account for finite temperature fluctuations to make contact with the binding enthalpies from surface science experiments and electrocatalysis applications.

Investigating the Complex Chemistry of Functional Energy Storage Systems: The Need for an Integrative, Multiscale (Molecular to Mesoscale) Perspective.

Electric energy storage systems such as batteries can significantly impact society in a variety of ways, including facilitating the widespread deployment of portable electronic devices, enabling the use of renewable energy generation for local off grid situations and providing the basis of highly efficient power grids integrated with energy production, large stationary batteries, and the excess capacity from electric vehicles. A critical challenge for electric energy storage is understanding the basic science associated with the gap between the usable output of energy storage systems and their theoretical energy contents. The goal of overcoming this inefficiency is to achieve more useful work (w) and minimize the generation of waste heat (q). Minimization of inefficiency can be approached at the macro level, where bulk parameters are identified and manipulated, with optimization as an ultimate goal. However, such a strategy may not provide insight toward the complexities of electric energy storage, especially the inherent heterogeneity of ion and electron flux contributing to the local resistances at numerous interfaces found at several scale lengths within a battery. Thus, the ability to predict and ultimately tune these complex systems to specific applications, both current and future, demands not just parametrization at the bulk scale but rather specific experimentation and understanding over multiple length scales within the same battery system, from the molecular scale to the mesoscale. Herein, we provide a case study examining the insights and implications from multiscale investigations of a prospective battery material, Fe3O4.