Prediction of Feasibility of Polaronic OER on (110) Surface of Rutile TiO2.

The polaronic effects at the atomic level hold paramount significance for advancing the efficacy of transition metal oxides in applications pertinent to renewable energy. The lattice-distortion mediated localization of photoexcited carriers in the form of polarons plays a pivotal role in the photocatalysis. This investigation focuses on rutile TiO2, an important material extensively explored for solar energy conversion in artificial photosynthesis, specifically targeting the generation of green H2 through photoelectrochemical (PEC) H2O splitting. By employing Hubbard-U corrected and hybrid density functional theory (DFT) methods, we systematically probe the polaronic effects in the catalysis of oxygen evolution reaction (OER) on the (110) surface of rutile TiO2. Theoretical understanding of polarons within the surface, coupled with simulations of OER at distinct titanium (Ti) and oxygen (O) active sites, reveals diverse polaron formation energies within the lattice sites with strong preference for bulk and surface bridge (Ob) oxygen sites. Moreover, we provide the evidence for the facilitative role of polarons in OER. We find that hole polarons situated at the equatorial oxygen sites near the Ti-active site, along with bridge site hole polarons distal from the Ob active site yield a small reduction in OER overpotential by ~0.06 eV and ~0.12 eV, respectively. However, subsurface, equatorial, and bridge site hole polarons significantly reduce the Ti-active site OER overpotential by ~0.4 eV through the peroxo-type oxygen pathway. We also observe that the presence of hole polarons stabilizes the *OH, *O, and *OOH intermediate species compared to the scenario without hole polarons. Overall, this study provides a detailed mechanistic insight into polaron-mediated OER, offering a promising avenue for improving the catalytic activity of transition metal oxide-based photocatalysts catering to renewable energy requisites.

Interpretable Machine Learning Models for Practical Antimonate Electrocatalyst Performance.

Computationally predicting the performance of catalysts under reaction conditions is a challenging task due to the complexity of catalytic surfaces and their evolution in situ, different reaction paths, and the presence of solid-liquid interfaces in the case of electrochemistry. We demonstrate here how relatively simple machine learning models can be found that enable prediction of experimentally observed onset potentials. Inputs to our model are comprised of data from the oxygen reduction reaction on non-precious transition-metal antimony oxide nanoparticulate catalysts with a combination of experimental conditions and computationally affordable bulk atomic and electronic structural descriptors from density functional theory simulations. From human-interpretable genetic programming models, we identify key experimental descriptors and key supplemental bulk electronic and atomic structural descriptors that govern trends in onset potentials for these oxides and deduce how these descriptors should be tuned to increase onset potentials. We finally validate these machine learning predictions by experimentally confirming that scandium as a dopant in nickel antimony oxide leads to a desired onset potential increase. Macroscopic experimental factors are found to be crucially important descriptors to be considered for models of catalytic performance, highlighting the important role machine learning can play here even in the presence of small datasets.

Toward Carbon Monoxide Methanation at Mild Conditions on Dual-Site Catalysts.

The catalytic carbon monoxide (CO) methanation is an ideal model reaction for the fundamental understanding of catalysis on the gas-solid interface and is crucial for various industrial processes. However, the harsh operating conditions make the reaction unsustainable, and the limitations set by the scaling relations between the dissociation energy barrier and dissociative binding energy of CO further increase the difficulty in designing high-performance methanation catalysts operating under milder conditions. Herein, we proposed a theoretical strategy to circumvent the limitations elegantly and achieve both facile CO dissociation and C/O hydrogenation on the catalyst containing a confined dual site. The DFT-based microkinetic modeling (MKM) reveals that the designed Co-Cr2/G dual-site catalyst could provide 4-6 orders of magnitude higher turnover frequency for CH4 production than the cobalt step sites. We believe that the proposed strategy in the current work will provide essential guidance for designing state-of-the-art methanation catalysts under mild conditions.

Interpretable design of Ir-free trimetallic electrocatalysts for ammonia oxidation with graph neural networks.

The electrochemical ammonia oxidation to dinitrogen as a means for energy and environmental applications is a key technology toward the realization of a sustainable nitrogen cycle. The state-of-the-art metal catalysts including Pt and its bimetallics with Ir show promising activity, albeit suffering from high overpotentials for appreciable current densities and the soaring price of precious metals. Herein, the immense design space of ternary Pt alloy nanostructures is explored by graph neural networks trained on ab initio data for concurrently predicting site reactivity, surface stability, and catalyst synthesizability descriptors. Among a few Ir-free candidates that emerge from the active learning workflow, Pt3Ru-M (M: Fe, Co, or Ni) alloys were successfully synthesized and experimentally verified to be more active toward ammonia oxidation than Pt, Pt3Ir, and Pt3Ru. More importantly, feature attribution analyses using the machine-learned representation of site motifs provide fundamental insights into chemical bonding at metal surfaces and shed light on design strategies for high-performance catalytic systems beyond the d-band center metric of binding sites.

Symmetry-resolved CO desorption and oxidation dynamics on O/Ru(0001) probed at the C K-edge by ultrafast x-ray spectroscopy.

We report on carbon monoxide desorption and oxidation induced by 400 nm femtosecond laser excitation on the O/Ru(0001) surface probed by time-resolved x-ray absorption spectroscopy (TR-XAS) at the carbon K-edge. The experiments were performed under constant background pressures of CO (6 × 10-8 Torr) and O2 (3 × 10-8 Torr). Under these conditions, we detect two transient CO species with narrow 2π* peaks, suggesting little 2π* interaction with the surface. Based on polarization measurements, we find that these two species have opposing orientations: (1) CO favoring a more perpendicular orientation and (2) CO favoring a more parallel orientation with respect to the surface. We also directly detect gas-phase CO2 using a mass spectrometer and observe weak signatures of bent adsorbed CO2 at slightly higher x-ray energies than the 2π* region. These results are compared to previously reported TR-XAS results at the O K-edge, where the CO background pressure was three times lower (2 × 10-8 Torr) while maintaining the same O2 pressure. At the lower CO pressure, in the CO 2π* region, we observed adsorbed CO and a distribution of OC-O bond lengths close to the CO oxidation transition state, with little indication of gas-like CO. The shift toward "gas-like" CO species may be explained by the higher CO exposure, which blocks O adsorption, decreasing O coverage and increasing CO coverage. These effects decrease the CO desorption barrier through dipole-dipole interaction while simultaneously increasing the CO oxidation barrier.

Catalytic Performance and Near-Surface X-ray Characterization of Titanium Hydride Electrodes for the Electrochemical Nitrate Reduction Reaction.

The electrochemical nitrate reduction reaction (NO3RR) on titanium introduces significant surface reconstruction and forms titanium hydride (TiHx, 0 < x ≤ 2). With ex situ grazing-incidence X-ray diffraction (GIXRD) and X-ray absorption spectroscopy (XAS), we demonstrated near-surface TiH2 enrichment with increasing NO3RR applied potential and duration. This quantitative relationship facilitated electrochemical treatment of Ti to form TiH2/Ti electrodes for use in NO3RR, thereby decoupling hydride formation from NO3RR performance. A wide range of NO3RR activity and selectivity on TiH2/Ti electrodes between -0.4 and -1.0 VRHE was observed and analyzed with density functional theory (DFT) calculations on TiH2(111). This work underscores the importance of relating NO3RR performance with near-surface electrode structure to advance catalyst design and operation.

Trends in oxygenate/hydrocarbon selectivity for electrochemical CO(2) reduction to C2 products.

The electrochemical conversion of carbon di-/monoxide into commodity chemicals paves a way towards a sustainable society but it also presents one of the great challenges in catalysis. Herein, we present the trends in selectivity towards specific dicarbon oxygenate/hydrocarbon products from carbon monoxide reduction on transition metal catalysts, with special focus on copper. We unveil the distinctive role of electrolyte pH in tuning the dicarbon oxygenate/hydrocarbon selectivity. The understanding is based on density functional theory calculated energetics and microkinetic modeling. We identify the critical reaction steps determining selectivity and relate their transition state energies to two simple descriptors, the carbon and hydroxide binding strengths. The atomistic insight gained enables us to rationalize a number of experimental observations and provides avenues towards the design of selective electrocatalysts for liquid fuel production from carbon di-/monoxide.

Vernier template synthesis of molecular knots.

Molecular knots are often prepared using metal helicates to cross the strands. We found that coordinatively mismatching oligodentate ligands and metal ions provides a more effective way to synthesize larger knots using Vernier templating. Strands composed of different numbers of tridentate 2,6-pyridinedicarboxamide groups fold around nine-coordinate lanthanide (III) ions to generate strand-entangled complexes with the lowest common multiple of coordination sites for the ligand strands and metal ions. Ring-closing olefin metathesis then completes the knots. A 3:2 (ditopic strand:metal) Vernier assembly produces +31#+31 and -31#-31 granny knots. Vernier complexes of 3:4 (tetratopic strand:metal) stoichiometry selectively form a 378-atom-long trefoil-of-trefoils triskelion knot with 12 alternating strand crossings or, by using opposing stereochemistry at the terminus of the strand, an inverted-core triskelion knot with six alternating and six nonalternating strand crossings.

Colloidal Platinum-Copper Nanocrystal Alloy Catalysts Surpass Platinum in Low-Temperature Propene Combustion.

Low-temperature removal of noxious environmental emissions plays a critical role in minimizing the harmful effects of hydrocarbon fuels. Emission-control catalysts typically consist of large quantities of rare, noble metals (e.g., platinum and palladium), which are expensive and environmentally damaging metals to extract. Alloying with cheaper base metals offers the potential to boost catalytic activity while optimizing the use of noble metals. In this work, we show that PtxCu100-x catalysts prepared from colloidal nanocrystals are more active than the corresponding Pt catalysts for complete propene oxidation. By carefully controlling their composition while maintaining nanocrystal size, alloys with dilute Cu concentrations (15-30% atomic fraction) demonstrate promoted activity compared to pure Pt. Complete propene oxidation was observed at temperatures as low as 150 °C in the presence of steam, and five to ten times higher turnover frequencies were found compared to monometallic Pt catalysts. Through DFT studies and structural and catalytic characterization, the remarkable activity of dilute PtxCu100-x alloys was related to the tuning of the electronic structure of Pt to reach optimal binding energies of C* and O* intermediates. This work provides a general approach toward investigation of structure-property relationships of alloyed catalysts with efficient and optimized use of noble metals.

Atom-Specific Probing of Electron Dynamics in an Atomic Adsorbate by Time-Resolved X-Ray Spectroscopy.

The electronic excitation occurring on adsorbates at ultrafast timescales from optical lasers that initiate surface chemical reactions is still an open question. Here, we report the ultrafast temporal evolution of x-ray absorption spectroscopy (XAS) and x-ray emission spectroscopy (XES) of a simple well-known adsorbate prototype system, namely carbon (C) atoms adsorbed on a nickel [Ni(100)] surface, following intense laser optical pumping at 400 nm. We observe ultrafast (∼100 fs) changes in both XAS and XES showing clear signatures of the formation of a hot electron-hole pair distribution on the adsorbate. This is followed by slower changes on a few picoseconds timescale, shown to be consistent with thermalization of the complete C/Ni system. Density functional theory spectrum simulations support this interpretation.

Modeling Potential-Dependent Electrochemical Activation Barriers: Revisiting the Alkaline Hydrogen Evolution Reaction.

Accurate theoretical simulation of electrochemical activation barriers is key to understanding electrocatalysis and guides the design of more efficient catalysts. Providing a detailed picture of proton transfer processes encounters several challenges: the constant potential requirement during charge transfer, the different time scales involved in the processes, and the thermal fluctuation of the solvent. Hence, it is prohibitively expensive computationally to apply density functional theory (DFT) calculations in modeling the potential-dependent activation barrier at the electrode-solvent interface, and the results are dubious. To address these challenges, we have developed an analytical approach based on charge conservation and decoupled potential energy surfaces to compute charge transfer barriers. The method makes it possible to simulate an electrochemical process at different potentials and explicitly include thermal fluctuations of the solvent at the electrode-solvent interface. We use the Pt-catalyzed alkaline hydrogen evolution reaction (HER) as our benchmark reaction, and we model the microkinetics of HER with consideration of the spatial fluctuations between the metal surface and the first solvent layer at room temperature. The distribution of water-metal distances has a large effect on the barriers of the charge transfer processes, and an accurate account of the statistical fluctuation in the reaction network leads to a several orders of magnitude increase in HER current as compared to transfer from a static solvent. The trends of the different reaction mechanisms in HER were successfully simulated with our model, and the theoretical I-V curves obtained are in good qualitative agreement with experimental results.

Combining artificial intelligence and physics-based modeling to directly assess atomic site stabilities: from sub-nanometer clusters to extended surfaces.

The performance of functional materials is dictated by chemical and structural properties of individual atomic sites. In catalysts, for example, the thermodynamic stability of constituting atomic sites is a key descriptor from which more complex properties, such as molecular adsorption energies and reaction rates, can be derived. In this study, we present a widely applicable machine learning (ML) approach to instantaneously compute the stability of individual atomic sites in structurally and electronically complex nano-materials. Conventionally, we determine such site stabilities using computationally intensive first-principles calculations. With our approach, we predict the stability of atomic sites in sub-nanometer metal clusters of 3-55 atoms with mean absolute errors in the range of 0.11-0.14 eV. To extract physical insights from the ML model, we introduce a genetic algorithm (GA) for feature selection. This algorithm distills the key structural and chemical properties governing the stability of atomic sites in size-selected nanoparticles, allowing for physical interpretability of the models and revealing structure-property relationships. The results of the GA are generally model and materials specific. In the limit of large nanoparticles, the GA identifies features consistent with physics-based models for metal-metal interactions. By combining the ML model with the physics-based model, we predict atomic site stabilities in real time for structures ranging from sub-nanometer metal clusters (3-55 atom) to larger nanoparticles (147 to 309 atoms) to extended surfaces using a physically interpretable framework. Finally, we present a proof of principle showcasing how our approach can determine stable and active nanocatalysts across a generic materials space of structure and composition.

Guiding the Catalytic Properties of Copper for Electrochemical CO2 Reduction by Metal Atom Decoration.

Tuning bimetallic effects is a promising strategy to guide catalytic properties. However, the nature of these effects can be difficult to assess and compare due to the convolution with other factors such as the catalyst surface structure and morphology and differences in testing environments. Here, we investigate the impact of atomic-scale bimetallic effects on the electrochemical CO2 reduction performance of Cu-based catalysts by leveraging a systematic approach that unifies protocols for materials synthesis and testing and enables accurate comparisons of intrinsic catalytic activity and selectivity. We used the same physical vapor deposition method to epitaxially grow Cu(100) films decorated with a small amount of noble or base metal atoms and a combination of experimental characterization and first-principles calculations to evaluate their physicochemical and catalytic properties. The results indicate that the metal atoms segregate to under-coordinated Cu sites during physical vapor deposition, suppressing CO reduction to oxygenates and hydrocarbons and promoting competing pathways to CO, formate, and hydrogen. Leveraging these insights, we rationalize bimetallic design principles to improve catalytic selectivity for CO2 reduction to CO, formate, oxygenates, or hydrocarbons. Our study provides one of the most extensive studies on Cu bimetallics for CO2 reduction, establishing a systematic approach that is broadly applicable to research in catalyst discovery.

Ultrafast Adsorbate Excitation Probed with Subpicosecond-Resolution X-Ray Absorption Spectroscopy.

We use a pump-probe scheme to measure the time evolution of the C K-edge x-ray absorption spectrum from CO/Ru(0001) after excitation by an ultrashort high-intensity optical laser pulse. Because of the short duration of the x-ray probe pulse and precise control of the pulse delay, the excitation-induced dynamics during the first picosecond after the pump can be resolved with unprecedented time resolution. By comparing with density functional theory spectrum calculations, we find high excitation of the internal stretch and frustrated rotation modes occurring within 200 fs of laser excitation, as well as thermalization of the system in the picosecond regime. The ∼100 fs initial excitation of these CO vibrational modes is not readily rationalized by traditional theories of nonadiabatic coupling of adsorbates to metal surfaces, e.g., electronic frictions based on first order electron-phonon coupling or transient population of adsorbate resonances. We suggest that coupling of the adsorbate to nonthermalized electron-hole pairs is responsible for the ultrafast initial excitation of the modes.

Achieving industrial ammonia synthesis rates at near-ambient conditions through modified scaling relations on a confined dual site.

The production of ammonia through the Haber-Bosch process is regarded as one of the most important inventions of the 20th century. Despite significant efforts in optimizing the process, it still consumes 1 to 2% of the worldwide annual energy for the high working temperatures and pressures. The design of a catalyst with a high activity at milder conditions represents another challenge for this reaction. Herein, we combine density functional theory and microkinetic modeling to illustrate a strategy to facilitate low-temperature and -pressure ammonia synthesis through modified energy-scaling relationships using a confined dual site. Our results suggest that an ammonia synthesis rate two to three orders of magnitude higher than the commercial Ru catalyst can be achieved under the same reaction conditions with the introduction of confinement. Such strategies will open pathways for the development of catalysts for the Haber-Bosch process that can operate at milder conditions and present more economically viable alternatives to current industrial solutions.

Identification of earth-abundant materials for selective dehydrogenation of light alkanes to olefins.

Selective ethane dehydrogenation (EDH) is an attractive on-purpose strategy for industrial ethylene production. Design of an effective, stable, and earth-abundant catalyst to replace noble metal Pt is the main obstacle for its large-scale application. Herein, we report an experimentally validated theoretical framework to discover promising catalysts for EDH, which combines descriptor-based microkinetic modeling, high-throughput computations, machine-learning concepts, and experiments. Our approach efficiently evaluates 1,998 bimetallic alloys by using accurately calculated C and CH3 adsorption energies and identifies a small number of new promising noble-metal-free catalysts for selective EDH. A Ni3Mo alloy predicted to be promising is successfully synthesized, and experimentally proven to outperform Pt in selective ethylene production from EDH, representing an important contribution to the improvement of light alkane dehydrogenation to olefins. These results will provide essential additions in the discovery and application of earth-abundant materials in catalysis.

Revealing the structure of a catalytic combustion active-site ensemble combining uniform nanocrystal catalysts and theory insights.

Supported metal catalysts are extensively used in industrial and environmental applications. To improve their performance, it is crucial to identify the most active sites. This identification is, however, made challenging by the presence of a large number of potential surface structures that complicate such an assignment. Often, the active site is formed by an ensemble of atoms, thus introducing further complications in its identification. Being able to produce uniform structures and identify the ones that are responsible for the catalyst performance is a crucial goal. In this work, we utilize a combination of uniform Pd/Pt nanocrystal catalysts and theory to reveal the catalytic active-site ensemble in highly active propene combustion materials. Using colloidal chemistry to exquisitely control nanoparticle size, we find that intrinsic rates for propene combustion in the presence of water increase monotonically with particle size on Pt-rich catalysts, suggesting that the reaction is structure dependent. We also reveal that water has a near-zero or mildly positive reaction rate order over Pd/Pt catalysts. Theory insights allow us to determine that the interaction of water with extended terraces present in large particles leads to the formation of step sites on metallic surfaces. These specific step-edge sites are responsible for the efficient combustion of propene at low temperature. This work reveals an elusive geometric ensemble, thus clearly identifying the active site in alkene combustion catalysts. These insights demonstrate how the combination of uniform catalysts and theory can provide a much deeper understanding of active-site geometry for many applications.

Predicting metal-metal interactions. I. The influence of strain on nanoparticle and metal adlayer stabilities.

Strain-engineering of bimetallic nanomaterials is an important design strategy for developing new catalysts. Herein, we introduce an approach for including strain effects into a recently introduced, density functional theory (DFT)-based alloy stability model. The model predicts adsorption site stabilities in nanoparticles and connects these site stabilities with catalytic reactivity and selectivity. Strain-based dependencies will increase the model's accuracy for nanoparticles affected by finite-size effects. In addition to the stability of small nanoparticles, strain also influences the heat of adsorption of epitaxially grown metal-on-metal adlayers. In this respect, we successfully benchmark the strain-including alloy stability model with previous experimentally determined trends in the heats of adsorption of Au and Cu adlayers on Pt (111). For these systems, our model predicts stronger bimetallic interactions in the first monolayer than monometallic interactions in the second monolayer. We explicitly quantify the interplay between destabilizing strain effects and the energy gained by forming new metal-metal bonds. While tensile strain in the first Cu monolayer significantly destabilizes the adsorption strength, compressive strain in the first Au monolayer has a minimal impact on the heat of adsorption. Hence, this study introduces and, by comparison with previous experiments, validates an efficient DFT-based approach for strain-engineering the stability, and, in turn, the catalytic performance, of active sites in bimetallic alloys with atomic level resolution.

Predicting metal-metal interactions. II. Accelerating generalized schemes through physical insights.

Operando-computational frameworks that integrate descriptors for catalyst stability within catalyst screening paradigms enable predictions of rates and selectivity on chemically faithful representations of nanoparticles under reaction conditions. These catalyst stability descriptors can be efficiently predicted by density functional theory (DFT)-based models. The alloy stability model, for example, predicts the stability of metal atoms in nanoparticles with site-by-site resolution. Herein, we use physical insights to present accelerated approaches of parameterizing this recently introduced alloy-stability model. These accelerated approaches meld quadratic functions for the energy of metal atoms in terms of the coordination number with linear correlations between model parameters and the cohesive energies of bulk metals. By interpolating across both the coordination number and chemical space, these accelerated approaches shrink the training set size for 12 fcc p- and d-block metals from 204 to as few as 24 DFT calculated total energies without sacrificing the accuracy of our model. We validate the accelerated approaches by predicting adsorption energies of metal atoms on extended surfaces and 147 atom cuboctahedral nanoparticles with mean absolute errors of 0.10 eV and 0.24 eV, respectively. This efficiency boost will enable a rapid and exhaustive exploration of the vast material space of transition metal alloys for catalytic applications.