Tim Mueller

High-performance transition metal–doped Pt3Ni octahedra for oxygen reduction reaction

Molybdenum doping drives high activity Platinum (Pt) is an effective catalyst of the oxygen reduction reaction in fuel cells but is scarce. One approach to extend Pt availability is to alloy it with more abundant metals such as nickel (Ni). Although these catalysts can be highly active, they are often not durable because of Ni loss. Huang et al. show that doping the surface of octahedral Pt3Ni nanocrystals with molybdenum not only leads to high activity (∼80 times that of a commercial catalyst) but enhances their stability. Science, this issue p. 1230 Molybdenum-doped platinum-nickel nanocrystal catalysts exhibit high activity and durability for a key fuel cell reaction. Bimetallic platinum-nickel (Pt-Ni) nanostructures represent an emerging class of electrocatalysts for oxygen reduction reaction (ORR) in fuel cells, but practical applications have been limited by catalytic activity and durability. We surface-doped Pt3Ni octahedra supported on carbon with transition metals, termed M‐Pt3Ni/C, where M is vanadium, chromium, manganese, iron, cobalt, molybdenum (Mo), tungsten, or rhenium. The Mo‐Pt3Ni/C showed the best ORR performance, with a specific activity of 10.3 mA/cm2 and mass activity of 6.98 A/mgPt, which are 81- and 73‐fold enhancements compared with the commercial Pt/C catalyst (0.127 mA/cm2 and 0.096 A/mgPt). Theoretical calculations suggest that Mo prefers subsurface positions near the particle edges in vacuum and surface vertex/edge sites in oxidizing conditions, where it enhances both the performance and the stability of the Pt3Ni catalyst.

Effect of particle size on hydrogen release from sodium alanate nanoparticles.

Density functional theory and the cluster expansion method are used to model 2-10 nm sodium alanate (NaAlH(4)) nanoparticles and related decomposition products Na(3)AlH(6), NaH, and Al. While bulk sodium alanate releases hydrogen in a two-step process, our calculations predict that below a certain size sodium alanate nanoparticles decompose in a single step directly to NaH, Al, and H(2) due to the effect of particle size on decomposition thermodynamics. This may explain why sodium alanate nanoparticles, unlike bulk sodium alanate, have been observed to release hydrogen in the operating temperature range of proton exchange membrane fuel cells. In addition, we identify low-energy surfaces that may be important for the dynamics of hydrogen storage and release from sodium alanate nanoparticles.

Roles of Mo Surface Dopants in Enhancing the ORR Performance of Octahedral PtNi Nanoparticles

Doping with a transition metal was recently shown to greatly boost the activity and durability of PtNi/C octahedral nanoparticles (NPs) for the oxygen reduction reaction (ORR), but its specific roles remain unclear. By combining electrochemistry, ex situ and in situ spectroscopic techniques, density functional theory calculations, and a newly developed kinetic Monte Carlo model, we showed that Mo atoms are preferentially located on the vertex and edge sites of Mo-PtNi/C in the form of oxides, which are stable within the wide potential window of the electrochemical cycle. These surface Mo oxides stabilize adjacent Pt sites, hereby stabilizing the octahedral shape enriched with (111) facets, and lead to increased concentration of Ni in subsurface layers where they are protected against acid dissolution. Consequently, the favorable Pt3Ni(111) structure for the ORR is stabilized on the surface of PtNi/C NPs in acid against voltage cycling. Significantly, the unusual potential-dependent oxygen coverage trend on Mo-doped PtNi/C NPs as revealed by the surface-sensitive Δμ analysis suggests that the Mo dopants may also improve the ORR kinetics by modifying the coordination environments of Pt atoms on the surface. Our studies point out a possible way to stabilize the favorable shape and composition established on conceptual catalytic models in practical nanoscale catalysts.

Investigation of a Quantum Monte Carlo Protocol To Achieve High Accuracy and High-Throughput Materials Formation Energies

High-throughput calculations based on density functional theory (DFT) methods have been widely implemented in the scientific community. However, depending on both the properties of interest as well as particular chemical/structural phase space, accuracy even for correct trends remains a key challenge for DFT. In this work, we evaluate the use of quantum Monte Carlo (QMC) to calculate material formation energies in a high-throughput environment. We test the performance of automated QMC calculations on 21 compounds with high quality reference data from the Committee on Data for Science and Technology (CODATA) thermodynamic database. We compare our approach to different DFT methods as well as different pseudopotentials, showing that errors in QMC calculations can be progressively improved especially when correct pseudopotentials are used. We determine a set of accurate pseudopotentials in QMC via a systematic investigation of multiple available pseudopotential libraries. We show that using this simple automated recipe, QMC calculations can outperform DFT calculations over a wide set of materials. Out of 21 compounds tested, chemical accuracy has been obtained in formation energies of 11 structures using our QMC recipe, compared to none using DFT calculations.

The Use of Cluster Expansions To Predict the Structures and Properties of Surfaces and Nanostructured Materials

The construction of cluster expansions parametrized by first-principles calculations is a powerful tool for calculating properties of materials. In this Perspective, we discuss the application of cluster expansions to surfaces and nanomaterials. We review the fundamentals of the cluster expansion formalism and how machine learning is used to improve the predictive accuracy of cluster expansions. We highlight several representative applications of cluster expansions to surfaces and nanomaterials, demonstrating how cluster expansions help researchers build structure-property relationships and enable rational design to accelerate the discovery of new materials. Potential applications and future challenges of cluster expansions are also discussed.

Identifying models of dielectric breakdown strength from high-throughput data via genetic programming

The identification of models capable of rapidly predicting material properties enables rapid screening of large numbers of materials and facilitates the design of new materials. One of the leading challenges for computational researchers is determining the best ways to analyze large material data sets to identify models that can rapidly predict a given property. In this paper, we demonstrate the use of genetic programming to generate simple models of dielectric breakdown based on 82 representative dielectric materials. We identified the band gap Eg and phonon cut-off frequency ωmax as the two most relevant features, and new classes of models featuring functions of Eg and ωmax were uncovered. The genetic programming approach was found to outperform other approaches for generating models, and we discuss some of the advantages of this approach.