Machine-Learning-Assisted Discovery of Mechanosynthesized Lead-Free Metal Halide Perovskites for the Oxidative Photocatalytic Cleavage of Alkenes.

Lead-free metal halide perovskites can potentially be air- and water-stable photocatalysts for organic synthesis, but there are limited studies on them for this application. Separately, machine learning (ML), a critical subfield of artificial intelligence, has played a pivotal role in identifying correlations and formulating predictions based on extensive datasets. Herein, an iterative workflow by incorporating high-throughput experimental data with ML to discover new lead-free metal halide perovskite photocatalysts for the aerobic oxidation of styrene is described. Through six rounds of ML optimization guided by SHapley Additive exPlanations (SHAP) analysis, BA2CsAg0.95Na0.05BiBr7 as a photocatalyst that afforded an 80% yield of benzoic acid under the standard conditions is identified, which is a 13-fold improvement compared to the 6% with when using Cs2AgBiBr6 as the initial photocatalyst benchmark that is started. BA2CsAg0.95Na0.05BiBr7 can tolerate various functional groups with 22 styrene derivatives, highlighting the generality of the photocatalytic properties demonstrated. Radical scavenging studies and density functional theory calculations revealed that the formation of the reactive oxygen species superoxide and singlet oxygen in the presence of BA2CsAg0.95Na0.05BiBr7 are critical for photocatalysis.

Tuning the Work Function of MXene via Surface Functionalization.

MXenes, a class of two-dimensional materials, have garnered significant attention due to their versatile surface chemistry and customizable properties. In this study, we investigate the work function (WF) tuning capabilities of MXene Ti3C2Tx, where Tx denotes the surface termination, synthesized via both conventional hydrogen fluoride-etched and recently reported molten salt-etched routes. When MXene samples are subjected to gas phase reactions, WF variations exceeding 0.6 eV are achieved, highlighting the potential for precise WF control. Notably, the WF increases from ∼4.23 eV (in N-doped MXene etched using molten salt) to ∼4.85 eV (N-doped MXene etched using HF). Complementary density functional theory (DFT) calculations reveal WF tuning across a >1 eV range via modification of the surface with different terminal groups (bare metal, F*, O*, N*, and Cl*). These changes in WF are attributed to surface termination modifications and the formation of TiO2 and TiN phases during annealing. DFT calculations further unveil an inverse correlation between the WF and the electron affinity of surface terminations. The findings from this comprehensive study provide insights into the tunable WF of MXenes, paving the way for their potential applications as interfacial layers in photovoltaic, energy conversion, and storage technologies.

Strong Metal-Support Interaction Boosts Activity, Selectivity, and Stability in Electrosynthesis of H2O2.

Noble metals have an irreplaceable role in catalyzing electrochemical reactions. However, large overpotential and poor long-term stability still prohibit their usage in many reactions (e.g., oxygen evolution/reduction). With regard to the low natural abundance, the improvement of their overall electrocatalytic performance (activity, selectivity, and stability) was urgently necessary. Herein, strong metal-support interaction (SMSI) was modulated through an unprecedented time-dependent mechanical milling method on Pd-loaded oxygenated TiC electrocatalysts. The encapsulation of Pd surfaces with reduced TiO2-x overlayers is precisely controlled by the mechanical milling time. This encapsulation induced a valence band restructuring and lowered the d-band center of surface Pd atoms. For hydrogen peroxide electrosynthesis through the two-electron oxygen reduction reaction (ORR), these electronic and geometric modifications resulted in optimal adsorption energies of reaction intermediates. Thus, SMSI phenomena not only enhanced electrocatalytic activity and selectivity but also created an encapsulating oxide overlayer that protected the Pd species, increasing its long-term stability. This SMSI induced by mechanical milling was also extended to other noble metal systems, showing great promise for the large-scale production of highly stable and tunable electrocatalysts.

Molecule Confined Isolated Metal Sites Enable the Electrocatalytic Synthesis of Hydrogen Peroxide.

The direct synthesis of hydrogen peroxide (H2 O2 ) through the two-electron oxygen reduction reaction is a promising alternative to the industrial anthraquinone oxidation process. Selectivity to H2 O2 is however limited by the four-electron pathway during oxygen reduction. Herein, it is reported that aminoanthraquinone confined isolated metal sites on carbon supports selectively steer oxygen reduction to H2 O2 through the two-electron pathway. Confining isolated NiNx sites under aminoanthraquinone increases the selectivity to H2 O2 from below 55% to above 80% over a wide potential range. Spectroscopy characterization and density functional theory calculations indicate that isolated NiNx sites are confined within a nanochannel formed between the molecule and the carbon support. The confinement reduces the thermodynamic barrier for OOH* desorption versus further dissociation, thus increasing the selectivity to H2 O2 . It is revealed how tailoring noncovalent interactions beyond the binding site can empower electrocatalysts for the direct synthesis of H2 O2 through oxygen reduction.

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.

Strong Metal-Support Interaction for 2D Materials: Application in Noble Metal/TiB2 Heterointerfaces and their Enhanced Catalytic Performance for Formic Acid Dehydrogenation.

Strong metal-support interaction (SMSI) is a phenomenon commonly observed on heterogeneous catalysts. Here, direct evidence of SMSI between noble metal and 2D TiB2 supports is reported. The temperature-induced TiB2 overlayers encapsulate the metal nanoparticles, resulting in core-shell nanostructures that are sintering-resistant with metal loadings as high as 12.0 wt%. The TiOx -terminated TiB2 surfaces are the active sites catalyzing the dehydrogenation of formic acid at room temperature. In contrast to the trade-off between stability and activity in conventional SMSI, TiB2 -based SMSI promotes catalytic activity and stability simultaneously. By optimizing the thickness and coverage of the overlayer, the Pt/TiB2 catalyst displays an outstanding hydrogen productivity of 13.8 mmol g-1 cat h-1 in 10.0 m aqueous solution without any additive or pH adjustment, with >99.9% selectivity toward CO2 and H2 . Theoretical studies suggest that the TiB2 overlayers are stabilized on different transition metals through an interplay between covalent and electrostatic interactions. Furthermore, the computationally determined trends in metal-TiB2 interactions are fully consistent with the experimental observations regarding the extent of SMSI on different transition metals. The present research introduces a new means to create thermally stable and catalytically active metal/support interfaces for scalable chemical and energy applications.

Synthesizing High-Volume Chemicals from CO2 without Direct H2 Input.

Decarbonizing the chemical industry will eventually entail using CO2 as a feedstock for chemical synthesis. However, many chemical syntheses involve CO2 reduction using inputs such as renewable hydrogen. In this review, chemical processes are discussed that use CO2 as an oxidant for upgrading hydrocarbon feedstocks. The captured CO2 is inherently reduced by the hydrocarbon co-reactants without consuming molecular hydrogen or renewable electricity. This CO2 utilization approach can be potentially applied to synthesize eight emission-intensive molecules, including olefins and epoxides. Catalytic systems and reactor concepts are discussed that can overcome practical challenges, such as thermodynamic limitations, over-oxidation, coking, and heat management. Under the best-case scenario, these hydrogen-free CO2 reduction processes have a combined CO2 abatement potential of approximately 1 gigatons per year and avoid the consumption of 1.24 PWh renewable electricity, based on current market demand and supply.

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.

Predicting Adsorption Properties of Catalytic Descriptors on Bimetallic Nanoalloys with Site-Specific Precision.

Bimetallic nanoparticles present a vastly tunable structural and compositional design space rendering them promising materials for catalytic and energy applications. Yet it remains an enduring challenge to efficiently screen candidate alloys with atomic level specificity while explicitly accounting for their inherent stabilities under reaction conditions. Herein, by leveraging correlations between binding energies of metal adsorption sites and metal-adsorbate complexes, we predict adsorption energies of typical catalytic descriptors (OH*, CH3*, CH*, and CO*) on bimetallic alloys with site-specific resolution. We demonstrate that our approach predicts adsorption energies on top and bridge sites of bimetallic nanoparticles having generic morphologies and chemical environments with errors between 0.09 and 0.18 eV. By forging a link between the inherent stability of an alloy and the adsorption properties of catalytic descriptors, we can now identify active site motifs in nanoalloys that possess targeted catalytic descriptor values while being thermodynamically stable under working conditions.

A coordination-based model for transition metal alloy nanoparticles.

We present a simple approach for predicting the relative energies of bimetallic nanoparticles spanning a wide-ranging combinatorial space, using only the identity and nearest-neighbor coordination number of individual metal atoms as independent parameters. By performing straightforward metal atom adsorption calculations on surface slab models, we parameterize expressions for the energy of metal atoms as a function of their coordination number in 21 bimetallic pairings of fcc metals. We rigorously establish the transferability of our model by predicting relative energies of a series of nanoparticles across a large number of morphologies, sizes, atomic compositions, and arrangements. The model is particularly accurate in predicting atomic rearrangements at or near the metal surfaces, which is essential for its potential applications when studying segregation phenomena or dynamic processes in heterogeneous catalysis. By rapidly forecasting site stabilities with atomic specificity across generic structural and compositional features, our model is able to reverse engineer thermodynamically feasible motifs of active sites in bimetallic nanoparticles through robust property ⇔ structure relations.

Electrostatic Origins of Linear Scaling Relationships at Bifunctional Metal/Oxide Interfaces: A Case Study of Au Nanoparticles on Doped MgO Substrates.

Linear scaling relationships (SRs), which relate binding energies of adsorbates across a space of catalyst surfaces, have been extensively explored for metal and oxide surfaces, but little is known about their properties at interfaces between metal nanoparticles and oxide supports, which are ubiquitous in heterogeneous catalysis. Using periodic DFT calculations, scaling principles are extended to bifunctional Au/oxide interfaces. Adopting a Au nanorod on doped MgO (100) as a model, SRs for species participating in water gas shift, methanol synthesis, and oxidation reactions are reported. SR slopes are not constrained by the bond order conservation rule postulated for metals, oxides, and zeolites, potentially permitting greater flexibility in catalyst design strategies. The deviation from bond counting, along with the physical origin of scaling behavior at interfaces, are explored using a conceptual framework involving electrostatic interactions at the Au/oxide interface.

A Discovery of Strong Metal-Support Bonding in Nanoengineered Au-Fe3O4 Dumbbell-like Nanoparticles by in Situ Transmission Electron Microscopy.

The strength of metal-support bonding in heterogeneous catalysts determines their thermal stability, therefore, a tremendous amount of effort has been expended to understand metal-support interactions. Herein, we report the discovery of an anomalous "strong metal-support bonding" between gold nanoparticles and "nano-engineered" Fe3O4 substrates by in situ microscopy. During in situ vacuum annealing of Au-Fe3O4 dumbbell-like nanoparticles, synthesized by the epitaxial growth of nano-Fe3O4 on Au nanoparticles, the gold nanoparticles transform into the gold thin films and wet the surface of nano-Fe3O4, as the surface reduction of nano-Fe3O4 proceeds. This phenomenon results from a unique coupling of the size-and shape-dependent high surface reducibility of nano-Fe3O4 and the extremely strong adhesion between Au and the reduced Fe3O4. This strong metal-support bonding reveals the significance of controlling the metal oxide support size and morphology for optimizing metal-support bonding and ultimately for the development of improved catalysts and functional nanostructures.