Bond-Energy-Integrated Descriptor for Oxygen Electrocatalysis of Transition Metal Oxides

Structured Catalysts and Catalytic Processes: Transport and Reaction Perspectives

The structure of catalysts determines the performance of catalytic processes. Intrinsically, the electronic and geometric structures influence the interaction between active species and the surface of the catalyst, which subsequently regulates the adsorption, reaction, and desorption behaviors. In recent decades, the development of catalysts with complex structures, including bulk, interfacial, encapsulated, and atomically dispersed structures, can potentially affect the electronic and geometric structures of catalysts and lead to further control of the transport and reaction of molecules. This review describes comprehensive understandings on the influence of electronic and geometric properties and complex catalyst structures on the performance of relevant heterogeneous catalytic processes, especially for the transport and reaction over structured catalysts for the conversions of light alkanes and small molecules. The recent research progress of the electronic and geometric properties over the active sites, specifically for theoretical descriptors developed in the recent decades, is discussed at the atomic level. The designs and properties of catalysts with specific structures are summarized. The transport phenomena and reactions over structured catalysts for the conversions of light alkanes and small molecules are analyzed. At the end of this review, we present our perspectives on the challenges for the further development of structured catalysts and heterogeneous catalytic processes.

Structure Sensitivity of Metal Catalysts Revealed by Interpretable Machine Learning and First-Principles Calculations

The nature of the active sites and their structure sensitivity are the keys to rational design of efficient catalysts but have been debated for almost one century in heterogeneous catalysis. Though the Brønsted-Evans-Polanyi (BEP) relationship along with linear scaling relation has long been used to study the reactivity, explicit geometry, and composition properties are absent in this relationship, a fact that prevents its exploration in structure sensitivity of supported catalysts. In this work, based on interpretable multitask symbolic regression and a comprehensive first-principles data set, we discovered a structure descriptor, the topological under-coordinated number mediated by number of valence electrons and the lattice constant, to successfully address the structure sensitivity of metal catalysts. The database used for training, testing, and transferability investigation includes bond-breaking barriers of 20 distinct chemical bonds over 10 transition metals, two metal crystallographic phases, and 17 different facets. The resulting 2D descriptor composing the structure term and the reaction energy term shows great accuracy to predict the reaction barriers and generalizability over the data set with diverse chemical bonds in symmetry, bond order, and steric hindrance. The theory is physical and concise, providing a constructive strategy not only to understand the structure sensitivity but also to decipher the entangled geometric and electronic effects of metal catalysts. The insights revealed are valuable for the rational design of the site-specific metal catalysts.

Computational chemistry for water-splitting electrocatalysis

Electrocatalytic water splitting driven by renewable electricity has attracted great interest in recent years for producing hydrogen with high-purity. However, the practical applications of this technology are limited by the development of electrocatalysts with high activity, low cost, and long durability. In the search for new electrocatalysts, computational chemistry has made outstanding contributions by providing fundamental laws that govern the electron behavior and enabling predictions of electrocatalyst performance. This review delves into theoretical studies on electrochemical water-splitting processes. Firstly, we introduce the fundamentals of electrochemical water electrolysis and subsequently discuss the current advancements in computational methods and models for electrocatalytic water splitting. Additionally, a comprehensive overview of benchmark descriptors is provided to aid in understanding intrinsic catalytic performance for water-splitting electrocatalysts. Finally, we critically evaluate the remaining challenges within this field.

Identifying a Universal Activity Descriptor and a Unifying Mechanism Concept on Perovskite Oxides for Green Hydrogen Production

Producing indispensable hydrogen and oxygen for social development via water electrolysis shows more prospects than other technologies. Although electrocatalysts have been explored for centuries, a universal activity descriptor for both hydrogen-evolution reaction (HER) and oxygen-evolution reaction (OER) is not yet developed. Moreover, a unifying concept is not yet established to simultaneously understand HER/OER mechanisms. Here, the relationships between HER/OER activities in three common electrolytes and over ten representative material properties on 12 3d-metal-based model oxides are rationally bridged through statistical methodologies. The orbital charge-transfer energy (Δ) can serve as an ideal universal descriptor, where a neither too large nor too small Δ (≈1 eV) with optimal electron-cloud density around Fermi level affords the best activities, fulfilling Sabatier's principle. Systematic experiments and computations unravel that pristine oxide with Δ ≈ 1 eV possesses metal-like high-valence configurations and active lattice-oxygen sites to help adsorb key protons in HER and induce lattice-oxygen participation in the OER, respectively. After reactions, partially generated metals in the HER and high-valence hydroxides in the OER dominate proton adsorption and couple with pristine lattice-oxygen activation, respectively. These can be successfully rationalized by the unifying orbital charge-transfer theory. This work provides the foundation of rational material design and mechanism understanding for many potential applications.

Small-Molecule Adsorption Energy Predictions for High-Throughput Screening of Electrocatalysts

Predicting adsorption energies of small molecules (e.g., OH, OOH, CO) on electrocatalysts involved in electrochemical reactions aids in accelerating the design and screening of electrocatalysts. Avoiding computationally expensive electronic structure calculations increases the speed of such predictions. Geometric and electronic descriptors have been reported to characterize the environment around surface active sites and predict adsorption energies. However, these descriptors cannot be used to predict adsorption energies of small molecules on various substrates, e.g., metal-oxide and nonmetal electrocatalysts. We compare the performance of these descriptors in predicting adsorption energies of small molecules on various electrocatalysts with adsorption energies calculated from density functional theory. We show that two recently developed machine learning algorithms, Crystal Graph Convolutional Neural Network (CGCNN) and Atomistic Line Graph Neural Network (ALIGNN), outperform the reported descriptors based on geometric (coordination number of the active site and its nearest neighbors) and electronic (the bond-energy-integrated orbitalwise coordination number, the electronegativity, and the number of valence electrons of the active site) properties in predicting the adsorption energies. Our results suggest that ALIGNN is almost always more accurate than CGCNN in adsorption energy predictions. The improvement ranges from 0.02 to 1.0 eV in the mean absolute errors (MAEs). We also compare the performance of CGCNN and ALIGNN algorithms in predicting the overpotentials of the oxygen evolution reaction occurring on various electrocatalysts with MAEs of 0.06 and 0.05 V, respectively.

Interpretable Machine Learning of Chemical Bonding at Solid Surfaces

Understanding the nature of chemical bonding and its variation in strength across physically tunable factors is important for the development of novel catalytic materials. One way to speed up this process is to employ machine learning (ML) algorithms with online data repositories curated from high-throughput experiments or quantum-chemical simulations. Despite the reasonable predictive performance of ML models for predicting reactivity properties of solid surfaces, the ever-growing complexity of modern algorithms, e.g., deep learning, makes them black boxes with little to no explanation. In this Perspective, we discuss recent advances of interpretable ML for opening up these black boxes from the standpoints of feature engineering, algorithm development, and post hoc analysis. We underline the pivotal role of interpretability as the foundation of next-generation ML algorithms and emerging AI platforms for driving discoveries across scientific disciplines.

Main Descriptors To Correlate Structures with the Performances of Electrocatalysts

Traditional trial and error approaches to search for hydrogen/oxygen redox catalysts with high activity and stability are typically tedious and inefficient. There is an urgent need to identify the most important parameters that determine the catalytic performance and so enable the development of design strategies for catalysts. In the past decades, several descriptors have been developed to unravel structure-performance relationships. This Minireview summarizes reactivity descriptors in electrocatalysis including adsorption energy descriptors involving reaction intermediates, electronic descriptors represented by a d-band center, structural descriptors, and universal descriptors, and discusses their merits/limitations. Understanding the trends in electrocatalytic performance and predicting promising catalytic materials using reactivity descriptors should enable the rational construction of catalysts. Artificial intelligence and machine learning have also been adopted to discover new and advanced descriptors. Finally, linear scaling relationships are analyzed and several strategies proposed to circumvent the established scaling relationships and overcome the constraints imposed on the catalytic performance.

Revealing the Correlation of OER with Magnetism: A New Descriptor of Curie/Neel Temperature for Magnetic Electrocatalysts

Developing accurate descriptors for oxygen evolution reaction (OER) is of great significance yet challenging, which roots in and also boosts the understanding of its intrinsic mechanisms. Despite various descriptors are reported, it still has limitations in the facile prediction, given that complicated analytical techniques as well as time-consuming modeling and calculations are indispensable. In the present work, strong correlation of magnetic property with OER performance is revealed by in-depth investigations on the crystal and electronic structures. A facile descriptor of Curie/Neel temperature (T_C/N ) is developed for La_{2- x} Sr_x Co₂ O_{6- δ} perovskite oxides, based on the inference that both magnetism and OER are rooted in the electron exchange interaction. Specifically, both the T_C/N and OER activity are proportional to the degree of p-d orbital hybridization, which increases with enlarged bond angle of Co─O─Co and/or increased oxidation of Co. This finding reveals that T_C/N from magnetic characterizations is an effective descriptor in designing novel OER electrocatalysts, and interdisciplinary researches are advantageous for revealing the controversial mechanisms of OER process.

Oxygen Reduction Activities of Strained Platinum Core-Shell Electrocatalysts Predicted by Machine Learning

Core-shell nanocatalyst activities are chiefly controlled by bimetallic material composition, shell thickness, and nanoparticle size. We present a machine learning framework predicting strain with site-specific precision to rationalize how strain on Pt core-shell nanocatalysts can enhance oxygen reduction activities. Large compressive strain on Pt@Cu and Pt@Ni induces optimal mass activities at 1.9 nm nanoparticle size. It is predicted that bimetallic Pt@Au and Pt@Ag have the best mass activities at 2.8 nm, where active sites are exposed to weak compressive strain. We demonstrate that optimal strain depends on the nanoparticle size; for instance, strengthening compressive strain on 1.92 nm sized Pt@Cu and Pt@Ni, or weakening compressive strain on 2.83 nm sized Pt@Ag and Pt@Au, can lead to further enhanced mass activities.

Progress and Challenges Toward the Rational Design of Oxygen Electrocatalysts Based on a Descriptor Approach

Oxygen redox catalysis, including the oxygen reduction reaction (ORR) and oxygen evolution reaction (OER), is crucial in determining the electrochemical performance of energy conversion and storage devices such as fuel cells, metal-air batteries,and electrolyzers. The rational design of electrochemical catalysts replaces the traditional trial-and-error methods and thus promotes the R&D process. Identifying descriptors that link structure and activity as well as selectivity of catalysts is the key for rational design. In the past few decades, two types of descriptors including bulk- and surface-based have been developed to probe the structure-property relationships. Correlating the current descriptors to one another will promote the understanding of the underlying physics and chemistry, triggering further development of more universal descriptors for the future design of electrocatalysts. Herein, the current benchmark activity descriptors for oxygen electrocatalysis as well as their applications are reviewed. Particular attention is paid to circumventing the scaling relationship of oxygen-containing intermediates. For hybrid materials, multiple descriptors will show stronger predictive power by considering more factors such as interface reconstruction, confinement effect, multisite adsorption, etc. Machine learning and high-throughput simulations can thus be crucial in assisting the discovery of new multiple descriptors and reaction mechanisms.

Metal-Halogen Bonding Seen through the Eyes of Vibrational Spectroscopy

Incorporation of a metal center into halogen-bonded materials can efficiently fine-tune the strength of the halogen bonds and introduce new electronic functionalities. The metal atom can adopt two possible roles: serving as halogen acceptor or polarizing the halogen donor and acceptor groups. We investigated both scenarios for 23 metal–halogen dimers trans-M(Y₂)(NC₅H₄X-3)₂ with M = Pd(II), Pt(II); Y = F, Cl, Br; X = Cl, Br, I; and NC₅H₄X-3 = 3-halopyridine. As a new tool for the quantitative assessment of metal–halogen bonding, we introduced our local vibrational mode analysis, complemented by energy and electron density analyses and electrostatic potential studies at the density functional theory (DFT) and coupled-cluster single, double, and perturbative triple excitations (CCSD(T)) levels of theory. We could for the first time quantify the various attractive contacts and their contribution to the dimer stability and clarify the special role of halogen bonding in these systems. The largest contribution to the stability of the dimers is either due to halogen bonding or nonspecific interactions. Hydrogen bonding plays only a secondary role. The metal can only act as halogen acceptor when the monomer adopts a (quasi-)planar geometry. The best strategy to accomplish this is to substitute the halo-pyridine ring with a halo-diazole ring, which considerably strengthens halogen bonding. Our findings based on the local mode analysis provide a solid platform for fine-tuning of existing and for design of new metal–halogen-bonded materials.

Chemisorption Can Reverse Defect-Defect Interaction on Heterogeneous Catalyst Surfaces

Atomic-level understanding of roles of defect-defect interaction in the bonding of adsorbates on surfaces is critical for tailoring catalysts atom-by-atom and designing new catalysts. Here, from first-principles calculations, we propose a microscopic mechanism for the role of sulfur vacancy-vacancy interaction in hydrogen bonding on surfaces of MoS₂, a nonprecious two-dimensional catalyst for hydrogen evolution reaction. We find that before hydrogen adsorption the interaction of a sulfur vacancy with others is repulsive, originating from the antibonding-like coupling of occupied in-gap vacancy states. When the sulfur vacancy is adsorbed by a hydrogen atom, its interaction with other unadsorbed sulfur vacancies becomes attractive, which can be attributed to the decoupling of repulsive vacancy-vacancy interactions and the occupying of bonding-like coupling states between the in-gap vacancy states that are unoccupied before hydrogen adsorption. This repulsive-to-attractive reverse of vacancy-vacancy interaction reduces the hydrogen adsorption energy and explains why the hydrogen adsorption energy decreases with increasing sulfur vacancy concentration. The emerging picture enables a more general discussion of local defect effects on the adsorption of various adsorbates at different surfaces, providing guidance to improve catalytic performance through defect engineering.

Porous Copper Microspheres for Selective Production of Multicarbon Fuels via CO2 Electroreduction

The electroreduction of carbon dioxide (CO₂ ) toward high-value fuels can reduce the carbon footprint and store intermittent renewable energy. The iodide-ion-assisted synthesis of porous copper (P-Cu) microspheres with a moderate coordination number of 7.7, which is beneficial for the selective electroreduction of CO₂ into multicarbon (C₂₊ ) chemicals is reported. P-Cu delivers a C₂₊ Faradaic efficiency of 78 ± 1% at a potential of -1.1 V versus a reversible hydrogen electrode, which is 32% higher than that of the compact Cu counterpart and approaches the record (79%) reported in the same cell configuration. In addition, P-Cu shows good stability without performance loss throughout a continuous operation of 10 h.

Screening highly active perovskites for hydrogen-evolving reaction via unifying ionic electronegativity descriptor

Facile and reliable screening of cost-effective, high-performance and scalable electrocatalysts is key for energy conversion technologies such as water splitting. ABO_3-δ perovskites, with rich constitutions and structures, have never been designed via activity descriptors for critical hydrogen evolution reaction (HER). Here, we apply coordination rationales to introduce A-site ionic electronegativity (AIE) as an efficient unifying descriptor to predict the HER activities of 13 cobalt-based perovskites. Compared with A-site structural or thermodynamic parameter, AIE endows the HER activity with the best volcano trend. (Gd_0.5La_0.5)BaCo₂O_5.5+δ predicted from an AIE value of ~2.33 exceeds the state-of-the-art Pt/C catalyst in electrode activity and stability. X-ray absorption and computational studies reveal that the peak HER activities at a moderate AIE value of ~2.33 can be associated with the optimal electronic states of active B-sites via inductive effect in perovskite structure (~200 nm depth), including Co valence, Co-O bond covalency, band gap and O 2p-band position.

Facile and reliable screening of efficient electrocatalysts is important for energy conversion technologies such as water splitting. Here, authors introduce A-site ionic electronegativity as a descriptor to understand the hydrogen-evolution activities of cobalt-based perovskites.