Machine Learning-Driven High-Throughput Screening of Alloy-Based Catalysts for Selective CO2 Hydrogenation to Methanol

Unifying thermochemistry concepts in computational heterogeneous catalysis

Thermophysical properties of adsorbates and gas-phase species define the free energy landscape of heterogeneously catalyzed processes and are pivotal for an atomistic understanding of the catalyst performance. These thermophysical properties, such as the free energy or the enthalpy, are typically derived from density functional theory (DFT) calculations. Enthalpies are species-interdependent properties that are only meaningful when referenced to other species. The widespread use of DFT has led to a proliferation of new energetic data in the literature and databases. However, there is a lack of consistency in how DFT data is referenced and how the associated enthalpies or free energies are stored and reported, leading to challenges in reproducing or utilizing the results of prior work. Additionally, DFT suffers from exchange-correlation errors that often require corrections to align the data with other global thermochemical networks, which are not always clearly documented or explained. In this review, we introduce a set of consistent terminology and definitions, review existing approaches, and unify the techniques using the framework of linear algebra. This set of terminology and tools facilitates the correction and alignment of energies between different data formats and sources, promoting the sharing and reuse of ab initio data. Standardization of thermochemistry concepts in computational heterogeneous catalysis reduces computational cost and enhances fundamental understanding of catalytic processes, which will accelerate the computational design of optimally performing catalysts.

Machine-Learning-Assisted Screening of Nanocluster Electrocatalysts: Mapping and Reshaping the Activity Volcano for the Oxygen Reduction Reaction

In computational heterogeneous catalysis, Sabatier's principle-based activity volcano plots provide an intuitive guide to catalyst design but impose a fundamental constraint on the maximum achievable catalytic performance. Recently, subnano clusters have emerged as an exciting platform, offering high noble metal utilization and superior performance for various reactions compared to extended surfaces, reflecting a complex structure-activity relationship in the non-scalable regime. However, understanding their non-monotonic catalytic activity, attributed to the large configurational space and their fluxional identity, poses a formidable challenge. Here, we present a machine learning (ML) framework that captures the non-monotonic trends in oxygen reduction reaction (ORR) activity at the subnanometer scale, attributed to their dynamic fluxional characteristics. We demonstrate a size-dependent shifting and reshaping of the ORR activity volcano, with Au replacing Pt at the peak. Leveraging only upon the non-ab initio geometric and electronic properties, our trained ML model accurately captures the site-specific adsorption energies of intermediates at the subnanometer regime. To account for the inconsistent trend in activity, we analyzed the correlation between electronic and geometric properties. Our findings reveal that the d-filling and coupling matrix of the neighboring metal atom significantly influences the intermediate adsorption on the local chemical environment compared to the d-band center. Following this analysis, we utilized ML to map the catalyst distribution in the activity volcano and identified the five best sub-nano electrocatalysts, demonstrating overpotential values lower than or comparable to the Pt(111) surface for the ORR. This study provides intuitive guidelines for the rational designing of highly efficient electrocatalysts for fuel cell applications while modifying the activity volcano plots for electrocatalysts at the subnanometer regime.

Design Principles of Catalytic Materials for CO2 Hydrogenation to Methanol

Heterogeneous catalysts are essential for thermocatalytic CO2 hydrogenation to methanol, a key route for sustainable production of this vital platform chemical and energy carrier. The primary catalyst families studied include copper-based, indium oxide-based, and mixed zinc-zirconium oxides-based materials. Despite significant progress in their design, research is often compartmentalized, lacking a holistic overview needed to surpass current performance limits. This perspective introduces generalized design principles for catalytic materials in CO2-to-methanol conversion, illustrating how complex architectures with improved functionality can be assembled from simple components (e.g., active phases, supports, and promoters). After reviewing basic concepts in CO2-based methanol synthesis, engineering principles are explored, building in complexity from single to binary and ternary systems. As active nanostructures are complex and strongly depend on their reaction environment, recent progress in operando characterization techniques and machine learning approaches is examined. Finally, common design rules centered around symbiotic interfaces integrating acid-base and redox functions and their role in performance optimization are identified, pinpointing important future directions in catalyst design for CO2 hydrogenation to methanol.

A machine learning-assisted study of the formation of oxygen vacancies in anatase titanium dioxide

Defect engineering of semiconductor photocatalysts is critical in reducing the reaction barriers. The generation of surface oxygen vacancies allows substantial tuning of the electronic structure of anatase titanium dioxide (TiO2), but disclosing the vacancy formation at the atomic level remains complex or time-consuming. Herein, we combine density functional theory calculations with machine learning to identify the main factors affecting the formation of oxygen defects and accelerate the prediction of vacancy formation. The results show that the first two-layer oxygen atoms on the typical surfaces of TiO2, including (100), (110), and (211) facets, are more likely to be activated when the gas is more reduced, the pressure is higher, and the reduction temperature is increased. Through machine learning, we can conveniently predict the formation of oxygen defects with high accuracy. Furthermore, we present an equation with acceptable accuracy for quantitatively describing the formation of oxygen vacancies in different chemical environments. Our work provides a fast and efficient strategy for characterizing the surface structure with atomic defects.

Active Sites for CO2 Hydrogenation to Methanol: Mechanistic Insights and Reaction Control

Catalytic CO2 conversion to methanol is a promising way to extenuate the adverse effects of CO2 emission, global warming and energy shortage. Understanding the fundamental features of CO2 activation and hydrogenation at the molecular level is essential for carbon utilization and sustainable chemical production in the current climate crisis. This review explores the recent advances in understanding the design of catalysts with desired active sites, including single-atom, dual-atom, interface, defects/vacancies and promoters/dopants. We focused on the design of various catalytic systems to enhance their catalytic performances by stabilizing active metal in a catalyst, identifying the unique structure of active species, and engineering coordination environments of active sites. Mechanistic insights provided by advanced operando and in situ spectroscopies were also discussed. Moreover, the review highlights the key factors affecting active sites and reaction mechanisms, such as local environments, oxidation states, and metal-support interactions. By integrating recent advancements and relating knowledge gaps, this review aims to endow an inclusive overview of the field and guide future research toward more efficient and selective catalysts for CO2 hydrogenation to methanol.

A systematic theoretical study of CO2 hydrogenation towards methanol on Cu-based bimetallic catalysts: role of the CHO&CH3OH descriptor in thermodynamic analysis

The application of density functional theory (DFT) has enriched our understanding of methanol synthesis through CO2 hydrogenation on Cu-based catalysts. However, variations in catalytic performance under different metal doping conditions have hindered the development of universal catalytic principles. To address these challenges, we systematically investigated the scaling relationships of adsorption energy among different reaction intermediates on pure Cu, Au-Cu, Ni-Cu, Pt-Cu, Pd-Cu and Zn-Cu models. Additionally, by summing the respective adsorption energies of two separate species, we have developed a dual intermediate descriptor of CHO&CH3OH, capable of achieving computational accuracy on par with DFT results using the multiple linear regression method, all the while enabling the rapid prediction of thermodynamic properties at various stages of methanol synthesis. This method facilitates a better understanding of the coupling mechanisms between energy and linear expressions on copper-based substrates, and the universal linear criterion can be applied to other catalytic systems, with the aim of pursuing potential catalysts having both high efficiency and low cost.

Analyzing the Active Site and Predicting the Overall Activity of Alloy Catalysts

As one of the potential catalysts, disordered solid solution alloys can offer a wealth of catalytic sites. However, accurately evaluating their activity localization structure and overall activity from each individual site remains a formidable challenge. Herein, an approach based on density functional theory and machine learning was used to obtain a large number of sites of the Pt-Ru alloy as the model multisite catalyst for the hydrogen evolution reaction. Subsequently, a series of statistical approaches were employed to unveil the relationship between the geometric structure and overall activity. Based on the radial frequency distribution of metal elements and the distribution of ΔGH, we have identified the surface and subsurface sites occupied by Pt and Ru, respectively, as the most active sites. Particularly, the concept of equivalent site ratio predicts that the overall activity is highest when the Ru content is 20-30%. Furthermore, a series of Pt-Ru alloys were synthesized to validate the proposed theory. This provides crucial insights into understanding the origin of catalytic activity in alloys and thus will better guide the rational development of targeted multisite catalysts.

High-entropy-based nano-materials for sustainable environmental applications

High entropy materials (HEMs), epitomized by high entropy alloys (HEAs), have sparked immense interest for a range of clean energy and environmental applications due to their remarkable structural versatility and adjustable characteristics. In the face of environmental challenges, HEMs have emerged as valuable tools for addressing issues ranging from wastewater remediation to energy conversion and storage. This review provides a comprehensive exploration of HEMs, spotlighting their catalytic capabilities in diverse redox reactions, such as carbon dioxide reduction to value-added products, degradation of organic pollutants, oxygen reduction, hydrogen evolution, and ammonia decomposition using electrocatalytic and photocatalytic pathways. Additionally, the review highlights HEMs as novel electrode nanomaterials, with the potential to enhance the performance of batteries and supercapacitors. Their unique features, including high capacitance, electrical conductivity, and thermal stability, make them valuable components for meeting crucial energy demands. Furthermore, the review examines challenges and opportunities in advancing HEMs, emphasizing the importance of understanding the underlying mechanisms governing their catalytic and electrochemical behaviors. Essential considerations for optimizing the HEM performance in catalysis and energy storage are outlined to guide future research. Moreover, to provide a comprehensive understanding of the current research landscape, a meticulous bibliometric analysis is presented, offering insights into the trends, focal points, and emerging directions within the realm of HEMs, particularly in addressing environmental concerns.

Theoretical Insights into Amido Group-Mediated Enhancement of CO2 Hydrogenation to Methanol on Cobalt Catalysts

Catalytic reduction of carbon dioxide into high-value-added products, such as methanol, is an effective approach to mitigate the greenhouse effect, and improving Co-based catalysts is anticipated to yield potential catalysts with high performance and low cost. In this study, based on first-principles calculations, we elucidate the promotion effects of surface *NHx (x = 1, 2, and 3) on the carbon dioxide hydrogenation to methanol from both activity and selectivity perspectives on Co-based catalysts. The presence of *NHx reduced the energy barrier of each elementary step on Co(100) by regulating the electronic structure to alter the binding strength of intermediates or by forming a hydrogen bond between surface oxygen-containing species and *NHx to stabilize transition states. The best promotion effect for different steps corresponds to different *NHx. The energy barrier of the rate-determining step of CO2 hydrogenation to methanol is lowered from 1.55 to 0.88 eV, and the product selectivity shifts from methane to methanol with the assistance of *NHx on the Co(100) surface. A similar phenomenon is observed on the Co(111) surface. The promotion effect of *NHx on Co-based catalysts is superior to that of water, indicating that the introduction of *NHx on a Co-based catalyst is an effective strategy to enhance the catalytic performance of CO2 hydrogenation to methanol.

Unravelling CO2 Reduction Reaction Intermediates on High Entropy Alloy Catalysts: An Interpretable Machine Learning Approach to Establish Scaling Relations

Establishment of a scaling relation among the reaction intermediates is highly important but very much challenging on complex surfaces, such as surfaces of high entropy alloys (HEAs). Herein, we designed an interpretable machine learning (ML) approach to establish a scaling relation among CO2 reduction reaction (CO2 RR) intermediates adsorbed at the same adsorption site. Local Interpretable Model-Agnostic Explanations (LIME), Accumulated Local Effects (ALE), and Permutation Feature Importance (PFI) are used for the global and local interpretation of the utilized black box models. These methods were successfully applied through an iterative way and validated on CuCoNiZnMg and CuCoNiZnSnbased HEAs data. Finally, we successfully predicted adsorption energies of *H2 CO (MAE: 0.24 eV) and *H3 CO (MAE: 0.23 eV) by using the *HCO training data. Similarly, adsorption energy of *O (MAE: 0.32 eV) is also predicted from *H training data. We believe that our proposed method can shift the paradigm of state-of-the-art ML in catalysis towards better interpretability.

Comparing the Catalytic Effect of Metals for Energetic Materials: Machine Learning Prediction of Adsorption Energies on Metals

Energetic materials (EMs) and metals are the important components of solid propellants, and a strong catalysis of metals on EMs could further enhance the combustion performance of solid propellants. Accordingly, the study on the adsorption of EMs such as octahydro-1,3,5,7-tetranitro-1,3,5,7-tetrazocine (HMX), hexahydro-1,3,5-trinitro-1,3,5-triazine (RDX), and ammonium dinitramide (ADN) on metals (Ti, Zr, Fe, Ni, Cu, and Al) was carried out by density functional theory (DFT) to reveal the catalytic effect of metals. The deep dissociation of EMs on Ti and Zr represents a stronger interaction and corresponds to the rapid thermal decomposition behavior of the EMs/metal composite in the experiment. It is expected that DFT calculation can be selected instead of experiments to compare the catalytic effect of metals and preliminarily screen out potential high-performance metals. Based on the data set of the calculated adsorption energy, further machine learning (ML) was used to predict the adsorption energy of EMs on metals for a convenient comparison of the catalytic effect of metals, since a quite high adsorption energy value represents a more thorough dissociation. The kernel ridge regression (KRR) method shows the best performance on predicting adsorption energy and helps to choose the metals for efficiently catalyzing ammonium nitrate (AN) and hexanitrohexaazaisowurtzitane (CL-20). Such adsorption computation and ML not only reveal the decomposition mechanism of EMs on metals but also provide a simple underlying method to predict the catalytic effect of metals.

Effects of Segregation on the Catalytic Properties of AgAuCuPdPt High-Entropy Alloy for CO Reduction Reaction

Multicomponent alloys are promising catalysts for diverse chemical conversions, owing to the ability to tune their vast compositional space to maximize catalytic activity and product selectivity. However, elemental segregation, whereby the surface or grain boundaries of the material are enriched in a few elements, is a physically observed phenomenon in such alloys. Such segregation alters not only the composition but also the kinds of catalytically active sites present at the surface. Thus, elemental segregation, which can be achieved via various processing techniques, can be used as an additional knob in searching for alloy compositions that are both active and selective for a target chemical conversion. We demonstrate this using molecular simulations, machine learning, and Bayesian optimization to search for both random solid solution and "segregated" AgAuCuPdPt alloy compositions that are potentially active and selective for CO reduction reaction (CORR). Finally, we validate our findings by computing the reaction-free energy landscape for the CORR on the optimal alloy compositions via density functional theory calculations.

High-entropy alloys in electrocatalysis: from fundamentals to applications

High-entropy alloys (HEAs) comprising five or more elements in near-equiatomic proportions have attracted ever increasing attention for their distinctive properties, such as exceptional strength, corrosion resistance, high hardness, and excellent ductility. The presence of multiple adjacent elements in HEAs provides unique opportunities for novel and adaptable active sites. By carefully selecting the element configuration and composition, these active sites can be optimized for specific purposes. Recently, HEAs have been shown to exhibit remarkable performance in electrocatalytic reactions. Further activity improvement of HEAs is necessary to determine their active sites, investigate the interactions between constituent elements, and understand the reaction mechanisms. Accordingly, a comprehensive review is imperative to capture the advancements in this burgeoning field. In this review, we provide a detailed account of the recent advances in synthetic methods, design principles, and characterization technologies for HEA-based electrocatalysts. Moreover, we discuss the diverse applications of HEAs in electrocatalytic energy conversion reactions, including the hydrogen evolution reaction, hydrogen oxidation reaction, oxygen reduction reaction, oxygen evolution reaction, carbon dioxide reduction reaction, nitrogen reduction reaction, and alcohol oxidation reaction. By comprehensively covering these topics, we aim to elucidate the intricacies of active sites, constituent element interactions, and reaction mechanisms associated with HEAs. Finally, we underscore the imminent challenges and emphasize the significance of both experimental and theoretical perspectives, as well as the potential applications of HEAs in catalysis. We anticipate that this review will encourage further exploration and development of HEAs in electrochemistry-related applications.

CO2 Hydrogenation to Gasoline and Aromatics: Mechanistic and Predictive Insights from DFT, DRIFTS and Machine Learning

The emission of CO2 from fossil fuels is the largest driver of global climate change. To realize the target of a carbon-neutrality by 2050, CO2 capture and utilization is crucial. The efficient conversion of CO2 to C5+ gasoline and aromatics remains elusive mainly due to CO2 thermodynamic stability and the high energy barrier of the C-C coupling step. Herein, advances in mechanistic understanding via Diffuse Reflectance Infrared Fourier Transform Spectroscopy (DRIFTS), density functional theory (DFT), and microkinetic modeling are discussed. It further emphasizes the power of machine learning (ML) to accelerate the search for optimal catalysts. A significant effort has been invested into this field of research with volumes of experimental and characterization data, this study discusses how they can be used as input features for machine learning prediction in a bid to better understand catalytic properties capable of accelerating breakthroughs in the process.

High-Throughput Screening of Promising Redox-Active Molecules with MolGAT

Redox flow batteries (RFBs) have emerged as a promising option for large-scale energy storage, owing to their high energy density, low cost, and environmental benefits. However, the identification of organic compounds with high redox activity, aqueous solubility, stability, and fast redox kinetics is a crucial and challenging step in developing an RFB technology. Density functional theory-based computational materials prediction and screening is a time-consuming and computationally expensive technique, yet it has a high success rate. To speed up the discovery of new materials with desired properties, machine-learning-based models can be trained on large data sets. Graph neural networks (GNNs) are particularly well-suited for non-Euclidean data and can model complex relationships, making them ideal for accelerating the discovery of novel materials. In this study, a GNN-based model called MolGAT was developed to predict the redox potential of organic molecules using molecular structures, atomic properties, and bond attributes. The model was trained on a data set of over 15,000 compounds with redox potentials ranging from -4.11 to 2.56. MolGAT outperformed other GNN variants, such as the Graph Attention Network, Graph Convolution Network, and AttentiveFP models. The trained model was used to screen a vast chemical data set comprising 581,014 molecules, namely OMDB, QM9, ZINC, CHEMBL, and DELANEY, and identified 23,467 potential redox-active compounds for use in redox flow batteries. Of those, 20,716 molecules were identified as potential catholytes with predicted redox potentials up to 2.87 V, while 2,751 molecules were deemed potential anolytes with predicted redox potentials as low as -2.88 V. This work demonstrates the capabilities of graph neural networks in condensed matter physics and materials science to screen promising redox-active species for further electronic structure calculations and experimental testing.

Machine learning-enabled exploration of the electrochemical stability of real-scale metallic nanoparticles

Surface Pourbaix diagrams are critical to understanding the stability of nanomaterials in electrochemical environments. Their construction based on density functional theory is, however, prohibitively expensive for real-scale systems, such as several nanometer-size nanoparticles (NPs). Herein, with the aim of accelerating the accurate prediction of adsorption energies, we developed a bond-type embedded crystal graph convolutional neural network (BE-CGCNN) model in which four bonding types were treated differently. Owing to the enhanced accuracy of the bond-type embedding approach, we demonstrate the construction of reliable Pourbaix diagrams for very large-size NPs involving up to 6525 atoms (approximately 4.8 nm in diameter), which enables the exploration of electrochemical stability over various NP sizes and shapes. BE-CGCNN-based Pourbaix diagrams well reproduce the experimental observations with increasing NP size. This work suggests a method for accelerated Pourbaix diagram construction for real-scale and arbitrarily shaped NPs, which would significantly open up an avenue for electrochemical stability studies.

Machine Learning Aided Interpretable Approach for Single Nucleotide-Based DNA Sequencing using a Model Nanopore

Solid-state nanopore-based electrical detection of DNA nucleotides with the quantum tunneling technique has emerged as a powerful strategy to be the next-generation sequencing technology. However, experimental complexity has been a foremost obstacle in achieving a more accurate high-throughput analysis with industrial scalability. Here, with one of the nucleotide training data sets of a model monolayer gold nanopore, we have predicted the transmission function for all other nucleotides with root-mean-square error scores as low as 0.12 using the optimized eXtreme Gradient Boosting Regression (XGBR) model. Further, the SHapley Additive exPlanations (SHAP) analysis helped in exploring the interpretability of the XGBR model prediction and revealed the complex relationship between the molecular properties of nucleotides and their transmission functions by both global and local interpretable explanations. Hence, experimental integration of our proposed machine-learning-assisted transmission function prediction method can offer a new direction for the realization of cheap, accurate, and ultrafast DNA sequencing.

Recent Advances on Composition-Microstructure-Properties Relationships of Precipitation Hardening Stainless Steel

Precipitation hardening stainless steels have attracted extensive interest due to their distinguished mechanical properties. However, it is necessary to further uncover the internal quantitative relationship from the traditional standpoint based on the statistical perspective. In this review, we summarize the latest research progress on the relationships among the composition, microstructure, and properties of precipitation hardened stainless steels. First, the influence of general chemical composition and its fluctuation on the microstructure and properties of PHSS are elaborated. Then, the microstructure and properties under a typical heat treatment regime are discussed, including the precipitation of B2-NiAl particles, Cu-rich clusters, Ni3Ti precipitates, and other co-existing precipitates in PHSS and the hierarchical microstructural features are presented. Next, the microstructure and properties after the selective laser melting fabricating process which act as an emerging technology compared to conventional manufacturing techniques are also enlightened. Thereafter, the development of multi-scale simulation and machine learning (ML) in material design is illustrated with typical examples and the great concerns in PHSS research are presented, with a focus on the precipitation techniques, effect of composition, and microstructure. Finally, promising directions for future precipitation hardening stainless steel development combined with multi-scale simulation and ML methods are prospected, offering extensive insight into the innovation of novel precipitation hardening stainless steels.

Machine Learning Prediction of the Transmission Function for Protein Sequencing with Graphene Nanoslit

Protein sequencing has rapidly changed the landscape of healthcare and life science by accelerating the growth of diagnostics and personalized medicines for a variety of fatal diseases. Next-generation nanopore/nanoslit sequencing is promising to achieve single-molecule resolution with chromosome-size-long readability. However, due to inherent complexity, high-throughput sequencing of all 20 amino acids demands different approaches. Aiming to accelerate the detection of amino acids, a general machine learning (ML) method has been developed for quick and accurate prediction of the transmission function for amino acid sequencing. Among the utilized ML models, the XGBoost regression model is found to be the most effective algorithm for fast prediction of the transmission function with a very low test root-mean-square error (RMSE ∼0.05). In addition, using the random forest ML classification technique, we are able to classify the neutral amino acids with a prediction accuracy of 100%. Therefore, our approach is an initiative for the prediction of the transmission function through ML and can provide a platform for the quick identification of amino acids with high accuracy.

High-throughput materials screening algorithm based on first-principles density functional theory and artificial neural network for high-entropy alloys

This work introduced the high-throughput phase prediction of PtPd-based high-entropy alloys via the algorithm based on a combined Korringa-Kohn-Rostoker coherent potential approximation (KKR-CPA) and artificial neural network (ANN) technique. As the first step, the KKR-CPA was employed to generate 2,720 data of formation energy and lattice parameters in the framework of the first-principles density functional theory. Following the data generation, 15 features were selected and verified for all HEA systems in each phase (FCC and BCC) via ANN. The algorithm exhibited high accuracy for all four prediction models on 36,556 data from 9139 HEA systems with 137,085 features, verified by R² closed to unity and the mean relative error (MRE) within 5%. From this dataset comprising 5002 and 4137 systems of FCC and BCC phases, it can be realized based on the highest tendency of HEA phase formation that (1) Sc, Co, Cu, Zn, Y, Ru, Cd, Os, Ir, Hg, Al, Si, P, As, and Tl favor FCC phase, (2) Hf, Ga, In, Sn, Pb, and Bi favor BCC phase, and (3) Ti, V, Cr, Mn, Fe, Ni, Zr, Nb, Mo, Tc, Rh, Ag, Ta, W, Re, Au, Ge, and Sb can be found in both FCC and BCC phases with comparable tendency, where all predictions are in good agreement with the data from the literature. Thus, the combination of KKR-CPA and ANN can reduce the computational cost for the screening of PtPd-based HEA and accurately predict the structure, i.e., FCC, BCC, etc.

Screening of bimetallic electrocatalysts for water purification with machine learning

Electrocatalysis provides a potential solution to [Formula: see text] pollution in wastewater by converting it to innocuous N2 gas. However, materials with excellent catalytic activity are typically limited to expensive precious metals, hindering their commercial viability. In response to this challenge, we have conducted the most extensive computational search to date for electrocatalysts that can facilitate [Formula: see text] reduction reaction, starting with 59 390 candidate bimetallic alloys from the Materials Project and Automatic-Flow databases. Using a joint machine learning- and computation-based screening strategy, we evaluated our candidates based on corrosion resistance, catalytic activity, N2 selectivity, cost, and the ability to synthesize. We found that only 20 materials will satisfy all criteria in our screening strategy, all of which contain varying amounts of Cu. Our proposed list of candidates is consistent with previous materials investigated in the literature, with the exception of Cu–Co and Cu–Ag based compounds that merit further investigation.

Rational Designing of Bimetallic/Trimetallic Hydrogen Evolution Reaction Catalysts Using Supervised Machine Learning

Cost-efficient electrocatalysts to replace precious platinum group metals- (PGMs-) based catalysts for the hydrogen evolution reaction (HER) carry significant potential for sustainable energy solutions. Machine learning (ML) methods have provided new avenues for intelligent screening and predicting efficient heterogeneous catalysts in recent years. We coalesce density functional theory (DFT) and supervised ML methods to discover earth-abundant active heterogeneous NiCoCu-based HER catalysts. An intuitive generalized microstructure model was designed to study the adsorbate's surface coverage and generate input features for the ML process. The study utilizes optimized eXtreme Gradient Boost Regression (XGBR) models to screen NiCoCu alloy-based catalysts for HER. We show that the most active HER catalysts can be screened from an extensive set of catalysts with this approach. Therefore, our approach can provide an efficient way to discover novel heterogeneous catalysts for various electrochemical reactions.

Machine Learning Assisted Exploration of High Entropy Alloy-Based Catalysts for Selective CO2 Reduction to Methanol

Catalytic conversion of CO₂ to carbon neutral fuels can be ecofriendly and allow for economic replacement of fossil fuels. Here, we have investigated high-throughput screening of high entropy alloy (Cu, Co, Ni, Zn, and Sn) based catalysts through machine learning (ML) for CO₂ hydrogenation to methanol. Stability and catalytic activity studies of these catalysts have been performed for all possible combinations, where different elemental, compositional, and surface microstructural features were used as input parameters. Adsorption energy values of CO₂ reduction intermediates on the CuCoNiZnMg- and CuCoNiZnSn-based catalysts have been used to train the ML models. Successful prediction of adsorption energies of the adsorbates using CuCoNiZnMg-based training data is achieved except for two intermediates. Hence, we show that activity and selectivity of these catalysts can be successfully predicted for CO₂ hydrogenation to methanol and have screened a series of high entropy-based catalysts (from 36750 considered catalysts) which could be promising for methanol synthesis.

A transferable prediction model of molecular adsorption on metals based on adsorbate and substrate properties

Surface adsorption is one of the fundamental processes in numerous fields, including catalysis, the environment, energy and medicine. The development of an adsorption model which provides an effective prediction of binding energy in minutes has been a long term goal in surface and interface science. The solution has been elusive as identifying the intrinsic determinants of the adsorption energy for various compositions, structures and environments is non-trivial. We introduce a new and flexible model for predicting adsorption energies to metal substrates. The model is based on easily computed, intrinsic properties of the substrate and adsorbate, which are the same for all the considered systems. It is parameterised using machine learning based on first-principles calculations of probe molecules (e.g., H₂O, CO₂, O₂, N₂) adsorbed to a range of pure metal substrates. The model predicts the computed dissociative adsorption energy to metal surfaces with a correlation coefficient of 0.93 and a mean absolute error of 0.77 eV for the large database of molecular adsorption energies provided by Catalysis-Hub.org which have a range of 15 eV. As the model is based on pre-computed quantities it provides near-instantaneous estimates of adsorption energies and it is sufficiently accurate to eliminate around 90% of candidates in screening study of new adsorbates. The model, therefore, significantly enhances current efforts to identify new molecular coatings in many applied research fields.

Catalytic Hydrogenation of CO2 to Methanol: A Review

High-efficiency utilization of CO2 facilitates the reduction of CO2 concentration in the global atmosphere and hence the alleviation of the greenhouse effect. The catalytic hydrogenation of CO2 to produce value-added chemicals exhibits attractive prospects by potentially building energy recycling loops. Particularly, methanol is one of the practically important objective products, and the catalytic hydrogenation of CO2 to synthesize methanol has been extensively studied. In this review, we focus on some basic concepts on CO2 activation, the recent research advances in the catalytic hydrogenation of CO2 to methanol, the development of high-performance catalysts, and microscopic insight into the reaction mechanisms. Finally, some thinking on the present research and possible future trend is presented.