100% selective cyclotrimerization of acetylene to benzene on Ag(111).

Benzene, a high-volume chemical, is produced from larger molecules by inefficient and environmentally harmful processes. Recent changes in hydrocarbon feedstocks from oil to gas motivate research into small molecule upgrading. For example, the cyclotrimerization of acetylene reaction has been demonstrated on Pd, Pd alloy, and Cu surfaces and catalysts, but they are not 100% selective to benzene. We discovered that acetylene can be converted to benzene with 100% selectivity on the Ag(111) surface. Our temperature programmed desorption experiments reveal a threshold acetylene surface coverage of ∼one monolayer, above which benzene is formed. Furthermore, additional layers of acetylene increase the amount of benzene produced while retaining 100% selectivity. Our scanning tunneling microscopy images show that acetylene prefers square packing on the Ag(111) surface at low coverages, which converts to hexagonal packing when acetylene multilayers are present. Within this denser layer, features consistent with the proposed C4 intermediates of the cyclotrimerization process are observed. Density functional theory calculations demonstrate that the barrier for forming the crucial C4 intermediate generally decreases as acetylene multilayers are formed because the multilayer interacts more strongly with the surface in the transition state than in the initial state. Given that acetylene desorbs from Ag(111) at ∼90 K, the C4 intermediate on the pathway to benzene must be formed below this temperature, implying that if Ag-based heterogeneous catalysts can be run at sufficiently high pressure and low enough temperature, efficient and selective trimerization of acetylene to benzene may be possible.

Bond Selective Photochemistry at Metal Nanoparticle Surfaces: CO Desorption from Pt and Pd.

The use of visible photon fluxes to influence catalytic reactions on metal nanoparticle surfaces has attracted attention based on observations of reaction mechanisms and selectivity not observed under equilibrium heating. These observations suggest that photon fluxes can selectively impact the rates of certain elementary steps, creating nonequilibrium energy distributions among various reaction pathways. However, quantitative studies validating these hypotheses on metal nanoparticle surfaces are lacking. We examine the influence of continuous wave visible photon fluxes on the CO desorption rates from 1 to 2 nm diameter Pt and Pd nanoparticle surfaces supported on γ-Al2O3. Temperature-programmed desorption measurements quantified via diffuse reflectance infrared Fourier transform spectroscopy demonstrate that visible photon fluxes significantly enhanced the rate of CO desorption from Pt nanoparticles in a wavelength-dependent manner. 440 nm photons most efficiently promoted CO desorption from Pt nanoparticle surfaces, aligning with the excitation energy for the interfacial electronic transition within the Pt-CO bond. Conversely, visible photon fluxes had no measurable influence on CO desorption rates from Pd nanoparticle surfaces after accounting for photon-induced heating. Density functional theory calculations demonstrate that the Pt-CO bond exhibits a narrower LUMO resonance, stronger coupling between the photoexcitation and forces induced on the metal-C bond, and vibrational energy dissipation that more effectively couples to desorption as compared to Pd-CO. These results demonstrate the specificity photons provide in facilitating chemical reactions on metal nanoparticle surfaces and substantiate the idea that photon fluxes can steer processes and outcomes of catalytic reactions in ways not achievable by equilibrium heating.

Latent Variable Machine Learning Framework for Catalysis: General Models, Transfer Learning, and Interpretability.

Machine learning has been successfully applied in recent years to screen materials for a variety of applications. However, despite recent advances, most screening-based machine learning approaches are limited in generality and transferability, requiring new models to be created from scratch for each new application. This is particularly apparent in catalysis, where there are many possible intermediates and transition states of interest in addition to a large number of potential catalytic materials. In this work, we developed a new machine learning framework that is built on chemical principles and allows the creation of general, interpretable, reusable models. Our new architecture uses latent variables to create a set of submodels that each take on a relatively simple learning task, leading to higher data efficiency and promoting transfer learning. This architecture infuses fundamental chemical principles, such as the existence of elements as discrete entities. We show that this architecture allows for the creation of models that can be reused for many different applications, providing significant improvements in efficiency and convenience. For example, our architecture allows simultaneous prediction of adsorption energies for many adsorbates on a broad array of alloy surfaces with mean absolute errors (MAEs) around 0.20-0.25 eV. The integration of latent variables provides physical interpretability, as predictions can be explained in terms of the learned chemical environment as represented by the latent space. Further, these latent variables also serve as new feature representations, allowing efficient transfer learning. For example, new models with useful levels of accuracy can be created with less than 10 data points, including transfer learning to an experimental data set with an MAE less than 0.15 eV. Lastly, we show that our new machine learning architecture is general and robust enough to handle heterogeneous and multifidelity data sets, allowing researchers to leverage existing data sets to speed up screening using their own computational setup.

Recent Advances in Real-Time Time-Dependent Density Functional Theory Simulations of Plasmonic Nanostructures and Plasmonic Photocatalysis.

Plasmonic catalysis provides a possible means for driving chemical reactions under relatively mild conditions. Rational design of these systems is impeded by the difficulty in understanding the electron dynamics and their interplay with reactions. Real-time, time-dependent density functional theory (RT-TDDFT) can provide dynamic information on excited states in plasmonic systems, including those relevant to plasmonic catalysis, at time scales and length scales that are otherwise out of reach of many experimental techniques. Here, we discuss previous RT-TDDFT studies of plasmonic systems, focusing on recent work that gains insight into plasmonic catalysis. These studies provide insight into plasmon dynamics, including size effects and the role of specific electronic states. Further, these studies provide significant insight into mechanisms underlying plasmonic catalysis, showing the importance of charge transfer between metal and adsorbate states, as well as local field enhancement, in different systems.

Toward MBenes Battery Electrode Materials: Layered Molybdenum Borides for Li-Ion Batteries.

Lithium-ion and sodium-ion batteries (LIBs and SIBs) are crucial in our shift toward sustainable technologies. In this work, the potential of layered boride materials (MoAlB and Mo2 AlB2 ) as novel, high-performance electrode materials for LIBs and SIBs, is explored. It is discovered that Mo2 AlB2 shows a higher specific capacity than MoAlB when used as an electrode material for LIBs, with a specific capacity of 593 mAh g-1 achieved after 500 cycles at 200 mA g-1 . It is also found that surface redox reactions are responsible for Li storage in Mo2 AlB2 , instead of intercalation or conversion. Moreover, the sodium hydroxide treatment of MoAlB leads to a porous morphology and higher specific capacities exceeding that of pristine MoAlB. When tested in SIBs, Mo2 AlB2 exhibits a specific capacity of 150 mAh g-1 at 20 mA g-1 . These findings suggest that layered borides have potential as electrode materials for both LIBs and SIBs, and highlight the importance of surface redox reactions in Li storage mechanisms.

A Priori Design of Dual-Atom Alloy Sites and Experimental Demonstration of Ethanol Dehydrogenation and Dehydration on PtCrAg.

Single-atom catalysts have received significant attention for their ability to enable highly selective reactions. However, many reactions require more than one adjacent site to align reactants or break specific bonds. For example, breaking a C-O or O-H bond may be facilitated by a dual site containing an oxophilic element and a carbophilic or "hydrogenphilic" element that binds each molecular fragment. However, design of stable and well-defined dual-atom sites with desirable reactivity is difficult due to the complexity of multicomponent catalytic surfaces. Here, we describe a new type of dual-atom system, trimetallic dual-atom alloys, which were designed via computation of the alloying energetics. Through a broad computational screening we discovered that Pt-Cr dimers embedded in Ag(111) can be formed by virtue of the negative mixing enthalpy of Pt and Cr in Ag and the favorable interaction between Pt and Cr. These dual-atom alloy sites were then realized experimentally through surface science experiments that enabled the active sites to be imaged and their reactivity related to their atomic-scale structure. Specifically, Pt-Cr sites in Ag(111) can convert ethanol, whereas PtAg and CrAg are unreactive toward ethanol. Calculations show that the oxophilic Cr atom and the hydrogenphilic Pt atom act synergistically to break the O-H bond. Furthermore, ensembles with more than one Cr atom, present at higher dopant loadings, produce ethylene. Our calculations have identified many other thermodynamically favorable dual-atom alloy sites, and hence this work highlights a new class of materials that should offer new and useful chemical reactivity beyond the single-atom paradigm.

Tuning reactivity in trimetallic dual-atom alloys: molecular-like electronic states and ensemble effects.

Single-atom alloys (SAAs) have drawn significant attention in recent years due to their excellent catalytic properties. Controlling the geometry and electronic structure of this type of localized catalytic active site is of fundamental and technological importance. Dual-atom alloys (DAAs) consisting of a heterometallic dimer embedded in the surface layer of a metal host would bring increased tunability and a larger active site, as compared to SAAs. Here, we use computational studies to show that DAAs allow tuning of the active site electronic structure and reactivity. Interestingly, combining two SAAs into a dual-atom site can result in molecular-like hybridization by virtue of the free-atom-like electronic d states exhibited by many SAAs. DAAs can inherit the weak d-d interaction between dopants and hosts from the constituent SAAs, but exhibit new electronic and reactive properties due to dopant-dopant interactions in the DAA. We identify many heterometallic DAAs that we predict to be more stable than either the constituent SAAs or homometallic dual-atom sites of each dopant. We also show how both electronic and ensemble effects can modify the strength of CO adsorption. Because of the molecular-like interactions that can occur, DAAs require a different approach for tuning chemical properties compared to what is used for previous classes of alloys. This work provides insights into the unique catalytic properties of DAAs, and opens up new possibilities for tailoring localized and well-defined catalytic active sites for optimal reaction pathways.

Dilute Alloys Based on Au, Ag, or Cu for Efficient Catalysis: From Synthesis to Active Sites.

The development of new catalyst materials for energy-efficient chemical synthesis is critical as over 80% of industrial processes rely on catalysts, with many of the most energy-intensive processes specifically using heterogeneous catalysis. Catalytic performance is a complex interplay of phenomena involving temperature, pressure, gas composition, surface composition, and structure over multiple length and time scales. In response to this complexity, the integrated approach to heterogeneous dilute alloy catalysis reviewed here brings together materials synthesis, mechanistic surface chemistry, reaction kinetics, in situ and operando characterization, and theoretical calculations in a coordinated effort to develop design principles to predict and improve catalytic selectivity. Dilute alloy catalysts─in which isolated atoms or small ensembles of the minority metal on the host metal lead to enhanced reactivity while retaining selectivity─are particularly promising as selective catalysts. Several dilute alloy materials using Au, Ag, and Cu as the majority host element, including more recently introduced support-free nanoporous metals and oxide-supported nanoparticle "raspberry colloid templated (RCT)" materials, are reviewed for selective oxidation and hydrogenation reactions. Progress in understanding how such dilute alloy catalysts can be used to enhance selectivity of key synthetic reactions is reviewed, including quantitative scaling from model studies to catalytic conditions. The dynamic evolution of catalyst structure and composition studied in surface science and catalytic conditions and their relationship to catalytic function are also discussed, followed by advanced characterization and theoretical modeling that have been developed to determine the distribution of minority metal atoms at or near the surface. The integrated approach demonstrates the success of bridging the divide between fundamental knowledge and design of catalytic processes in complex catalytic systems, which can accelerate the development of new and efficient catalytic processes.

When more is less: Nonmonotonic trends in adsorption on clusters in alloy surfaces.

Single-atom alloys can be effective catalysts and have been compared to supported single-atom catalysts. To rationally design single-atom alloys and other surfaces with localized ensembles, it is crucial to understand variations in reactivity when varying the dopant and the ensemble size. Here, we examined hydrogen adsorption on surfaces embedded with localized clusters and discovered general trends. Counterintuitively, increasing the amount of a more reactive metal sometimes makes a surface site less reactive. This behavior is due to the hybridization and splitting of narrow peaks in the electronic density of states of many of these surfaces, making them analogous to free-standing nanoclusters. When a single-atom alloy has a peak just below the Fermi energy, the corresponding two-dopant cluster often has weaker adsorption than the single-atom alloy due to splitting of this peak across the Fermi energy. Furthermore, single-atom alloys have qualitatively different behaviors than larger ensembles. Specifically, the adsorption energy is a U-shaped function of the dopant's group for single-atom alloys. Additionally, adsorption energies on single-atom alloys correlate more strongly with the dopant's p-band center than with the d-band center.

PTML Model for Selection of Nanoparticles, Anticancer Drugs, and Vitamins in the Design of Drug-Vitamin Nanoparticle Release Systems for Cancer Cotherapy.

Nanosystems are gaining momentum in pharmaceutical sciences because of the wide variety of possibilities for designing these systems to have specific functions. Specifically, studies of new cancer cotherapy drug-vitamin release nanosystems (DVRNs) including anticancer compounds and vitamins or vitamin derivatives have revealed encouraging results. However, the number of possible combinations of design and synthesis conditions is remarkably high. In addition, a large number of anticancer and vitamin derivatives have been already assayed, but a notably less number of cases of DVRNs were assayed as a whole (with the anticancer compound and the vitamin linked to them). Our approach combines with the perturbation theory and machine learning (PTML) model to predict the probability of obtaining an interesting DVRN by changing the anticancer compound and/or the vitamin present in a DVRN that is already tested for other anticancer compounds or vitamins that have not been tested yet as part of a DVRN. In a previous work, we built a linear PTML model useful for the design of these nanosystems. In doing so, we used information fusion (IF) techniques to carry out data enrichment of DVRN data compiled from the literature with the data for preclinical assays of vitamins from the ChEMBL database. The design features of DVRNs and the assay conditions of nanoparticles (NPs) and vitamins were included as multiplicative PT operators (PTOs) to the system, which indicates the importance of these variables. However, the previous work omitted experiments with nonlinear ML techniques and different types of PTOs such as metric-based PTOs. More importantly, the previous work does not consider the structure of the anticancer drug to be included in the new DVRNs. In this work, we are going to accomplish three main objectives (tasks). In the first task, we found a new model, alternative to the one published before, for the rational design of DVRNs using metric-based PTOs. The most accurate PTML model was the artificial neural network model, which showed values of specificity, sensitivity, and accuracy in the range of 90-95% in training and external validation series for more than 130,000 cases (DVRNs vs ChEMBL assays). Furthermore, in the second task, we used IF techniques to carry out data enrichment of our previous data set. In doing so, we constructed a new working data set of >970,000 cases with the data of preclinical assays of DVRNs, vitamins, and anticancer compounds from the ChEMBL database. All these assays have multiple continuous variables or descriptors dk and categorical variables cj (conditions of the assays) for drugs (dack, cacj), vitamins (dvk, cvj), and NPs (dnk, cnj). These data include >20,000 potential anticancer compounds with >270 protein targets (cac1), >580 assay cell organisms (cac2), and so forth. Furthermore, we include >36,000 assay vitamin derivatives in >6200 types of cells (c2vit), >120 assay organisms (c3vit), >60 assay strains (c4vit), and so forth. The enriched data set also contains >20 types of DVRNs (c5n) with 9 NP core materials (c4n), 8 synthesis methods (c7n), and so forth. We expressed all this information with PTOs and developed a qualitatively new PTML model that incorporates information of the anticancer drugs. This new model presents 96-97% of accuracy for training and external validation subsets. In the last task, we carried out a comparative study of ML and/or PTML models published and described how the models we are presenting cover the gap of knowledge in terms of drug delivery. In conclusion, we present here for the first time a multipurpose PTML model that is able to select NPs, anticancer compounds, and vitamins and their conditions of assay for DVRN design.

Machine Learning Prediction of H Adsorption Energies on Ag Alloys.

Adsorption energies on surfaces are excellent descriptors of their chemical properties, including their catalytic performance. High-throughput adsorption energy predictions can therefore help accelerate first-principles catalyst design. To this end, we present over 5000 DFT calculations of H adsorption energies on dilute Ag alloys and describe a general machine learning approach to rapidly predict H adsorption energies for new Ag alloy structures. We find that random forests provide accurate predictions and that the best features are combinations of traditional chemical and structural descriptors. Further analysis of our model errors and the underlying forest kernel reveals unexpected finite-size electronic structure effects: embedded dopant atoms can display counterintuitive behavior such as nonmonotonic trends as a function of composition and high sensitivity to dopants far from the adsorbing H atom. We explain these behaviors with simple tight-binding Hamiltonians and d-orbital densities of states. We also use variations among forest leaves to predict the uncertainty of predictions, which allows us to mitigate the effects of larger errors.

Nonadiabatic Hydrogen Dissociation on Copper Nanoclusters.

Copper surfaces exhibit high catalytic selectivity but have poor hydrogen dissociation kinetics; therefore, we consider icosahedral Cu13 nanoclusters to understand how nanoscale structure might improve catalytic prospects. We find that the spin state is a surprisingly important design consideration. Cu13 clusters have large magnetic moments due to finite size and symmetry effects and exhibit magnetization-dependent catalytic behavior. The most favorable transition state for hydrogen dissociation has a lower activation energy than that on single-crystal copper surfaces but requires a magnetization switch from 5 to 3 µB. Without this switch, the activation energy is higher than that on single-crystal surfaces. Weak spin-orbit coupling hinders this switch, decreasing the kinetic rate of hydrogen dissociation by a factor of 16. We consider strategies to facilitate magnetization switches through optical excitations, substitution, charge states, and co-catalysts; these considerations demonstrate how control of magnetic properties could improve catalytic performance.

O2 Activation by Metal Surfaces: Implications for Bonding and Reactivity on Heterogeneous Catalysts.

The activation of O2 on metal surfaces is a critical process for heterogeneous catalysis and materials oxidation. Fundamental studies of well-defined metal surfaces using a variety of techniques have given crucial insight into the mechanisms, energetics, and dynamics of O2 adsorption and dissociation. Here, trends in the activation of O2 on transition metal surfaces are discussed, and various O2 adsorption states are described in terms of both electronic structure and geometry. The mechanism and dynamics of O2 dissociation are also reviewed, including the importance of the spin transition. The reactivity of O2 and O toward reactant molecules is also briefly discussed in the context of catalysis. The reactivity of a surface toward O2 generally correlates with the adsorption strength of O, the tendency to oxidize, and the heat of formation of the oxide. Periodic trends can be rationalized in terms of attractive and repulsive interactions with the d-band, such that inert metals tend to feature a full d band that is low energy and has a large spatial overlap with adsorbate states. More open surfaces or undercoordinated defect sites can be much more reactive than close-packed surfaces. Reactions between O and other species tend to be more prevalent than reactions between O2 and other species, particularly on more reactive surfaces.

Effect of nanoscale flows on the surface structure of nanoporous catalysts.

The surface structure and composition of a multi-component catalyst are critical factors in determining its catalytic performance. The surface composition can depend on the local pressure of the reacting species, leading to the possibility that the flow through a nanoporous catalyst can affect its structure and reactivity. Here, we explore this possibility for oxidation reactions on nanoporous gold, an AgAu bimetallic catalyst. We use microscopy and digital reconstruction to obtain the morphology of a two-dimensional slice of a nanoporous gold sample. Using lattice Boltzmann fluid dynamics simulations along with thermodynamic models based on first-principles total-energy calculations, we show that some sections of this sample have low local O2 partial pressures when exposed to reaction conditions, which leads to a pure Au surface in these regions, instead of the active bimetallic AgAu phase. We also explore the effect of temperature on the surface structure and find that moderate temperatures (≈300-450 K) should result in the highest intrinsic catalytic performance, in apparent agreement with experimental results.

Controlling O coverage and stability by alloying Au and Ag.

One of the most critical factors in oxidation catalysis is controlling the state of oxygen on the surface. Au and Ag are both effective selective oxidation catalysts for various reactions, and their interactions with oxygen are critical for determining their catalytic performance. Here, we show that the state of oxygen on a catalytic surface can be controlled by alloying Au and Ag. Using temperature programmed desorption, density functional theory (DFT), and Monte Carlo simulations, we examine how alloying Au into an Ag(110) surface affects O2 dissociation, O coverage, and O stability. DFT calculations indicate that Au resides in the second layer, in agreement with previous experimental findings. The minimum ensemble size for O2 dissociation is 2 Ag atoms in adjacent rows of the second layer. Surprisingly, adsorbed O2 and the dissociation transition state interact directly with metal atoms in the adjacent trough, such that Au in this position inhibits O2 dissociation by direct repulsion with oxygen electronic states. Using Monte Carlo simulations based on DFT energetics, we create models of the surface that agree closely with our experimental results. Both show that the O2 uptake decreases nearly linearly as the Au concentration increases, and no O2 uptake occurs for Au concentrations above 50%. For Au concentrations greater than 18%, increasing the Au concentration also decreases the stability of the adsorbed O. Based on these results, the O coverage and O stability can be tuned, in some cases independently. We also study how the reactivity of the surface is affected by these factors using CO2 oxidation as a simple test reaction.

Hydrocarbon adsorption in an aqueous environment: A computational study of alkyls on Cu(111).

Hydrocarbon chains are important intermediates in various aqueous-phase surface processes, such as CO2 electroreduction, aqueous Fischer-Tropsch synthesis, and aqueous phase reforming of biomass-derived molecules. Further, the interaction between water and adsorbed hydrocarbons represents a difficult case for modern computational methods. Here, we explore various methods for calculating the energetics of this interaction within the framework of density functional theory and explore trade-offs between the use of low water coverages, molecular dynamics approaches, and minima hopping for identification of low energy structures. An effective methodology for simulating low temperature processes is provided by using a unit cell in which the vacuum space is filled with water, employing the minima hopping algorithm to search for low-lying minima, and including dispersion (van der Waals) interactions. Using this methodology, we show that a high coverage of adsorbed alkyls is destabilized by the presence of water, while a low coverage of alkyls is stabilized. Solvation has a small effect on the energetics of hydrocarbon chain growth, generally decreasing its favorability at low temperatures. We studied higher temperatures by running molecular dynamics simulations starting at the minima found by the minima hopping algorithm and found that increased temperatures facilitate chain growth. The self-consistent continuum solvation method effectively describes the alkyl-water interaction and is in general agreement with the explicit solvation results in most cases, but care should be taken at high alkyl coverage.

Catalyst design for enhanced sustainability through fundamental surface chemistry.

Decreasing energy consumption in the production of platform chemicals is necessary to improve the sustainability of the chemical industry, which is the largest consumer of delivered energy. The majority of industrial chemical transformations rely on catalysts, and therefore designing new materials that catalyse the production of important chemicals via more selective and energy-efficient processes is a promising pathway to reducing energy use by the chemical industry. Efficiently designing new catalysts benefits from an integrated approach involving fundamental experimental studies and theoretical modelling in addition to evaluation of materials under working catalytic conditions. In this review, we outline this approach in the context of a particular catalyst-nanoporous gold (npAu)-which is an unsupported, dilute AgAu alloy catalyst that is highly active for the selective oxidative transformation of alcohols. Fundamental surface science studies on Au single crystals and AgAu thin-film alloys in combination with theoretical modelling were used to identify the principles which define the reactivity of npAu and subsequently enabled prediction of new reactive pathways on this material. Specifically, weak van der Waals interactions are key to the selectivity of Au materials, including npAu. We also briefly describe other systems in which this integrated approach was applied.

A unified picture of adsorption on transition metals through different atoms.

A key issue in catalyst design is understanding how adsorption energies of surface intermediates vary across both different surfaces and various types of adsorbing atoms. In this work, we examine trends in adsorption energies of a wide variety of adsorbates that attach to transition metal surfaces through different atoms (H, C, N, O, F, S, etc.). All adsorption energies, as calculated by density functional theory, have nearly identical dependence on the metal bands (the d-band center and the number of p electrons) and the adsorbates' highest occupied molecular orbital (HOMO) energies. However, the dependence on the adsorbate-surface coupling and the d-band filling varies with the energy of the HOMO. Adsorbates with low HOMOs experience a higher level of Pauli repulsion than those with higher HOMOs. This leads to a classification of adsorbates into two groups, where adsorption energies in each group correlate. Even across the groups, adsorbates with similar HOMO energies are likely to have correlated adsorption energies.