Benchmarking water adsorption on metal surfaces with ab initio molecular dynamics.

Solid-water interfaces are ubiquitous in nature and technology. In particular, technologies evolving in the green transition, such as electrocatalysis, heavily rely on the junction of an electrolyte and an electrode as a central part of the device. For the understanding of atomic-scale processes taking place at the electrolyte-electrode interface, density functional theory (DFT) has become the de facto standard. The validation of DFT's ability to simulate the interfacial solid/water interaction is crucial, and ideal simulation setups need to be identified in order to prevent avoidable systematic errors. Here, we develop a rigorous sampling protocol for benchmarking the adsorption/desorption energetics of water on metallic surfaces against experimental temperature programmed desorption, single crystal adsorption calorimetry, and thermal energy atom scattering. We screened DFT's quality on a series of transition metal surfaces, applying three of the most common exchange-correlation approximations: PBE-D3, RPBE-D3, and BEEF-vdW. We find that all three xc-functionals reflect the pseudo-zeroth order desorption of water rooted in the combination of attractive adsorbate-adsorbate interactions and their saturation at low and intermediate coverages, respectively. However, both RPBE-D3 and BEEF-vdW lead to more accurate water adsorption strengths, while PBE-D3 clearly overbinds near-surface water. We relate the variations in binding strength to specific variations in water-metal and water-water interactions, highlighting the structural consequences inherent in an uninformed choice of simulation parameters. Our study gives atomistic insight into water's complex adsorption equilibrium. Furthermore, it represents a guideline for future DFT-based simulations of solvated solid interfaces by providing an assessment of systematic errors in specific setups.

GPAW: An open Python package for electronic structure calculations.

We review the GPAW open-source Python package for electronic structure calculations. GPAW is based on the projector-augmented wave method and can solve the self-consistent density functional theory (DFT) equations using three different wave-function representations, namely real-space grids, plane waves, and numerical atomic orbitals. The three representations are complementary and mutually independent and can be connected by transformations via the real-space grid. This multi-basis feature renders GPAW highly versatile and unique among similar codes. By virtue of its modular structure, the GPAW code constitutes an ideal platform for the implementation of new features and methodologies. Moreover, it is well integrated with the Atomic Simulation Environment (ASE), providing a flexible and dynamic user interface. In addition to ground-state DFT calculations, GPAW supports many-body GW band structures, optical excitations from the Bethe-Salpeter Equation, variational calculations of excited states in molecules and solids via direct optimization, and real-time propagation of the Kohn-Sham equations within time-dependent DFT. A range of more advanced methods to describe magnetic excitations and non-collinear magnetism in solids are also now available. In addition, GPAW can calculate non-linear optical tensors of solids, charged crystal point defects, and much more. Recently, support for graphics processing unit (GPU) acceleration has been achieved with minor modifications to the GPAW code thanks to the CuPy library. We end the review with an outlook, describing some future plans for GPAW.

Enriching Surface-Accessible CO2 in the Zero-Gap Anion-Exchange-Membrane-Based CO2 Electrolyzer.

Zero-gap anion exchange membrane (AEM)-based CO2 electrolysis is a promising technology for CO production, however, their performance at elevated current densities still suffers from the low local CO2 concentration due to heavy CO2 neutralization. Herein, via modulating the CO2 feed mode and quantitative analyzing CO2 utilization with the aid of mass transport modeling, we develop a descriptor denoted as the surface-accessible CO2 concentration ([CO2 ]SA ), which enables us to indicate the transient state of the local [CO2 ]/[OH- ] ratio and helps define the limits of CO2 -to-CO conversion. To enrich the [CO2 ]SA , we developed three general strategies: (1) increasing catalyst layer thickness, (2) elevating CO2 pressure, and (3) applying a pulsed electrochemical (PE) method. Notably, an optimized PE method allows to keep the [CO2 ]SA at a high level by utilizing the dynamic balance period of CO2 neutralization. A maximum jCO of 368±28 mA cmgeo -2 was achieved using a commercial silver catalyst.

MCML: Combining physical constraints with experimental data for a multi-purpose meta-generalized gradient approximation.

The predictive power of density functional theory for materials properties can be improved without increasing the overall computational complexity by extending the generalized gradient approximation (GGA) for electronic exchange and correlation to density functionals depending on the electronic kinetic energy density in addition to the charge density and its gradient, resulting in a meta-GGA. Here, we propose an empirical meta-GGA model that is based both on physical constraints and on experimental and quantum chemistry reference data. The resulting optimized meta-GGA MCML yields improved surface and gas phase reaction energetics without sacrificing the accuracy of bulk property predictions of existing meta-GGA approaches.

DFT simulation of the X-ray diffraction pattern of aluminum-ion-intercalated graphite used as the cathode material of the aluminum-ion battery.

Many breakthroughs have been achieved in rechargeable aluminum-ion battery technologies in recent years. Most recently, operando X-ray diffraction (XRD) combined with density functional theory (DFT) calculations was reported to study the chloroaluminate anion (AlCl4-)-intercalated graphite cathode of the battery. However, there are quite a few discrepancies between the measured and simulated XRD patterns. This work is focused on the simulation of XRD patterns of graphite intercalation compounds (GICs) with DFT calculations. Our results reveal that both the geometry of AlCl4- in graphite and the gallery height of GICs are dependent on the intercalant density. At low intercalant density, the gallery height keeps constant, but at high intercalant densities, the gallery height is linearly related to the intercalant density. Our simulated XRD patterns are highly consistent with the measured operando XRD patterns. Not only do the angles of the peaks match very well, but also the relative intensities and the corresponding electrode capacities show reasonable agreement with the experimental results. The DFT simulation of the XRD pattern provides significant information on the stage index and the charge capacity of the GIC electrode.

Machine learning with bond information for local structure optimizations in surface science.

Local optimization of adsorption systems inherently involves different scales: within the substrate, within the molecule, and between the molecule and the substrate. In this work, we show how the explicit modeling of different characteristics of the bonds in these systems improves the performance of machine learning methods for optimization. We introduce an anisotropic kernel in the Gaussian process regression framework that guides the search for the local minimum, and we show its overall good performance across different types of atomic systems. The method shows a speed-up of up to a factor of two compared with the fastest standard optimization methods on adsorption systems. Additionally, we show that a limited memory approach is not only beneficial in terms of overall computational resources but can also result in a further reduction of energy and force calculations.

Low-Scaling Algorithm for Nudged Elastic Band Calculations Using a Surrogate Machine Learning Model.

We present the incorporation of a surrogate Gaussian process regression (GPR) atomistic model to greatly accelerate the rate of convergence of classical nudged elastic band (NEB) calculations. In our surrogate model approach, the cost of converging the elastic band no longer scales with the number of moving images on the path. This provides a far more efficient and robust transition state search. In contrast to a conventional NEB calculation, the algorithm presented here eliminates any need for manipulating the number of images to obtain a converged result. This is achieved by inventing a new convergence criteria that exploits the probabilistic nature of the GPR to use uncertainty estimates of all images in combination with the force in the saddle point in the target model potential. Our method is an order of magnitude faster in terms of function evaluations than the conventional NEB method with no accuracy loss for the converged energy barrier values.

Graph Theory Approach to High-Throughput Surface Adsorption Structure Generation.

We present a methodology for graph based enumeration of surfaces and unique chemical adsorption structures bonded to those surfaces. Utilizing the graph produced from a bulk structure, we create a unique graph representation for any general slab cleave and further extend that representation to include a large variety of catalytically relevant adsorbed molecules. We also demonstrate simple geometric procedures to generate 3D initial guesses of these enumerated structures. While generally useful for generating a wide variety of structures used in computational surface science and heterogeneous catalysis, these techniques are also key to facilitating an informatics approach to the high-throughput search for more effective catalysts.

Bottom-Up Design of a Copper-Ruthenium Nanoparticulate Catalyst for Low-Temperature Ammonia Oxidation.

A novel nanoparticulate catalyst of copper (Cu) and ruthenium (Ru) was designed for low-temperature ammonia oxidation at near-stoichiometric mixtures using a bottom-up approach. A synergistic effect of the two metals was found. An optimum CuRu catalyst presents a reaction rate threefold higher than that for Ru and forty-fold higher than that for Cu. X-ray absorption spectroscopy suggests that in the most active catalyst Cu forms one or two monolayer thick patches on Ru and the catalysts are less active once 3D Cu islands form. The good performance of the tuned Cu/Ru catalyst is attributed to changes in the electronic structure, and thus the altered adsorption properties of the surface Cu sites.

Intrinsic Selectivity and Structure Sensitivity of Rhodium Catalysts for C(2+) Oxygenate Production.

Synthesis gas (CO + H2) conversion is a promising route to converting coal, natural gas, or biomass into synthetic liquid fuels. Rhodium has long been studied as it is the only elemental catalyst that has demonstrated selectivity to ethanol and other C2+ oxygenates. However, the fundamentals of syngas conversion over rhodium are still debated. In this work a microkinetic model is developed for conversion of CO and H2 into methane, ethanol, and acetaldehyde on the Rh (211) and (111) surfaces, chosen to describe steps and close-packed facets on catalyst particles. The model is based on DFT calculations using the BEEF-vdW functional. The mean-field kinetic model includes lateral adsorbate-adsorbate interactions, and the BEEF-vdW error estimation ensemble is used to propagate error from the DFT calculations to the predicted rates. The model shows the Rh(211) surface to be ∼6 orders of magnitude more active than the Rh(111) surface, but highly selective toward methane, while the Rh(111) surface is intrinsically selective toward acetaldehyde. A variety of Rh/SiO2 catalysts are synthesized, tested for catalytic oxygenate production, and characterized using TEM. The experimental results indicate that the Rh(111) surface is intrinsically selective toward acetaldehyde, and a strong inverse correlation between catalytic activity and oxygenate selectivity is observed. Furthermore, iron impurities are shown to play a key role in modulating the selectivity of Rh/SiO2 catalysts toward ethanol. The experimental observations are consistent with the structure-sensitivity predicted from theory. This work provides an improved atomic-scale understanding and new insight into the mechanism, active site, and intrinsic selectivity of syngas conversion over rhodium catalysts and may also guide rational design of alloy catalysts made from more abundant elements.

Nickel-silver alloy electrocatalysts for hydrogen evolution and oxidation in an alkaline electrolyte.

The development of improved catalysts for the hydrogen evolution reaction (HER) and hydrogen oxidation reaction (HOR) in basic electrolytes remains a major technical obstacle to improved fuel cells, water electrolyzers, and other devices for electrochemical energy storage and conversion. Based on the free energy of adsorbed hydrogen intermediates, theory predicts that alloys of nickel and silver are active for these reactions. In this work, we synthesize binary nickel-silver bulk alloys across a range of compositions and show that nickel-silver alloys are indeed more active than pure nickel for hydrogen evolution and, possibly, hydrogen oxidation. To overcome the mutual insolubility of silver and nickel, we employ electron-beam physical vapor codeposition, a low-temperature synthetic route to metastable alloys. This method also produces flat and uniform films that facilitate the measurement of intrinsic catalytic activity with minimal variations in the surface area, ohmic contact, and pore transport. Rotating-disk-electrode measurements demonstrate that the hydrogen evolution activity per geometric area of the most active catalyst in this study, Ni0.75Ag0.25, is approximately twice that of pure nickel and has comparable stability and hydrogen oxidation activity. Our experimental results are supported by density functional theory calculations, which show that bulk alloying of Ni and Ag creates a variety of adsorption sites, some of which have near-optimal hydrogen binding energy.

mBEEF: an accurate semi-local Bayesian error estimation density functional.

We present a general-purpose meta-generalized gradient approximation (MGGA) exchange-correlation functional generated within the Bayesian error estimation functional framework [J. Wellendorff, K. T. Lundgaard, A. Møgelhøj, V. Petzold, D. D. Landis, J. K. Nørskov, T. Bligaard, and K. W. Jacobsen, Phys. Rev. B 85, 235149 (2012)]. The functional is designed to give reasonably accurate density functional theory (DFT) predictions of a broad range of properties in materials physics and chemistry, while exhibiting a high degree of transferability. Particularly, it improves upon solid cohesive energies and lattice constants over the BEEF-vdW functional without compromising high performance on adsorption and reaction energies. We thus expect it to be particularly well-suited for studies in surface science and catalysis. An ensemble of functionals for error estimation in DFT is an intrinsic feature of exchange-correlation models designed this way, and we show how the Bayesian ensemble may provide a systematic analysis of the reliability of DFT based simulations.

Density functional theory in surface chemistry and catalysis.

Recent advances in the understanding of reactivity trends for chemistry at transition-metal surfaces have enabled in silico design of heterogeneous catalysts in a few cases. The current status of the field is discussed with an emphasis on the role of coupling theory and experiment and future challenges.

Platinum redispersion on metal oxides in low temperature fuel cells.

We have analyzed the aptitude of several metal oxide supports (TiO(2), SnO(2), NbO(2), ZrO(2), SiO(2), Ta(2)O(5) and Nb(2)O(5)) to redisperse platinum under electrochemical conditions pertinent to the Proton Exchange Membrane Fuel Cell (PEMFC) cathode. The redispersion on oxide supports in air has been studied in detail; however, due to different operating conditions it is not straightforward to link the chemical and the electrochemical environment. The largest differences reflect in (1) the oxidation state of the surface (the oxygen species coverage), (2) temperature and (3) the possibility of platinum dissolution at high potentials and the interference of redispersion with normal working potential of the PEMFC cathode. We have calculated the PtO(x) (x = 0, 1, 2) adsorption energies on different metal oxides' surface terminations as well as inside the metal oxides' bulk, and we have concluded that NbO(2) might be a good support for platinum redispersion at PEMFC cathodes.

A theoretical evaluation of possible transition metal electro-catalysts for N2 reduction.

Theoretical studies of the possibility of forming ammonia electrochemically at ambient temperature and pressure are presented. Density functional theory calculations were used in combination with the computational standard hydrogen electrode to calculate the free energy profile for the reduction of N(2) admolecules and N adatoms on several close-packed and stepped transition metal surfaces in contact with an acidic electrolyte. Trends in the catalytic activity were calculated for a range of transition metal surfaces and applied potentials under the assumption that the activation energy barrier scales with the free energy difference in each elementary step. The most active surfaces, on top of the volcano diagrams, are Mo, Fe, Rh, and Ru, but hydrogen gas formation will be a competing reaction reducing the faradaic efficiency for ammonia production. Since the early transition metal surfaces such as Sc, Y, Ti, and Zr bind N-adatoms more strongly than H-adatoms, a significant production of ammonia compared with hydrogen gas can be expected on those metal electrodes when a bias of -1 V to -1.5 V vs. SHE is applied. Defect-free surfaces of the early transition metals are catalytically more active than their stepped counterparts.

Mechanism of the initial stages of nitrogen-doped single-walled carbon nanotube growth.

We have studied the mechanism of the initial stages of nitrogen-doped single-walled carbon nanotube growth illustrated for the case of a floating catalyst chemical vapor deposition system, which uses carbon monoxide (CO) and ammonia (NH(3)) as precursors and iron as a catalyst. We performed first-principles electronic-structure calculations, fully incorporating the effects of spin polarization and magnetic moments, to investigate the bonding and chemistry of CO, NH(3), and their fragments on a model Fe(55) icosahedral cluster. A possible dissociation path for NH(3) to atomic nitrogen and hydrogen was identified, with a reaction barrier consistent with an experimentally determined value we measured by tandem infrared and mass spectrometry. Both C-C and C-N bond formation reactions were found to be barrierless and exothermic, while a parasitic reaction of HCN formation had a barrier of over 1 eV.

Electrochemical chlorine evolution at rutile oxide (110) surfaces.

Based on density functional theory (DFT) calculations we study the electrochemical chlorine evolution reaction on rutile (110) oxide surfaces. First we construct the Pourbaix surface diagram for IrO(2) and RuO(2), and from this we find the chlorine evolution reaction intermediates and identify the lowest overpotential at which all elementary reaction steps in the chlorine evolution reaction are downhill in free energy. This condition is then used as a measure for catalytic activity. Linear scaling relations between the binding energies of the intermediates and the oxygen binding energies at cus-sites are established for MO(2) (M being Ir, Ru, Pt, Ti). The linear relations form the basis for constructing a generalized surface phase diagram where two parameters, the potential and the binding energy of oxygen, are needed to determine the surface composition. We calculate the catalytic activity as function of the oxygen binding energy, giving rise to a Sabatier volcano. By combining the surface phase diagram and the volcano describing the catalytic activity, we find that the reaction mechanism differs depending on catalyst material. The flexibility in reaction path means that the chlorine evolution activity is high for a wide range of oxygen binding energies. We find that the required overpotential for chlorine evolution is lower than the overpotential necessary for oxygen evolution.

Autonomous intelligent agents for accelerated materials discovery.

We present an end-to-end computational system for autonomous materials discovery. The system aims for cost-effective optimization in large, high-dimensional search spaces of materials by adopting a sequential, agent-based approach to deciding which experiments to carry out. In choosing next experiments, agents can make use of past knowledge, surrogate models, logic, thermodynamic or other physical constructs, heuristic rules, and different exploration-exploitation strategies. We show a series of examples for (i) how the discovery campaigns for finding materials satisfying a relative stability objective can be simulated to design new agents, and (ii) how those agents can be deployed in real discovery campaigns to control experiments run externally, such as the cloud-based density functional theory simulations in this work. In a sample set of 16 campaigns covering a range of binary and ternary chemistries including metal oxides, phosphides, sulfides and alloys, this autonomous platform found 383 new stable or nearly stable materials with no intervention by the researchers.

High-throughput calculations of catalytic properties of bimetallic alloy surfaces.

A comprehensive database of chemical properties on a vast set of transition metal surfaces has the potential to accelerate the discovery of novel catalytic materials for energy and industrial applications. In this data descriptor, we present such an extensive study of chemisorption properties of important adsorbates - e.g., C, O, N, H, S, CHx, OH, NH, and SH - on 2,035 bimetallic alloy surfaces in 5 different stoichiometric ratios, i.e., 0%, 25%, 50%, 75%, and 100%. To our knowledge, it is the first systematic study to compile the adsorption properties of such a well-defined, large chemical space of catalytic interest. We propose that a collection of catalytic properties of this magnitude can assist with the development of machine learning enabled surrogate models in theoretical catalysis research to design robust catalysts with high activity for challenging chemical transformations. This database is made publicly available through the platform www.Catalysis-hub.org for easy retrieval of the data for further scientific analysis.

Catalysis-Hub.org, an open electronic structure database for surface reactions.

We present a new open repository for chemical reactions on catalytic surfaces, available at https://www.catalysis-hub.org . The featured database for surface reactions contains more than 100,000 chemisorption and reaction energies obtained from electronic structure calculations, and is continuously being updated with new datasets. In addition to providing quantum-mechanical results for a broad range of reactions and surfaces from different publications, the database features a systematic, large-scale study of chemical adsorption and hydrogenation on bimetallic alloy surfaces. The database contains reaction specific information, such as the surface composition and reaction energy for each reaction, as well as the surface geometries and calculational parameters, essential for data reproducibility. By providing direct access via the web-interface as well as a Python API, we seek to accelerate the discovery of catalytic materials for sustainable energy applications by enabling researchers to efficiently use the data as a basis for new calculations and model generation.