We introduce density functional theory and review recent progress in its application to transition metal chemistry. Topics covered include local, meta, hybrid, hybrid meta, and range-separated functionals, band theory, software, validation tests, and applications to spin states, magnetic exchange coupling, spectra, structure, reactivity, and catalysis, including molecules, clusters, nanoparticles, surfaces, and solids.

Density functional theory for transition metals and transition metal chemistry

Supported Catalysts Weiting Yu,† Marc D. Porosoff,† and Jingguang G. Chen*,†,‡,§ †Catalysis Center for Energy Innovation, Department of Chemical and Bimolecular Engineering, University of Delaware, Newark, Delaware 19716, United States ‡Department of Chemical Engineering, Columbia University, New York, New York 10027, United States Chemistry Department, Brookhaven National Laboratory, Upton, New York 11973, United States

Review of Pt-based bimetallic catalysis: from model surfaces to supported catalysts.

Monolayer bimetallic surfaces: Experimental and theoretical studies of trends in electronic and chemical properties

Hydrogenation of acetylene–ethylene mixtures over Pd and Pd–Ag alloys: First-principles-based kinetic Monte Carlo simulations

Reaction of NO and O2 to NO2 on Pt : Kinetics and catalyst deactivation

First-principles-based kinetic Monte Carlo simulation was used to track the elementary surface transformations involved in the catalytic decomposition of NO over Pt(100) and Rh(100) surfaces under lean-burn operating conditions. Density functional theory (DFT) calculations were carried out to establish the structure and energetics for all reactants, intermediates and products over Pt(100) and Rh(100). Lateral interactions which arise from neighbouring adsorbates were calculated by examining changes in the binding energies as a function of coverage and different coadsorbed configurations. These data were fitted to a bond order conservation (BOC) model which is subsequently used to establish the effects of coverage within the simulation. The intrinsic activation barriers for all the elementary reaction steps in the proposed mechanism of NO reduction over Pt(100) were calculated by using DFT. These values are corrected for coverage effects by using the parametrized BOC model internally within the simulation....

First-principles-based kinetic Monte Carlo simulation of nitric oxide decomposition over Pt and Rh surfaces under lean-burn conditions

An ab initio-based kinetic Monte Carlo algorithm was developed to simulate the direct decomposition of NO over Pt and different PtAu alloy surfaces. The algorithm was used to test the influence of the composition and the specific atomic surface structure of the alloy on the simulated activity and selectivity to form N2. The apparent activation barrier found for the simulation of lean NO decomposition over Pt(100) was 7.4 kcal/mol, which is lower than the experimental value of 11 kcal/mol that was determined over supported Pt nanoparticles. Differences are likely due to differences in the surface structure between the ideal (100) surface and supported Pt particles. The apparent reaction orders for lean NO decomposition over the Pt(100) substrate were calculated to be 0.9 and -0.5 for NO and O2, respectively. Oxygen acts to poison Pt. Simulations on the different Pt-Au(100) surface alloys indicate that the turnover frequency goes through a maximum as the Au composition in the surface is increased, and the maximum occurs near 44% Au. Turnover frequencies, however, are dictated by the actual arrangements of Pt and Au atoms in the surface rather than by their overall composition. Surfaces with similar compositions but different alloy arrangements can lead to very different activities. Surfaces composed of 50% Pt and 50% Au (Pt4 and Au4 surface ensembles) showed very little enhancement in the activity over that which was found over pure Pt. The Pt-Pt bridge sites required for NO adsorption and decomposition were still effectively poisoned by atomic oxygen. The well-dispersed Pt(50%)Au(50%) alloy, on the other hand, increased the TOF over that found for pure Pt by a factor of 2. The most active surface alloy was one in which the Pt was arranged into "+" ensembles surrounded by Au atoms. The overall composition of this surface is Pt(56.2%)Au(43.8%). The unique "+" ensembles maintain Pt bridge sites for NO to adsorb on but limit O2 as well as NO activation by eliminating next-nearest neighbor Pt-bridge sites. The repulsive interactions between two adatoms prevent them from sharing the same metal atoms. The decrease in the oxygen coverage leads to a greater number of vacant sites available for NO adsorption. This increases the NO coupling reaction and hence N2 formation. The inhibition of the rate of N2 formation by O2 is therefore suppressed. The coverage of atomic oxygen decreases from 53% on the Pt(100) surface down to 19% on the "+" ensemble surface. This increases the rate of N2 formation by a factor of 4.3 over that on pure Pt. The reaction kinetics over the "+" ensemble Pt(56.2%)Au(43.8%) surface indicate apparent reaction orders in NO and oxygen of 0.7 and 0.0, respectively. This suggests that oxygen does not poison the PtAu "+" alloy ensemble. The activity and selectivity of the PtAu ensembles significantly decrease for alloys that go beyond 60% Au. Higher coverages of Au shut down sites for NO adsorption and, in addition, weaken the NO and O bond strengths, which subsequently promotes desorption as well as NO oxidation. The computational approach identified herein can be used to more rapidly test different metal compositions and their explicit atomic arrangements for improved catalytic performance. This can be done "in silico" and thus provides a method that may aid high-throughput experimental efforts in the design of new materials. The synthesis and stability of the metal complexes suggested herein still ultimately need to be tested.

Screening by Kinetic Monte Carlo Simulation of Pt−Au(100) Surfaces for the Steady-State Decomposition of Nitric Oxide in Excess Dioxygen†

A catalyst development engine for providing a rapid approach to the rational development of scalable heterogeneous catalysts and of high-performance solid materials. The catalyst development engine includes three main components: the high-throughput data generation cycle, the knowledge generation cycle, and the knowledge repository or database. The high-throughput data generation cycle rapidly generates high quality data in a high-throughput mode for the virtual and experimental evaluation of new catalytic materials and also generates data on well-characterized systems for use in the knowledge generation cycle. The knowledge generation cycle generates working hypotheses, relating performance to key catalyst properties, which guide the search for better catalysts, and generates fundamental structure-property relationships required in order to select catalysts for further experimental evaluation in the high-throughput cycle. Both cycles run concurrently with the theoretical and experimental modules highly integrated. Catalyst synthesis modules use methods such as impregnation, precipitation, and others. Catalyst evaluation is done free of transport limitations to generate kinetic data which can be used in the knowledge cycle. Such an approach accelerates development and scale-up of new materials without the impediments introduced by conventional combinatorial approaches based on randomly selected materials prepared under unrealistic and difficult to scale up conditions.

Knowledge-based process for the development of materials

A catalyst development engine (CDE) provides a rapid approach to the rational development of scalable heterogeneous catalysts and of high-performance solid materials The CDE includes three main components: the testing cycle, the knowledge cycle, and the knowledge repository or database The knowledge cycle generates working hypotheses relating performance to key catalyst properties via machine learning methods, computation chemistry and micro-kinetic modeling Such an approach accelerates development and scale-up of new materials without the impediments introduced by conventional combinatorial approaches based on randomly selected materials

Method and system for the development of materials

L'invention se rapporte a un moteur de developpement de catalyseur (CDE) qui permet une approche rapide du developpement rationnel des catalyseurs heterogenes echelonnables et des materiaux solides a performance elevee. Ce CDE comprend trois composants principaux: le cycle d'essai, le cycle de connaissance, et le referentiel ou la base de donnees de connaissance. Le cycle de connaissance genere des hypotheses de travail qui relient la performance aux proprietes clefs du catalyseur au moyen de procedes d'apprentissage par machine, de calculs chimiques et de la modelisation micro-cinetique. Une telle approche permet d'accelerer le developpement et l'extrapolation de nouveaux materiaux sans rencontrer les obstacles inherents aux approches combinatoires conventionnelles fondees sur la selection aleatoire des materiaux.

Laurent D. Kieken

Papers

First-principles-based kinetic Monte Carlo simulation of nitric oxide decomposition over Pt and Rh surfaces under lean-burn conditions

Screening by Kinetic Monte Carlo Simulation of Pt−Au(100) Surfaces for the Steady-State Decomposition of Nitric Oxide in Excess Dioxygen†

Knowledge-based process for the development of materials

Method and system for the development of materials

Procede et systeme de developpement de materiaux