Discovery Of Catalysts Research Articles

Boosting Alkaline Hydrogen Oxidation Activity of Ru Single-Atom Through Promoting Hydroxyl Adsorption on Ru/WC1- x Interfaces.

The sluggish kinetics of the hydrogen oxidation reaction (HOR) in alkaline conditions continue to pose a significant challenge for the practical implementation of anion-exchange membrane fuel cells. Developing single-atom catalysts can accelerate the pace of new HOR catalyst discovery for highly cost-effective and active HOR performance. However, single-atom catalysts (SACs) for the alkaline HOR have rarely been reported, and fundamental studies on the rational design of SACs are still required. Herein, the design of Ru SAC supported on the tungsten carbide (Ru SA/WC1- x ) via in situ high-temperature annealing strategy is reported. The resulting Ru SA/WC1- x catalyst exhibits remarkably enhanced HOR performance in alkaline media, a level of activity that can not be achieved with carbon-supported Ru SAC. Electrochemical results and density functional theory demonstrate that promoting the hydroxyl adsorption on Ru SA/WC1- x interfaces, which is derived from the low potential of zero charge of WC1- x support, has a significant effect on enhancing the HOR performance of SACs. This enhancement leads to 5.8 and 60.1 times higher Ru mass activity of Ru SA/WC1- x than Ru nanoparticles on carbon and Ru single-atom on N-doped carbon, respectively. This work provides new insights into the design of highly active SACs for alkaline HOR.

Language models and protocol standardization guidelines for accelerating synthesis planning in heterogeneous catalysis

Synthesis protocol exploration is paramount in catalyst discovery, yet keeping pace with rapid literature advances is increasingly time intensive. Automated synthesis protocol analysis is attractive for swiftly identifying opportunities and informing predictive models, however such applications in heterogeneous catalysis remain limited. In this proof-of-concept, we introduce a transformer model for this task, exemplified using single-atom heterogeneous catalysts (SACs), a rapidly expanding catalyst family. Our model adeptly converts SAC protocols into action sequences, and we use this output to facilitate statistical inference of their synthesis trends and applications, potentially expediting literature review and analysis. We demonstrate the model’s adaptability across distinct heterogeneous catalyst families, underscoring its versatility. Finally, our study highlights a critical issue: the lack of standardization in reporting protocols hampers machine-reading capabilities. Embracing digital advances in catalysis demands a shift in data reporting norms, and to this end, we offer guidelines for writing protocols, significantly improving machine-readability. We release our model as an open-source web application, inviting a fresh approach to accelerate heterogeneous catalysis synthesis planning.

Elucidating the Reactivity of Oxygenates on Single-Atom Alloy Catalysts.

Doping isolated transition metal atoms into the surface of coinage-metal hosts to form single-atom alloys (SAAs) can significantly improve the catalytic activity and selectivity of their monometallic counterparts. These atomically dispersed dopant metals on the SAA surface act as highly active sites for various bond coupling and activation reactions. In this study, we investigate the catalytic properties of SAAs with different bimetallic combinations [Ni-, Pd-, Pt-, and Rh-doped Cu(111), Ag(111), and Au(111)] for chemistries involving oxygenates relevant to biomass reforming. Density functional theory is employed to calculate and compare the formation energies of species such as methoxy (CH3O), methanol (CH3OH), and hydroxymethyl (CH2OH), thereby understanding the stability of these adsorbates on SAAs. Activation energies and reaction energies of C-O coupling, C-H activation, and O-H activation on these oxygenates are then computed. Analysis of the data in terms of thermochemical linear scaling and Bro̷nsted-Evans-Polanyi relationship shows that some SAAs have the potential to combine weak binding with low activation energies, thereby exhibiting enhanced catalytic behavior over their monometallic counterparts for key elementary steps of oxygenate conversion. This work contributes to the discovery and development of SAA catalysts toward greener technologies, having potential applications in the transition from fossil to renewable fuels and chemicals.

A review of mechanistic insights into CO2 reduction to higher alcohols for rational catalyst design

The utilization of captured CO2 for chemical synthesis could play an important role in reducing CO2 emissions. Higher alcohols stand out among various products of CO2 reduction due to high market prices and diverse applications, e.g., as fuel additives. However, developing catalysts for this reaction requires a profound understanding of the reaction mechanisms and catalyst design principles, which are discussed in the present review. Depending on the catalytic sites, higher alcohol synthesis could proceed via vastly different pathways. Herein, we outline how various proposed reaction mechanisms lead to different catalyst design strategies for optimizing the rate of CO2 conversion into reactive C1 intermediates (CO, CHx, CHxO, and HCOO) and their coupling into C2+ intermediates that are eventually converted into higher alcohols. Lastly, we discuss knowledge gaps in achieving rational catalyst design for higher alcohol synthesis and the breakthrough potential of machine-learning techniques for catalyst discovery.

Speeding up the Detection of Adsorbate Lateral Interactions in Graph-Theoretical Kinetic Monte Carlo Simulations.

Kinetic Monte Carlo (KMC) has become an indispensable tool in heterogeneous catalyst discovery, but realistic simulations remain computationally demanding on account of the need to capture complex and long-range lateral interactions between adsorbates. The Zacros software package (https://zacros.org) adopts a graph-theoretical cluster expansion (CE) framework that allows such interactions to be computed with a high degree of generality and fidelity. This involves solving a series of subgraph isomorphism problems in order to identify relevant interaction patterns in the lattice. In an effort to reduce the computational burden, we have adapted two well-known subgraph isomorphism algorithms, namely, VF2 and RI, for use in KMC simulations and implemented them in Zacros. To benchmark their performance, we simulate a previously established model of catalytic NO oxidation, treating the O* lateral interactions with a series of progressively larger CEs. For CEs with long-range interactions, VF2 and RI are found to provide impressive speedups relative to simpler algorithms. RI performs best, giving speedups reaching more than 150× when combined with OpenMP parallelization. We also simulate a recently developed methane cracking model, showing that RI offers significant improvements in performance at high surface coverages.

Advanced Catalyst Design and Reactor Configuration Upgrade in Electrochemical Carbon Dioxide Conversion.

Electrochemical carbon dioxide reduction reaction (CO2 RR) driven by renewable energy shows great promise in mitigating and potentially reversing the devastating effects of anthropogenic climate change and environmental degradation. The simultaneous synthesis of energy-dense chemicals can meet global energy demand while decoupling emissions from economic growth. However, the development of CO2 RR technology faces challenges in catalyst discovery and device optimization that hinder their industrial implementation. In this contribution, a comprehensive overview of the current state of CO2 RR research is provided, starting with the background and motivation for this technology, followed by the fundamentals and evaluated metrics. Then the underlying design principles of electrocatalysts are discussed, emphasizing their structure-performance correlations and advanced electrochemical assembly cells that can increase CO2 RR selectivity and throughput. Finally, the review looks to the future and identifies opportunities for innovation in mechanism discovery, material screening strategies, and device assemblies to move toward a carbon-neutral society.

Catalytic CO2 Reduction with Heptacoordinated Polypyridine Complexes: Switching the Selectivity via Metal Replacement.

The discovery of molecular catalysts for the CO2 reduction reaction (CO2 RR) in the presence of water, which are both effective and selective towards the generation of carbon-based products, is a critical task. Herein we report the catalytic activity towards the CO2 RR in acetonitrile/water mixtures by a cobalt complex and its iron analog both featuring the same redox-active ligand and an unusual seven-coordination environment. Bulk electrolysis experiments show that the cobalt complex mainly yields formate (52 % selectivity at an applied potential of -2.0 V vs Fc+ /Fc and 1 % H2 O) or H2 (up to 86 % selectivity at higher applied bias and water content), while the iron complex always delivers CO as the major product (selectivity >74 %). The different catalytic behavior is further confirmed under photochemical conditions with the [Ru(bpy)3 ]2+ sensitizer (bpy=2,2'-bipyridine) and N,N-diisopropylethylamine as electron donor, where the cobalt complex leads to preferential H2 formation (up to 89 % selectivity), while the iron analog quantitatively generates CO (up to 88 % selectivity). This is ascribed to a preference towards a metal-hydride vs. a metal-carboxyl pathway for the cobalt and the iron complex, respectively, and highlights how metal replacement may effectively impact on the reactivity of transition metal complexes towards solar fuel formation.

Data-driven discovery of electrocatalysts for CO2 reduction using active motifs-based machine learning

The electrochemical carbon dioxide reduction reaction (CO2RR) is an attractive approach for mitigating CO2 emissions and generating value-added products. Consequently, discovery of promising CO2RR catalysts has become a crucial task, and machine learning (ML) has been utilized to accelerate catalyst discovery. However, current ML approaches are limited to exploring narrow chemical spaces and provide only fragmentary catalytic activity, even though CO2RR produces various chemicals. Here, by merging pre-developed ML model and a CO2RR selectivity map, we establish high-throughput virtual screening strategy to suggest active and selective catalysts for CO2RR without being limited to a database. Further, this strategy can provide guidance on stoichiometry and morphology of the catalyst to researchers. We predict the activity and selectivity of 465 metallic catalysts toward four expected reaction products. During this process, we discover previously unreported and promising behavior of Cu-Ga and Cu-Pd alloys. These findings are then validated through experimental methods.

Mechanistic Studies on the Bismuth-Catalyzed Transfer Hydrogenation of Azoarenes.

Organobismuth-catalyzed transfer hydrogenation has recently been disclosed as an example of low-valent Bi redox catalysis. However, its mechanistic details have remained speculative. Herein, we report experimental and computational studies that provide mechanistic insights into a Bi-catalyzed transfer hydrogenation of azoarenes using p-trifluoromethylphenol (4) and pinacolborane (5) as hydrogen sources. A kinetic analysis elucidated the rate orders in all components in the catalytic reaction and determined that 1 a (2,6-bis[N-(tert-butyl)iminomethyl]phenylbismuth) is the resting state. In the transfer hydrogenation of azobenzene using 1 a and 4, an equilibrium between 1 a and 1 a ⋅ [OAr]2 (Ar=p-CF3 -C6 H4 ) is observed, and its thermodynamic parameters are established through variable-temperature NMR studies. Additionally, pKa -gated reactivity is observed, validating the proton-coupled nature of the transformation. The ensuing 1 a ⋅ [OAr]2 is crystallographically characterized, and shown to be rapidly reduced to 1 a in the presence of 5. DFT calculations indicate a rate-limiting transition state in which the initial N-H bond is formed via concerted proton transfer upon nucleophilic addition of 1 a to a hydrogen-bonded adduct of azobenzene and 4. These studies guided the discovery of a second-generation Bi catalyst, the rate-limiting transition state of which is lower in energy, leading to catalytic transfer hydrogenation at lower catalyst loadings and at cryogenic temperature.

Co-Co Dinuclear Active Sites Dispersed on Zirconium-doped Heterostructured Co9 S8 /Co3 O4 for High-current-density and Durable Acidic Oxygen Evolution.

Developing cost-effective and sustainable acidic water oxidation catalysts requires significant advances in material design and in-depth mechanism understanding for proton exchange membrane water electrolysis. Herein, we developed a single atom regulatory strategy to construct Co-Co dinuclear active sites (DASs) catalysts that atomically dispersed zirconium doped Co9 S8 /Co3 O4 heterostructure. The X-ray absorption fine structure elucidated the incorporation of Zr greatly facilitated the generation of Co-Co DASs layer with stretching of cobalt oxygen bond and S-Co-O heterogeneous grain boundaries interfaces, engineering attractive activity of significantly reduced overpotential of 75 mV at 10 mA cm-2 , a breakthrough of 500 mA cm-2 high current density, and water splitting stability of 500 hours in acid, making it one of the best-performing acid-stable OER non-noble metal materials. The optimized catalyst with interatomic Co-Co distance (ca. 2.80 Å) followed oxo-oxo coupling mechanism that involved obvious oxygen bridges on dinuclear Co sites (1,090 cm-1 ), confirmed by in situ SR-FTIR, XAFS and theoretical simulations. Furthermore, a major breakthrough of 120,000 mA g-1 high mass current density using the first reported noble metal-free cobalt anode catalyst of Co-Co DASs/ZCC in PEM-WE at 2.14 V was recorded.

The design and discovery of catalysts for simultaneous catalysis of chlorobenzene and nitrogen oxides via domain knowledge guided machine learning

A domain knowledge guided machine learning (ML) strategy is proposed to design and discover catalysts for the simultaneous catalysis of chlorobenzene (CB) and nitrogen oxides (NOx). Symbolic regression (SR) is used to find an analytic formula for the acidity of metallic ions and a few ML regression models are developed for the redox ability in terms of standard reduction potential (SRP) of metallic ions, resulting in the recommendations of bi-functions catalysts. The domain knowledge considers that metallic ions with acidity values in the range of 0.5–1.2 are suitable for both catalytic support and active components, while catalytic active component requires metallic ions with high redox ability (SRP>0.4). Symbolic regression gives an analytic formula of ion acidity and the support vector regression model finds the redox ability from the available features. At last, experimental and characterization results verify catalytic performances of the screened-out catalyst formulas.

Copernic Catalysts announces research collaboration with Schrodinger to accelerate sustainable catalyst discovery

Copernic Catalysts announces research collaboration with Schrodinger to accelerate sustainable catalyst discovery

Toward De Novo Catalyst Discovery: Fast Identification of New Catalyst Candidates for Alcohol‐Mediated Morita–Baylis–Hillman Reactions**

AbstractRecently we have demonstrated how a genetic algorithm (GA) starting from random tertiary amines can be used to discover a new and efficient catalyst for the alcohol‐mediated Morita–Baylis–Hillman (MBH) reaction. In particular, the discovered catalyst was shown experimentally to be eight times more active than DABCO, commonly used to catalyze the MBH reaction. This represents a breakthrough in using generative models for catalyst optimization. However, the GA procedure, and hence discovery, relied on two important pieces of information; 1) the knowledge that tertiary amines catalyze the reaction and 2) the mechanism and reaction profile for the catalyzed reaction, in particular the transition state structure of the rate‐determining step. Thus, truly de novo catalyst discovery must include these steps. Here we present such a method for discovering catalyst candidates for a specific reaction while simultaneously proposing a mechanism for the catalyzed reaction. We show that tertiary amines and phosphines are potential catalysts for the MBH reaction by screening 11 molecular templates representing common functional groups. The method relies on an automated reaction discovery workflow using meta‐dynamics calculations. Combining this method for catalyst candidate discovery with our GA‐based catalyst optimization method results in an algorithm for truly de novo catalyst discovery.

Toward De Novo Catalyst Discovery: Fast Identification of New Catalyst Candidates for Alcohol-Mediated Morita-Baylis-Hillman Reactions.

Recently we have demonstrated how a genetic algorithm (GA) starting from random tertiary amines can be used to discover a new and efficient catalyst for the alcohol-mediated Morita-Baylis-Hillman (MBH) reaction. In particular, the discovered catalyst was shown experimentally to be eight times more active than DABCO, commonly used to catalyze the MBH reaction. This represents a breakthrough in using generative models for catalyst optimization. However, the GA procedure, and hence discovery, relied on two important pieces of information; 1) the knowledge that tertiary amines catalyze the reaction and 2) the mechanism and reaction profile for the catalyzed reaction, in particular the transition state structure of the rate-determining step. Thus, truly de novo catalyst discovery must include these steps. Here we present such a method for discovering catalyst candidates for a specific reaction while simultaneously proposing a mechanism for the catalyzed reaction. We show that tertiary amines and phosphines are potential catalysts for the MBH reaction by screening 11 molecular templates representing common functional groups. The method relies on an automated reaction discovery workflow using meta-dynamics calculations. Combining this method for catalyst candidate discovery with our GA-based catalyst optimization method results in an algorithm for truly de novo catalyst discovery.

Discovery of Graphene Growth Alloy Catalysts Using High-Throughput Machine Learning.

Despite today's commercial-scale graphene production using chemical vapor deposition (CVD), the growth of high-quality single-layer graphene with controlled morphology and crystallinity remains challenging. Considerable effort is still spent on designing improved CVD catalysts for producing high-quality graphene. Conventionally, however, catalyst design has been pursued using empirical intuition or trial-and-error approaches. Here, we combine high-throughput density functional theory and machine learning to identify new prospective transition metal alloy catalysts that exhibit performance comparable to that of established graphene catalysts, such as Ni(111) and Cu(111). The alloys identified through this process generally consist of combinations of early- and late-transition metals, and a majority are alloys of Ni or Cu. Nevertheless, in many cases, these conventional catalyst metals are combined with unconventional partners, such as Zr, Hf, and Nb. The approach presented here therefore highlights an important new approach for identifying novel catalyst materials for the CVD growth of low-dimensional nanomaterials.

Bayesian Optimization-guided Discovery of High-performance Methane Combustion Catalysts based on Multi-component PtPd@CeZrOx Core-Shell Nanospheres.

Formula regulation of multi-component catalysts by manual search is undoubtedly a time-consuming task, which has severely impeded the development efficiency of high-performance catalysts. In this work, PtPd@CeZrOx core-shell nanospheres, as a successful case study, is explicitly demonstrated how Bayesian optimization (BO) accelerates the discovery of methane combustion catalysts with the optimal formula ratio (the Pt/Pd mole ratio ranges from 1/2.33-1/9.09, and Ce/Zr from 1/0.22-1/0.35), which directly results in a lower conversion temperature (T50 approaching to 330 °C) than ones reported hitherto. Consequently, the best sample obtained could be efficiently developed after two rounds of iterations, containing only 18 experiments in all that is far less than the common human workload via the traditional trial-and-error search for optimal compositions. Further, this BO-based machine learning strategy can be straightforward extended to serve the autonomous discovery in multi-component material systems, for other desired properties, showing promising opportunities to practical applications in future.

Bayesian Optimization‐guided Discovery of High‐performance Methane Combustion Catalysts based on Multi‐component PtPd@CeZrOx Core–Shell Nanospheres

AbstractFormula regulation of multi‐component catalysts by manual search is undoubtedly a time‐consuming task, which has severely impeded the development efficiency of high‐performance catalysts. In this work, PtPd@CeZrOx core–shell nanospheres, as a successful case study, is explicitly demonstrated how Bayesian optimization (BO) accelerates the discovery of methane combustion catalysts with the optimal formula ratio (the Pt/Pd mole ratio ranges from 1/2.33–1/9.09, and Ce/Zr from 1/0.22–1/0.35), which directly results in a lower conversion temperature (T50 approaching to 330 °C) than ones reported hitherto. Consequently, the best sample obtained could be efficiently developed after two rounds of iterations, containing only 18 experiments in all that is far less than the common human workload via the traditional trial‐and‐error search for optimal compositions. Further, this BO‐based machine learning strategy can be straightforward extended to serve the autonomous discovery in multi‐component material systems, for other desired properties, showing promising opportunities to practical applications in future.

Efficient activation of peroxymonosulfate by Fe single-atom: The key role of Fe-pyrrolic nitrogen coordination in generating singlet oxygen and high-valent Fe species

Nitrogen-doped carbon matrix single-atom catalysts (SACs) for the efficient removal of organic pollutants have attracted widespread attention. However, the ligand structure and the origin of the high activity between nitrogen species and single-atoms remain elusive. Herein, nitrogen-doped carbon matrix iron single-atom catalysts (Fe/NC-SACs) that exhibit high catalytic reactivity (98.2% SMX degradation in 5 min), broad pH resistance (pH 3.0–11.1), high stability, and sustainable water treatment capacity are reported. High-valent iron (Fe IV=O) and singlet oxygen (1O2) were the reactive oxygen species observed. The electrochemical results demonstrated the generation of catalyst-PMS complexes. The DFT calculations revealed that Fe-pyrrolic N4 was the best ligand for PMS, exhibiting the highest adsorption energy, bond length variation and electron transfer capacity. The central Fe single atom and the carbon electrons adjacent to the pyrrolic N were the reactive sites of the PMS. The main source of 1O2 was the oxidation of PMS. This work provides guidance for the discovery of high-performance catalysts and provides a single-atom catalyst that can be used for practical environmental purification.

Machine Learning-Enabled Predictions of Condensed Fukui Functions and Designing of Metal Pincer Complexes for Catalytic Hydrogenation of CO2.

This research showcases the machine learning (ML)-enabled homogeneous catalyst discovery to be employed in carbon dioxide hydrogenation. To achieve the desired turnover frequency (TOF), the electrophilicity of the central metal atom is a crucial factor in transition metal pincer complexes. The condensed Fukui function is a direct measure of the catalytic performance of these pincer complexes. Herein, we demonstrate that machine learning is a convenient and effiecient method to calculate condensed Fukui functions of the central metal atom. The electrophilicity values of 202 pincer complexes were calculated by using density functional theory (DFT) to train the ML model. The test data of the experimentally established pincer complexes show a direct linkage between calculated electrophilicity and experimental TOF. Further, this data was used to develop an ML protocol to screen 2,84,062 catalyst complexes to get the electrophilicity values of the Mn, Fe, Co, and Ni transition metals encompassing various permutation combinations of PNP, PNN, NNN, and PCP pincer ligands. These findings validate the efficacy of machine learning in the rapid screening of metal pincer catalysts based on condensed Fukui functions.

Accelerated discovery of multi-elemental reverse water-gas shift catalysts using extrapolative machine learning approach

Designing novel catalysts is key to solving many energy and environmental challenges. Despite the promise that data science approaches, including machine learning (ML), can accelerate the development of catalysts, truly novel catalysts have rarely been discovered through ML approaches because of one of its most common limitations and criticisms—the assumed inability to extrapolate and identify extraordinary materials. Herein, we demonstrate an extrapolative ML approach to develop new multi-elemental reverse water-gas shift catalysts. Using 45 catalysts as the initial data points and performing 44 cycles of the closed loop discovery system (ML prediction + experiment), we experimentally tested a total of 300 catalysts and identified more than 100 catalysts with superior activity compared to those of the previously reported high-performance catalysts. The composition of the optimal catalyst discovered was Pt(3)/Rb(1)-Ba(1)-Mo(0.6)-Nb(0.2)/TiO2. Notably, niobium (Nb) was not included in the original dataset, and the catalyst composition identified was not predictable even by human experts.