Nongnuch Artrith

Understanding the Composition and Activity of Electrocatalytic Nanoalloys in Aqueous Solvents: A Combination of DFT and Accurate Neural Network Potentials

The shape, size, and composition of catalyst nanoparticles can have a significant influence on catalytic activity. Understanding such structure-reactivity relationships is crucial for the optimization of industrial catalysts and the design of novel catalysts with enhanced properties. In this letter, we employ a combination of first-principles computations and large-scale Monte-Carlo simulations with highly accurate neural network potentials to study the equilibrium surface structure and composition of bimetallic Au/Cu nanoparticles (NPs), which have recently been of interest as stable and efficient CO2 reduction catalysts. We demonstrate that the inclusion of explicit water molecules at a first-principles level of accuracy is necessary to predict experimentally observed trends in Au/Cu NP surface composition; in particular, we find that Au-coated core-shell NPs are thermodynamically favored in vacuum, independent of Au/Cu chemical potential and NP size, while NPs with mixed Au-Cu surfaces are preferred in aqueous solution. Furthermore, we show that both CO and O2 adsorption energies differ significantly for NPs with the equilibrium surface composition found in water and those with the equilibrium surface composition found in vacuum, suggesting large changes in CO2 reduction activity. Our results emphasize the importance of understanding and being able to predict the effects of catalytic environment on catalyst structure and activity. In addition, they demonstrate that first-principles-based neural network potentials provide a promising approach for accurately investigating the relationships between solvent, surface composition and morphology, surface electronic structure, and catalytic activity in systems composed of thousands of atoms.

Structure and dynamics of water confined in single-wall nanotubes.

The structure and dynamics of water confined in model single-wall carbon- and boron-nitride nanotubes (called SWCNT and SWBNNT, respectively) of different diameters have been investigated by molecular dynamics (MD) simulations at room temperature. The simulations were performed on periodically extended nanotubes filled with an amount of water that was determined by soaking a section of the nanotube in a water box in an NpT simulation (1 atm, 298 K). All MD production simulations were performed in the canonical (NVT) ensemble at a temperature of 298 K. Water was described by the extended simple point charge (SPC/E) model. The wall-water interactions were varied, within reasonable limits, to study the effect of a modified hydrophobicity of the pore walls. We report distribution functions for the water in the tubes in spherical and cylindrical coordinates and then look at the single-molecule dynamics, in particular self-diffusion. While this motion is slowed down in narrow tubes, in keeping with previous findings (Liu et al. J. Chem. Phys. 2005, 123, 234701-234707; Liu and Wang. Phys. Rev. 2005, 72, 085420/1-085420/4; Liu et al. Langmuir 2005, 21, 12025-12030) bulk-water like self-diffusion coefficients are found in wider tubes, more or less independently of the wall-water interaction. There may, however, be an anomaly in the self-diffusion for the SWBNNT.

Engineering Transition-Metal-Coated Tungsten Carbides for Efficient and Selective Electrochemical Reduction of CO2 to Methane

The design of catalysts for CO2 reduction is challenging because of the fundamental relationships between the binding energies of the reaction intermediates. Metal carbides have shown promise for transcending these relationships and enabling low-cost alternatives. Herein, we show that directional bonding arising from the mixed covalent/metallic character plays a critical role in governing the surface chemistry. This behavior can be described by consideration of individual d-band components. We use this model to predict efficient catalysts based on tungsten carbide with a sub-monolayer of iron adatoms. Our approach can be used to predict site-preference and binding-energy trends for complex catalyst surfaces.

Reduced overpotentials for electrocatalytic water splitting over Fe- and Ni-modified BaTiO3

Water electrolysis is a key technology for the replacement of fossil fuels by environmentally friendly alternatives, but state-of-the-art water oxidation catalysts rely on rare elements such as Pt groups and other noble metals. In this article, we employ first-principles calculations to explore the potential of modified barium titanate (BaTiO3), an inexpensive perovskite oxide that can be synthesized from earth-abundant precursors, for the design of efficient water oxidation electrocatalysts. Our calculations identify Fe and Ni doping as a means to improve the electrical conductivity and to reduce the overpotential required for water oxidation over BaTiO3. Based on computed Pourbaix diagrams and pH/potential-dependent surface phase diagrams, we further show that BaTiO3 is stable under reaction conditions and is not sensitive with respect to poisoning by reaction intermediates and hydrogen adsorption. This proof of concept demonstrates that even minor compositional modifications of existing materials may greatly improve their catalytic activity, a fact that is often neglected when larger composition spaces are screened.

Efficient and accurate machine-learning interpolation of atomic energies in compositions with many species

Author(s): Artrith, N; Urban, A; Ceder, G | Abstract:

Electronic-Structure Origin of Cation Disorder in Transition-Metal Oxides

Cation disorder is an important design criterion for technologically relevant transition-metal (TM) oxides, such as radiation-tolerant ceramics and Li-ion battery electrodes. In this Letter, we use a combination of first-principles calculations, normal mode analysis, and band-structure arguments to pinpoint a specific electronic-structure effect that influences the stability of disordered phases. We find that the electronic configuration of a TM ion determines to what extent the structural energy is affected by site distortions. This mechanism explains the stability of disordered phases with large ionic radius differences and provides a concrete guideline for the discovery of novel disordered compositions.