High-Throughput Experimentation and Catalyst Informatics for Oxidative Coupling of Methane

Atomic Design of Alkyne Semihydrogenation Catalysts via Active Learning

Alkyne hydrogenation on palladium-based catalysts modified with silver is currently used in industry to eliminate trace amounts of alkynes in alkenes produced from steam cracking and alkane dehydrogenation processes. Intensive efforts have been devoted to designing an alternative catalyst for improvement, especially in terms of selectivity and catalyst cost, which is still far away from that as expected. Here, we describe an atomic design of a high-performance Ni-based intermetallic catalyst aided by active machine learning combined with density functional theory calculations. The engineered NiIn catalyst exhibits >97% selectivity to ethylene and propylene at the full conversion of acetylene and propyne at mild temperature, outperforming the reported Ni-based catalysts and even noble Pd-based ones. Detailed mechanistic studies using theoretical calculations and advanced characterizations elucidate that the atomic-level defined coordination environment of Ni sites and well-designed hybridization of Ni 3d with In 5p orbital determine the semihydrogenation pathway.

High-throughput experimentation for photocatalytic water purification in practical environments

High-throughput screening instrument was developed for photocatalytic water purification, enabling the simultaneous testing of 132 photocatalytic reactions under uniform visible light irradiation, temperature control, and stirring. The instrument was used to investigate the effects of different catalysts (TiO2, ZnO, α-Fe2O3) and environmental waters (seawater, urban wastewater, and industrial wastewater) on dye degradation. It was observed environmental ions, particularly carbonate and phosphate ions, significantly reduced catalyst activity by inhibiting the adsorption of dye molecules. To develop effective catalysts for dye degradation in industrial wastewater, 15 types of noble metal nanoparticles (NPs) were supported on photocatalysts. The study found that noble metal NPs with high work functions and oxidation resistance, such as Au and Pt, exhibited higher activity even in the industrial wastewater, likely converting environmental ions into active species. These findings, based on 432 test results, demonstrate the effectiveness of the developed high-throughput screening instrument for optimizing photocatalytic water purification.

Automatic feature engineering for catalyst design using small data without prior knowledge of target catalysis

The empirical aspect of descriptor design in catalyst informatics, particularly when confronted with limited data, necessitates adequate prior knowledge for delving into unknown territories, thus presenting a logical contradiction. This study introduces a technique for automatic feature engineering (AFE) that works on small catalyst datasets, without reliance on specific assumptions or pre-existing knowledge about the target catalysis when designing descriptors and building machine-learning models. This technique generates numerous features through mathematical operations on general physicochemical features of catalytic components and extracts relevant features for the desired catalysis, essentially screening numerous hypotheses on a machine. AFE yields reasonable regression results for three types of heterogeneous catalysis: oxidative coupling of methane (OCM), conversion of ethanol to butadiene, and three-way catalysis, where only the training set is swapped. Moreover, through the application of active learning that combines AFE and high-throughput experimentation for OCM, we successfully visualize the machine's process of acquiring precise recognition of the catalyst design. Thus, AFE is a versatile technique for data-driven catalysis research and a key step towards fully automated catalyst discoveries.

Parallel Catalyst Synthesis Protocol for Accelerating Heterogeneous Olefin Polymerization Research

The data scientific approach has become an indispensable tool for capturing structure-performance relationships in complex systems, where the quantity and quality of data play a crucial role. In heterogeneous olefin polymerization research, the exhaustive and multi-step nature of Ziegler-Natta catalyst synthesis has long posed a bottleneck in synthetic throughput and data generation. In this contribution, a custom-designed 12-parallel reactor system and a catalyst synthesis protocol were developed to achieve the parallel synthesis of a magnesium ethoxide-based Ziegler-Natta catalyst. The established system, featuring a miniature reaction vessel with magnetically suspended stirring, allows for over a tenfold reduction in synthetic scale while ensuring the consistency and reliability of the synthesis. We demonstrate that the established protocol is highly efficient for the generation of a catalyst library with diverse compositions and physical features, holding promise as a foundation for the data-driven establishment of the structure-performance relationship in heterogeneous olefin polymerization catalysis.

Structure-Based Multilevel Descriptors for High-throughput Screening of Elastomers

To discover new materials, high-throughput screening (HTS) with machine learning (ML) requires universally available descriptors that can accurately predict the desired properties. For elastomers, experimental and simulation data in current descriptors may not be available for all candidates of interest, hindering elastomer discovery through HTS. To address this challenge, we introduce structure-based multilevel (SM) descriptors of elastomers derived solely from molecular structure that is universally available. Our SM descriptors are hierarchically organized to capture both local soft and hard segment structures as well as the global structures of elastomers. With the SM-Morgan Fingerprint (SM-MF) descriptor, one of our SM descriptors, a machine learning model accurately predicts elastomer toughness with a remarkable accuracy of 0.91. Furthermore, an HTS pipeline is established to swiftly screen elastomers with targeted toughness. We also demonstrate the generality and applicability of SM descriptors by using them to construct HTS pipelines for screening elastomers with a targeted critical strain or Young's modulus. The user-friendliness and low computational cost of SM descriptors make them a promising tool to significantly enhance HTS in the search for novel materials.

Role and dynamics of transition metal carbides in methane coupling

Transition metal carbides have numerous applications and are known to excel in terms of hardness, thermal stability and conductivity. In particular, the Pt-like behavior of Mo and W carbides has led to the popularization of metal carbides in catalysis, ranging from electrochemically-driven reactions to thermal methane coupling. Herein, we show the active participation of carbidic carbon in the formation of C₂ products during methane coupling at high temperature that is associated with the dynamics of Mo and W carbides. A detailed mechanistic study reveals that the catalyst performance of these metal carbides can be traced back to its carbon diffusivity and exchange capability upon interaction with methane (gas phase carbon). A stable C₂ selectivity over time on stream for Mo carbide (Mo₂C) can be rationalized by fast carbon diffusion dynamics, while W carbide (WC) shows loss of selectivity due to slow diffusion leading to surface carbon depletion. This finding showcases that the bulk carbidic carbon of the catalyst plays a crucial role and that the metal carbide is not only responsible for methyl radical formation. Overall, this study evidences the presence of a carbon equivalent to the Mars-Van Krevelen type mechanism for non-oxidative coupling of methane.

Machine Learning Descriptors for Data-Driven Catalysis Study

Traditional trial-and-error experiments and theoretical simulations have difficulty optimizing catalytic processes and developing new, better-performing catalysts. Machine learning (ML) provides a promising approach for accelerating catalysis research due to its powerful learning and predictive abilities. The selection of appropriate input features (descriptors) plays a decisive role in improving the predictive accuracy of ML models and uncovering the key factors that influence catalytic activity and selectivity. This review introduces tactics for the utilization and extraction of catalytic descriptors in ML-assisted experimental and theoretical research. In addition to the effectiveness and advantages of various descriptors, their limitations are also discussed. Highlighted are both 1) newly developed spectral descriptors for catalytic performance prediction and 2) a novel research paradigm combining computational and experimental ML models through suitable intermediate descriptors. Current challenges and future perspectives on the application of descriptors and ML techniques to catalysis are also presented.

Toward the Golden Age of Materials Informatics: Perspective and Opportunities

Materials informatics is reaching the transition point and is evolving from its early stages of adoption and development and moving toward its golden age. Here, the transformation of the early stage of materials informatics toward the next level of materials informatics is explored. In particular, it has become crucial to be able to manipulate materials synthesis data, materials properties data, and materials characterization data. Through the use of ontology, material design and understanding can be carried out simultaneously in a whitebox manner. Here, a perspective on the ultimate goal of materials informatics along with potential key components is discussed.

Catalysts informatics: paradigm shift towards data-driven catalyst design

Designing catalysts is a challenging matter as catalysts are involved with various factors that impact synthesis, catalysts, reactor and reaction. In order to overcome these difficulties, catalysts informatics is proposed as an alternative way to design and understand catalysts. The underlying concept of catalysts informatics is to design the catalysts from trends and patterns found in catalysts data. Here, three key concepts are introduced: experimental catalysts database, knowledge extraction from catalyst data via data science, and a catalysts informatics platform. Methane oxidation is chosen as a prototype reaction for demonstrating various aspects of catalysts informatics. This work summarizes how catalysts informatics plays a role in catalyst design. The work covers big data generation via high throughput experiments, machine learning, catalysts network method, catalyst design from small data, catalysts informatics platform, and the future of catalysts informatics via ontology. Thus, the proposed catalysts informatics would help innovate how catalysts can be designed and understood.

Effects of Rare Earth Metal Promotion over Zeolite-Supported Fe-Cu-Based Catalysts on the Light Olefin Production Performance in Fischer-Tropsch Synthesis

Fischer-Tropsch synthesis (FTS), a significant reaction for effective H₂ utilization, is a promising approach for direct production of light olefins from syngas (H₂ + CO). For the FT-Olefin process, an efficient catalyst restricting the product distribution of FTS to light olefins is required. Aligned with this goal, we synthesized 24 catalysts comprising Fe and Cu in combination with rare earth metals (La, Ce, Nd, Ho, Er) and zeolite supports (ultrastable Y and mordenite). FT-Olefin performances of these catalysts were screened using a high-throughput test system at atmospheric pressure, and then promising catalysts were tested under high pressure in a conventional test system. Results show that Nd increases selectivity to light olefins and Ho suppresses C₅+ and coke formation. It is also demonstrated that zeolite-metal interaction, leading to a mixture of both acidic and basic sites, is significant in increasing light olefin production. The mordenite-supported 20 wt % Fe, 0.5 wt % Cu, and 0.5 wt % Ho catalyst provides the highest light olefin yield with the lowest coke and heavier hydrocarbon selectivity.

Surface and Interface Coordination Chemistry Learned from Model Heterogeneous Metal Nanocatalysts: From Atomically Dispersed Catalysts to Atomically Precise Clusters

The surface and interface coordination structures of heterogeneous metal catalysts are crucial to their catalytic performance. However, the complicated surface and interface structures of heterogeneous catalysts make it challenging to identify the molecular-level structure of their active sites and thus precisely control their performance. To address this challenge, atomically dispersed metal catalysts (ADMCs) and ligand-protected atomically precise metal clusters (APMCs) have been emerging as two important classes of model heterogeneous catalysts in recent years, helping to build bridge between homogeneous and heterogeneous catalysis. This review illustrates how the surface and interface coordination chemistry of these two types of model catalysts determines the catalytic performance from multiple dimensions. The section of ADMCs starts with the local coordination structure of metal sites at the metal-support interface, and then focuses on the effects of coordinating atoms, including their basicity and hardness/softness. Studies are also summarized to discuss the cooperativity achieved by dual metal sites and remote effects. In the section of APMCs, the roles of surface ligands and supports in determining the catalytic activity, selectivity, and stability of APMCs are illustrated. Finally, some personal perspectives on the further development of surface coordination and interface chemistry for model heterogeneous metal catalysts are presented.

Identifying Descriptors for Promoted Rhodium-Based Catalysts for Higher Alcohol Synthesis via Machine Learning

Rhodium-based catalysts offer remarkable selectivities toward higher alcohols, specifically ethanol, via syngas conversion. However, the addition of metal promoters is required to increase reactivity, augmenting the complexity of the system. Herein, we present an interpretable machine learning (ML) approach to predict and rationalize the performance of Rh-Mn-P/SiO2 catalysts (P = 19 promoters) using the open-source dataset on Rh-catalyzed higher alcohol synthesis (HAS) from Pacific Northwest National Laboratory (PNNL). A random forest model trained on this dataset comprising 19 alkali, transition, post-transition metals, and metalloid promoters, using catalytic descriptors and reaction conditions, predicts the higher alcohols space-time yield (STYHA) with an accuracy of R 2 = 0.76. The promoter's cohesive energy and alloy formation energy with Rh are revealed as significant descriptors during posterior feature-importance analysis. Their interplay is captured as a dimensionless property, coined promoter affinity index (PAI), which exhibits volcano correlations for space-time yield. Based on this descriptor, we develop guidelines for the rational selection of promoters in designing improved Rh-Mn-P/SiO2 catalysts. This study highlights ML as a tool for computational screening and performance prediction of unseen catalysts and simultaneously draws insights into the property-performance relations of complex catalytic systems.

Synthesis of Heterogeneous Catalysts in Catalyst Informatics to Bridge Experiment and High-Throughput Calculation

The coupling of high-throughput calculations with catalyst informatics is proposed as an alternative way to design heterogeneous catalysts. High-throughput first-principles calculations for the oxidative coupling of methane (OCM) reaction are designed and performed where 1972 catalyst surface planes for the CH₄ to CH₃ reaction are calculated. Several catalysts for the OCM reaction are designed based on key elements that are unveiled via data visualization and network analysis. Among the designed catalysts, several active catalysts such as CoAg/TiO₂, Mg/BaO, and Ti/BaO are found to result in high C₂ yield. Results illustrate that designing catalysts using high-throughput calculations is achievable in principle if appropriate trends and patterns within the data generated via high-throughput calculations are identified. Thus, high-throughput calculations in combination with catalyst informatics offer a potential alternative method for catalyst design.

Metal Ions (Li, Mg, Zn, Ce) Doped into La2O3 Nanorod for Boosting Catalytic Oxidative Coupling of Methane

A series of La2O3 nanorod catalysts with doping of active metal ions (Li, Mg, Zn and Ce) were synthesized successfully by the hydrothermal method. The La2O3 nanorods show a uniform size with the length of 50–200 nm and the width of 5–20 nm, and the {110} crystal facet is a preferentially exposed surface. The active metal ions (Li, Mg, Zn and Ce) doped into the lattice of La2O3 nanorods enhance the selectivity of the desired products during oxidative coupling of methane (OCM) and decrease the reaction temperature. Among these catalysts, the Mg-La2O3 catalyst exhibits the best catalytic performance during the OCM reaction, i.e., its selectivity and yield of C2 products at 780 °C is 73% and 21%, respectively. The effect of doped metal ions on catalytic activity for OCM was systematically investigated. Insight into the fabrication strategy and promoting factors of the OCM reaction indicates the potential to further design a high-efficient catalyst in the future.

Thermochemical Conversion of Plastic Waste into Fuels, Chemicals, and Value-Added Materials: A Critical Review and Outlooks

Plastic waste is an emerging environmental issue for our society. Critical action to tackle this problem is to upcycle plastic waste as valuable feedstock. Thermochemical conversion of plastic waste has received growing attention. Although thermochemical conversion is promising for handling mixed plastic waste, it typically occurs at high temperatures (300-800 °C). Catalysts can play a critical role in improving the energy efficiency of thermochemical conversion, promoting targeted reactions, and improving product selectivity. This Review aims to summarize the state-of-the-art of catalytic thermochemical conversions of various types of plastic waste. First, general trends and recent development of catalytic thermochemical conversions including pyrolysis, gasification, hydrothermal processes, and chemolysis of plastic waste into fuels, chemicals, and value-added materials were reviewed. Second, the status quo for the commercial implementation of thermochemical conversion of plastic waste was summarized. Finally, the current challenges and future perspectives of catalytic thermochemical conversion of plastic waste including the design of sustainable and robust catalysts were discussed.

Oxidative Coupling of Methane: Examining the Inactivity of the MnOx -Na2 WO4 /SiO2 Catalyst at Low Temperature

Oxidative coupling of methane (OCM) catalyzed by MnO_x -Na₂ WO₄ /SiO₂ has great industrial promise to convert methane directly to C_2-3 products, but its high light-off temperature is the most challenging obstacle to commercialization and its working mechanism is still a mystery. We report the discovery of a low-temperature active and selective MnO_x -Na₂ WO₄ /SiO₂ catalyst enriched with Q² units in the SiO₂ carrier, being capable of converting 23 % CH₄ with 72 % C_2-3 selectivity at 660 °C. From experiments and theoretical calculations, a large number of Q² units in the MnO_x -Na₂ WO₄ /SiO₂ catalyst is a trigger for markedly lowering the light-off temperature of the Mn³⁺ ↔Mn²⁺ redox cycle involved in the OCM reaction because of the easy formation of MnSiO₃ . Notably, the MnSiO₃ formation proceeds merely through the SiO₂ -involved reaction in the presence of Na₂ WO₄ : Mn₇ SiO₁₂ +6 SiO₂ ↔7 MnSiO₃ +1.5 O₂ . The Na₂ WO₄ not only drives the light-off of this cycle but also gets it working with substantial selectivity toward C_2-3 products. Our findings shine a light on the rational design of more advanced MnO_x -Na₂ WO₄ based OCM catalysts through establishing new Mn³⁺ ↔Mn²⁺ redox cycles with lowered light-off temperature.

Transition-Metal-Doping of CaO as Catalyst for the OCM Reaction, a Reality Check

In this study, first-row transition metal-doped calcium oxide materials (Mn, Ni, Cr, Co., and Zn) were synthesized, characterized, and tested for the OCM reaction. Doped carbonate precursors were prepared by a co-precipitation method. The synthesis parameters were optimized to yield materials with a pure calcite phase, which was verified by XRD. EPR measurements on the doped CaO materials indicate a successful substitution of Ca2+ with transition metal ions in the CaO lattice. The materials were tested for their performance in the OCM reaction, where a beneficial effect towards selectivity and activity effect could be observed for Mn, Ni, and Zn-doped samples, where the selectivity of Co- and Cr-doped CaO was strongly reduced. The optimum doping concentration could be identified in the range of 0.04-0.10 atom%, showing the strongest decrease in the apparent activation energy, as well as the maximum increase in selectivity.

High-Throughput Screening and Literature Data Driven Machine Learning Assisting Investigation of Multi-component La2O3-based Catalysts for Oxidative Coupling of Methane

Multi-component La2O3-based catalysts for oxidative coupling of methane (OCM) were designed based on high-throughput screening (HTS) and literature datasets with multi-output machine learning (ML) approaches including random forest regression (RFR),...

Prospects and challenges for autonomous catalyst discovery viewed from an experimental perspective

Autonomous catalysis research requires elaborate integration of operando experiments into automated workflows. Suitable experimental data for analysis by artificial intelligence can be measured more readily according to standard operating procedures.

Oxidative Coupling of Methane for Ethylene Production: Reviewing Kinetic Modelling Approaches, Thermodynamics and Catalysts

Ethylene production via oxidative coupling of methane (OCM) represents an interesting route for natural gas upscaling, being the focus of intensive research worldwide. Here, OCM developments are analysed in terms of kinetic mechanisms and respective applications in chemical reactor models, discussing current challenges and directions for further developments. Furthermore, some thermodynamic aspects of the OCM reactions are also revised, providing achievable olefins yields in a wide range of operational reaction conditions. Finally, OCM catalysts are reviewed in terms of respective catalytic performances and thermal stability, providing an executive summary for future studies on OCM economic feasibility.

A combined computational and experimental study of methane activation during oxidative coupling of methane (OCM) by surface metal oxide catalysts

The experimentally validated computational models developed herein, for the first time, show that Mn-promotion does not enhance the activity of the surface Na2WO4 catalytic active sites for CH4 heterolytic dissociation during OCM. Contrary to previous understanding, it is demonstrated that Mn-promotion poisons the surface WO4 catalytic active sites resulting in surface WO5 sites with retarded kinetics for C-H scission. On the other hand, dimeric Mn2O5 surface sites, identified and studied via ab initio molecular dynamics and thermodynamics, were found to be more efficient in activating CH4 than the poisoned surface WO5 sites or the original WO4 sites. However, the surface reaction intermediates formed from CH4 activation over the Mn2O5 surface sites are more stable than those formed over the Na2WO4 surface sites. The higher stability of the surface intermediates makes their desorption unfavorable, increasing the likelihood of over-oxidation to CO x , in agreement with the experimental findings in the literature on Mn-promoted catalysts. Consequently, the Mn-promoter does not appear to have an essential positive role in synergistically tuning the structure of the Na2WO4 surface sites towards CH4 activation but can yield MnO x surface sites that activate CH4 faster than Na2WO4 surface sites, but unselectively.

Constructing catalyst knowledge networks from catalyst big data in oxidative coupling of methane for designing catalysts

Designing high performance catalysts for the oxidative coupling of methane (OCM) reaction is often hindered by inconsistent catalyst data, which often leads to difficulties in extracting information such as combinatorial effects of elements upon catalyst performance as well as difficulties in reaching yields beyond a particular threshold. In order to investigate C₂ yields more systematically, high throughput experiments are conducted in an effort to mass-produce catalyst-related data in a way that provides more consistency and structure. Graph theory is applied in order to visualize underlying trends in the transformation of high-throughput data into networks, which are then used to design new catalysts that potentially result in high C₂ yields during the OCM reaction. Transforming high-throughput data in this manner has resulted in a representation of catalyst data that is more intuitive to use and also has resulted in the successful design of a myriad of catalysts that elicit high C₂ yields, several of which resulted in yields greater than those originally reported in the high-throughput data. Thus, transforming high-throughput catalytic data into catalyst design-friendly maps provides a new method of catalyst design that is more efficient and has a higher likelihood of resulting in high performance catalysts.

Catalyst data created through high-throughput experimentation is transformed into catalyst knowledge networks, leading to a new method of catalyst design where successfully designed catalysts result in high C₂ yields during the OCM reaction.

Catalysis Gene Expression Profiling: Sequencing and Designing Catalysts

Identification of catalysts is a difficult matter as catalytic activities involve a vast number of complex features that each catalyst possesses. Here, catalysis gene expression profiling is proposed from unique features discovered in catalyst data collected by high-throughput experiments as an alternative way of representing the catalysts. Combining constructed catalyst gene sequences with hierarchical clustering results in catalyst gene expression profiling where natural language processing is used to identify similar catalysts based on edit distance. In addition, catalysts with similar properties are designed by modifying catalyst genes where the designed catalysts are experimentally confirmed to have catalytic activities that are associated with their catalyst gene sequences. Thus, the proposed method of catalyst gene expressions allows for a novel way of describing catalysts that allows for similarities in catalysts and catalytic activity to be easily recognized while enabling the ability to design new catalysts based on manipulating chemical elements of catalysts with similar catalyst gene sequences.

Unveiling gas-phase oxidative coupling of methane via data analysis

Unveiling the details of the mechanisms of a chemical reaction is a difficult task as reaction mechanisms are strongly coupled with reaction conditions. Here, catalysts informatics combined with high-throughput experimental data is implemented to understand the oxidative coupling of methane (OCM) reaction. In particular, pairwise correlation and data visualization are performed to reveal the relation between reaction conditions and selectivity/conversion. In addition, machine learning is used to fill the gap between experimental data points; thus, a more detailed understanding of the OCM reaction against reaction conditions can be achieved. Therefore, catalysts informatics is proposed for understanding the details of the reaction mechanism, thereby aiding reaction design.

Photocatalytic Conversion of Methane: Recent Advancements and Prospects

Abundant and affordable methane is not only a high-quality fossil fuel, it is also a raw material for the synthesis of value-added chemicals. Solar-energy-driven conversion of methane offers a promising approach to directly transform methane to valuable energy sources under mild conditions, but remains a great challenge at present. In this Review, recent advances in the photocatalytic conversion of methane are systematically summarized. Insights into the construction of effective semiconductor-based photocatalysts from the perspective of light-absorption units and active centers are highlighted and discussed in detail. The performance of various photocatalysts in the conversion of methane is presented, with the photooxidation classified according to the oxidant systems. Lastly, challenges and future perspectives in the photocatalytic oxidation of methane are described.