High-Throughput Screening of a Single-Atom Alloy for Electroreduction of Dinitrogen to Ammonia

Advancing electrocatalytic reactions through mapping key intermediates to active sites via descriptors

Descriptors play a crucial role in electrocatalysis as they can provide valuable insights into the electrochemical performance of energy conversion and storage processes. They allow for the understanding of different catalytic activities and enable the prediction of better catalysts without relying on the time-consuming trial-and-error approaches. Hence, this comprehensive review focuses on highlighting the significant advancements in commonly used descriptors for critical electrocatalytic reactions. First, the fundamental reaction processes and key intermediates involved in several electrocatalytic reactions are summarized. Subsequently, three types of descriptors are classified and introduced based on different reactions and catalysts. These include d-band center descriptors, readily accessible intrinsic property descriptors, and spin-related descriptors, all of which contribute to a profound understanding of catalytic behavior. Furthermore, multi-type descriptors that collectively determine the catalytic performance are also summarized. Finally, we discuss the future of descriptors, envisioning their potential to integrate multiple factors, broaden application scopes, and synergize with artificial intelligence for more efficient catalyst design and discovery.

Supported nano-sized precious metal catalysts for oxidation of catalytic volatile organic compounds

Volatile organic compounds (VOCs) are common contaminants found as indoor as well as outdoor pollutants, which can induce acute or chronic health hazards to the human physiological system. The catalytic oxidation method is widely considered as one of the effective methods for removing VOCs, and the development of highly effective catalysts is highly urgent for booming this interesting field. This review focuses on the recent progress of VOC oxidation catalyzed by supported nano-sized precious metal catalysts, and discusses the effects of metal composition, supports, size, and morphology on the catalytic activity. In addition, the roles played by both nano-sized precious metals and supports in enhancing the performance of catalytic VOCs are also systematically discussed, which will guide the further development of more advanced VOC catalysts.

Mechanistic Studies of Stimulus-Response Integrated Catalysis of Single-Atom Alloys under Electric Fields for Electrochemical Nitrogen Reduction

The present work introduces a novel catalytic strategy to promote the nitrogen reduction reaction (NRR) by employing a cooperative Cu-based single-atom alloy (SAA) and oriented external electric fields (OEEFs) as catalysts. The field strength (F)-dependent reaction pathways are investigated by means of first-principles calculations. Different dipole-induced responses of intermediates to electric fields break the original scaling relationships and effectively tune not only the activity but also the product selectivity of the NRR. When the most active Os1Cu SAA is taken as an example, in the absence of an OEEF, the overpotential (η) of the NRR is 0.62 V, which is even larger than that of the competitive hydrogen evolution reaction (HER). A negative field not only reduces η but switches the preference to the NRR over the HER. In particular, η at F = -1.14 V/Å reaches the bottom of 0.18 V, which is 70% lower than that in the field-free state.

Ten-electron count rule for the binding of adsorbates on single-atom alloy catalysts

Single-atom alloys have recently emerged as highly active and selective alloy catalysts. Unlike pure metals, single-atom alloys escape the well-established conceptual framework developed nearly three decades ago for predicting catalytic performance. Although this offers the opportunity to explore so far unattainable chemistries, this leaves us without a simple guide for the design of single-atom alloys able to catalyse targeted reactions. Here, based on thousands of density functional theory calculations, we reveal a 10-electron count rule for the binding of adsorbates on the dopant atoms, usually the active sites, of single-atom alloy surfaces. A simple molecular orbital approach rationalizes this rule and the nature of the adsorbate-dopant interaction. In addition, our intuitive model can accelerate the rational design of single-atom alloy catalysts. Indeed, we illustrate how the unique insights provided by the electron count rule help identify the most promising dopant for an industrially relevant hydrogenation reaction, thereby reducing the number of potential materials by more than one order of magnitude.

Efficient Machine Learning Model Focusing on Active Sites for the Discovery of Bifunctional Oxygen Electrocatalysts in Binary Alloys

The distinctive characteristics of alloy catalysts, encompassing composition, structure, and modifiable adsorption sites, present significant potential for the development of highly efficient electrocatalysts for oxygen evolution/reduction reactions [oxygen evolution reactions (OERs)/oxygen reduction reactions (ORRs)]. Machine learning (ML) methods can quickly establish the relationship between material features and catalytic activity, thus accelerating the development of alloy electrocatalysts. However, the current abundance of features presents a crucial challenge in selecting the most pertinent ones. In this study, we explored seven intrinsic features directly derived from the material's structure, with a specific focus on the chemical environment of active sites and their nearest neighbors. An accurate and efficient ML model to predict potential bifunctional oxygen electrocatalysts based on the intrinsic features of AB-type alloy active sites and intermediate free energies in the OERs/ORRs was established. These features possess clear physical and chemical meanings, closely linked to the electronic and geometric structures of active sites and neighboring atoms, thereby providing indispensable insights for the discovery of high-performance electrocatalysts. The ML model achieved R2 scores of 0.827, 0.913, and 0.711 for the predicted values of the three intermediate (OH, O, OOH) free energies, with corresponding mean absolute errors of 0.175, 0.242, and 0.200 eV, respectively. These results indicate that the ML model exhibits high accuracy in predicting the intermediate free energies. Furthermore, the ML model exhibited a prediction efficiency 150,000 times faster than traditional density functional theory calculations. This work will offer valuable insights and a framework for facilitating the rapid design of potential catalysts by ML methods.

Bridging Together Theoretical and Experimental Perspectives in Single-Atom Alloys for Electrochemical Ammonia Production

Ammonia is an essential commodity in the food and chemical industry. Despite the energy-intensive nature, the Haber-Bosch process is the only player in ammonia production at large scales. Developing other strategies is highly desirable, as sustainable and decentralized ammonia production is crucial. Electrochemical ammonia production by directly reducing nitrogen and nitrogen-based moieties powered by renewable energy sources holds great potential. However, low ammonia production and selectivity rates hamper its utilization as a large-scale ammonia production process. Creating effective and selective catalysts for the electrochemical generation of ammonia is critical for long-term nitrogen fixation. Single-atom alloys (SAAs) have become a new class of materials with distinctive features that may be able to solve some of the problems with conventional heterogeneous catalysts. The design and optimization of SAAs for electrochemical ammonia generation have recently been significantly advanced. This comprehensive review discusses these advancements from theoretical and experimental research perspectives, offering a fundamental understanding of the development of SAAs for ammonia production.

Direct Electrochemical Synthesis of Acetamide from CO2 and N2 on a Single-Atom Alloy Catalyst

The electrochemical conversion of carbon dioxide into value-added compounds not only paves the way toward a sustainable society but also unlocks the potential for electrocatalytic synthesis of amides through the introduction of N atoms. However, it also poses one of the greatest challenges in catalysis: achieving simultaneous completion of C-C coupling and C-N coupling. Here, we have meticulously investigated the catalytic prowess of Cu-based single-atom alloys in facilitating the electrochemical synthesis of acetamide from CO2 and N2. Through a comprehensive screening process encompassing catalyst stability, adsorption capability, and selectivity against the HER, W/Cu(111) SAA has emerged as an auspicious contender. The reaction entails CO2 reduction to CO, C-C coupling leading to the formation of a ketene intermediate *CCO, N2 reduction, and C-N coupling between NH3 and *CCO culminating in the production of acetamide. The W/Cu(111) surface not only exhibits exceptional activity in the formation of acetamide, with a barrier energy of 0.85 eV for the rate-determining CO hydrogenation step, but also effectively suppresses undesired side reactions leading to various C1 and C2 byproducts during CO2 reduction. This work presents a highly effective approach for forming C-C and C-N bonds via coelectroreduction of CO2 and N2, illuminating the reaction mechanism underlying acetamide synthesis from these two gases on single-atom alloy catalysts. The catalyst design strategy employed in this study has the potential to be extended to a range of amide chemicals, thereby broadening the scope of products that can be obtained through CO2/N2 reduction.

Machine Learning Design of Single-Atom Catalysts for Nitrogen Fixation

First-principles calculations have been combined with machine learning in the design of transition-metal single-atom catalysts. Readily available descriptors are selected to describe the nitrogen activation capability of metals and coordinating atoms. Thus, a series of V/Nb/Ta-Nx single-atom catalysts are screened out as promising structures upon considering the stability, activity, and selectivity investigated computationally. Furthermore, by using the gradient boosting regression algorithm, an accurate prediction of the hydrogenation barriers for the nitrogen reduction reaction (NRR) is achievable, with a root-mean-squared error of 0.07 eV. The integration of high-throughput computation and machine learning constitutes a powerful strategy for the acceleration of catalyst design. This approach facilitates the rapid and accurate prediction of the NRR performance of more than 1000 single-atom catalyst structures. Moreover, the current work provides further insights by elaborately correlating the structure and performance, which may be instructive for both the design and application of vanadium-group catalysts.

Metal-Doped C3B Monolayer as the Promising Electrocatalyst for Hydrogen/Oxygen Evolution Reaction: A Combined Density Functional Theory and Machine Learning Study

The development of high-efficiency electrocatalysts for hydrogen evolution reduction (HER)/oxygen evolution reduction (OER) is highly desirable. In particular, metal borides have attracted much attention because of their excellent performances. In this study, we designed a series of metal borides by doping of a transition metal (TM) in a C3B monolayer and further explored their potential applications for HER/OER via density functional theory (DFT) calculations and machine learning (ML) analysis. Our results revealed that the |ΔG*H| values of Fe-, Ag-, Re-, and Ir-doped C3B are approximately 0.00 eV, indicating their excellent HER performances. On the other hand, among all the considered TM atoms, the Ni- and Pt-doped C3B exhibit excellent OER activities with the overpotentials smaller than 0.44 V. Together with their low overpotentials for HER (<0.16 V), we proposed that Ni/C3B and Pt/C3B could be the potential bifunctional electrocatalysts for water splitting. In addition, the ML method was employed to identify the important factors to affect the performance of the TM/C3B electrocatalyst. Interestingly, the results showed that the OER performance is closely related to the inherent properties of TM atoms, i.e., the number of d electrons, electronegativity, atomic radius, and first ionization energy; all these values could be directly obtained without DFT calculations. Our results not only proposed several promising electrocatalysts for HER/OER but also suggested a guidance to design the potential TM-boron (TM-B)-based electrocatalysts.

Contrasting Capability of Single Atom Palladium for Thermocatalytic versus Electrocatalytic Nitrate Reduction Reaction

The occurrence of high concentrations of nitrate in various water resources is a significant environmental and human health threat, demanding effective removal technologies. Single atom alloys (SAAs) have emerged as a promising bimetallic material architecture in various thermocatalytic and electrocatalytic schemes including nitrate reduction reaction (NRR). This study suggests that there exists a stark contrast between thermocatalytic (T-NRR) and electrocatalytic (E-NRR) pathways that resulted in dramatic differences in SAA performances. Among Pd/Cu nanoalloys with varying Pd-Cu ratios from 1:100 to 100:1, Pd/Cu(1:100) SAA exhibited the greatest activity (TOFPd = 2 min-1) and highest N2 selectivity (94%) for E-NRR, while the same SAA performed poorly for T-NRR as compared to other nanoalloy counterparts. DFT calculations demonstrate that the improved performance and N2 selectivity of Pd/Cu(1:100) in E-NRR compared to T-NRR originate from the higher stability of NO3* in electrocatalysis and a lower N2 formation barrier than NH due to localized pH effects and the ability to extract protons from water. This study establishes the performance and mechanistic differences of SAA and nanoalloys for T-NRR versus E-NRR.

Efficient asymmetrical silicon-metal dimer electrocatalysts for the nitrogen reduction reaction

The electrocatalytic nitrogen reduction reaction (ENRR) has been regarded as an eco-friendly and feasible substitute for the Haber-Bosch method. Identifying the effective catalysts for the ENRR is an extremely important prerequisite but challenging. Herein, asymmetrical silicon-metal dimer catalysts doped into g-C₃N₄ nanosheets with nitrogen vacancies (SiM@C₃N₄) were designed to address nitrogen activation and reduction. The concept catalysts of SiM@C₃N₄ can combine the advantages of silicon-based and metal-based catalysts during the ENRR. Among the catalysts investigated, SiMo@C₃N₄ and SiRu@C₃N₄ exhibited the highest activities towards the ENRR with ultra-low onset potentials of -0.20 and -0.39 V; meanwhile, they suppressed the competing hydrogen evolution reaction (HER) due to the relative difficulty in releasing hydrogen. Additionally, SiRu@C₃N₄ is demonstrated to possess strong hydrophobicity, which is greatly beneficial to the production of ammonia. This research provides insights into asymmetrical silicon-metal dimer catalysts and reveals a new method for developing dual-atom electrocatalysts. This asymmetrical dimer strategy can be applied in other electrocatalytic reactions for energy conversion.

How machine learning can accelerate electrocatalysis discovery and optimization

Advances in machine learning (ML) provide the means to bypass bottlenecks in the discovery of new electrocatalysts using traditional approaches. In this review, we highlight the currently achieved work in ML-accelerated discovery and optimization of electrocatalysts via a tight collaboration between computational models and experiments. First, the applicability of available methods for constructing machine-learned potentials (MLPs), which provide accurate energies and forces for atomistic simulations, are discussed. Meanwhile, the current challenges for MLPs in the context of electrocatalysis are highlighted. Then, we review the recent progress in predicting catalytic activities using surrogate models, including microkinetic simulations and more global proxies thereof. Several typical applications of using ML to rationalize thermodynamic proxies and predict the adsorption and activation energies are also discussed. Next, recent developments of ML-assisted experiments for catalyst characterization, synthesis optimization and reaction condition optimization are illustrated. In particular, the applications in ML-enhanced spectra analysis and the use of ML to interpret experimental kinetic data are highlighted. Additionally, we also show how robotics are applied to high-throughput synthesis, characterization and testing of electrocatalysts to accelerate the materials exploration process and how this equipment can be assembled into self-driven laboratories.

Coupling Transition Metal Compound with Single-Atom Site for Water Splitting Electrocatalysis

Single-atom site catalysts (SACs) provide an ideal platform to identify the active centers, explore the catalytic mechanism, and establish the structure-property relationships, and thus have attracted increasing interests for electrocatalytic energy conversion. Substantial endeavors have been devoted to the construction of carbon-supported SACs, and their progress have been comprehensively reviewed. Compared with carbon-supported SACs, transition metal compounds (TMCs)-supported SACs are still in their infancy in the field of electrocatalysis. However, they have also aroused ever-increasing attention for driving electrocatalytic water splitting, and emerged as an indispensable class of SACs in recent years, predominately owing to their inherently structural features, such as rich anchoring sites, surface defects, and lattice vacancy. Herein, in this review, we have systematically summarized the recent advances of a variety of TMC supported SACs toward electrocatalytic water splitting. The advanced characterization techniques and theoretical analyses for identifying and monitoring the atomic structure of SACs are firstly manifested. Subsequently, the anchoring and stabilization mechanisms for TMC supported SACs are also highlighted. Thereafter, the advances of TMC supported SACs for driving water electrolysis are systematically unraveled.

Superstructures of Zeolitic Imidazolate Frameworks to Single- and Multiatom Sites for Electrochemical Energy Conversion

The exploration of electrocatalysts with high catalytic activity and long-term stability for electrochemical energy conversion is significant yet remains challenging. Zeolitic imidazolate framework (ZIF)-derived superstructures are a source of atomic-site-containing electrocatalysts. These atomic sites anchor the guest encapsulation and self-assembly of aspheric polyhedral particles produced using microreactor fabrication. This review provides an overview of ZIF-derived superstructures by highlighting some of the key structural types, such as open carbon cages, 1D superstructures, hollow structures, and the interconversion of superstructures. The fundamentals and representative structures are outlined to demonstrate the role of superstructures in the construction of materials with atomic sites, such as single- and dual-atom materials. Then, the roles of ZIF-derived single-atom sites for the electroreduction of CO₂ and electrochemical synthesis of H₂ O₂ are discussed, and their electrochemical performance for energy conversion is outlined. Finally, the perspective on advancing single- and dual-atom electrode-based electrochemical processes with enhanced redox activity and a low-impedance charge-transfer pathway for cathodes is provided. The challenges associated with ZIF-derived superstructures for electrochemical energy conversion are discussed.

Breaking the Volcano-Shaped Relationship for Highly Efficient Electrocatalytic Nitrogen Reduction: A Computational Guideline

The volcano-shaped relationship is very common in electrocatalytic nitrogen reduction reaction (e-NRR) and is usually caused by the competition between the first and last hydrogenation steps. How to break such a relationship to further improve the catalytic performance remains a great challenge. Herein, using first-principles calculations, we investigate a range of transition-metal (TM)-doped Cu-based single-atom alloys (TM₁-Cu(111)) as catalysts for e-NRR. When the adsorption of N₂ on the catalysts is strong enough, the inert N₂ molecules can be effectively activated for the first hydrogenation step. Meanwhile, the last hydrogenation step is not affected by the scaling relationship and remains easy on all of the catalysts due to the unstable top-site adsorption of NH₂, resulting in the break of the volcano-shaped relationship in e-NRR. Thus, only the first hydrogenation step is identified as the potential determining step. Four TM₁-Cu(111) catalysts (TM = Re, W, Tc, and Mo) are selected as promising catalysts with limiting potential ranging from -0.38 to -0.56 V, showing outstanding e-NRR activity. Besides, the four catalysts also inhibit the competing hydrogen evolution reaction and long-term stability. Our work provides a guideline for breaking the volcano-shaped relationship in e-NRR and significant in the rational design of highly efficient electrocatalysts.

Stick or Spill? Scaling Relationships for the Binding Energies of Adsorbates on Single-Atom Alloy Catalysts

Single-atom alloy catalysts combine catalytically active metal atoms, present as dopants, with the selectivity of coinage metal hosts. Determining whether adsorbates stick at the dopant or spill over onto the host is key to understanding catalytic mechanisms on these materials. Despite a growing body of work, simple descriptors for the prediction of spillover energies (SOEs), i.e., the relative stability of an adsorbate on the dopant versus the host site, are not yet available. Using Density Functional Theory (DFT) calculations on a large set of adsorbates, we identify the dopant charge and the SOE of carbon as suitable descriptors. Combining them into a linear surrogate model, we can reproduce DFT-computed SOEs within 0.06 eV mean absolute error. More importantly, our work provides an intuitive theoretical framework, based on the concepts of electrostatic interactions and covalency, that explains SOE trends and can guide the rational design of future single-atom alloy catalysts.

PdFe Single-Atom Alloy Metallene for N2 Electroreduction

Single-atom alloys hold great promise for electrocatalytic nitrogen reduction reaction (NRR), while the comprehensive experimental/theoretical investigations of SAAs for the NRR are still missing. Herein, PdFe₁ single-atom alloy metallene, in which the Fe single atoms are confined on a Pd metallene support, is first developed as an effective and robust NRR electrocatalyst, delivering exceptional NRR performance with an NH₃ yield of 111.9 μg h^-1  mg^-1 , a Faradaic efficiency of 37.8 % at -0.2 V (RHE), as well as a long-term stability for 100 h electrolysis. In-depth mechanistic investigations by theoretical computations and operando X-ray absorption/Raman spectroscopy indentify Pd-coordinated Fe single atoms as active centers to enable efficient N₂ activation via N₂ -to-Fe σ-donation, reduced protonation energy barriers, suppressed hydrogen evolution and excellent thermodynamic stability, thus accounting for the high activity, selectivity and stability of PdFe₁ for the NRR.

"Capture-Backdonation-Recapture" Mechanism for Promoting N2 Reduction by Heteronuclear Metal-Free Double-Atom Catalysts

Facing the increasingly serious energy and environmental crisis, the development of heteronuclear metal-free double-atom catalysts is a potential strategy to realize efficient catalytic nitrogen reduction with low energy consumption and no pollution because it could combine the advantages of flexible active sites in double-atom catalysts while also being pollution-free and have high Faraday efficiency in metal-free catalysts simultaneously. However, according to the existing mechanism, the finite orbits of other nonmetallic atoms, except the boron atom, reduce the possibility of metal-free catalysis and hinder the development of heteronuclear metal-free double-atom catalysts. Herein, we propose a new "capture-backdonation-recapture" mechanism, which skillfully uses the electron capture-backdonation-recapture between boron, the substrate, and other nonmetallic elements to solve the above problems. Based on this mechanism, by means of the first-principle calculations, the material structure, adsorption energy, catalytic activity, and selectivity of 36 catalysts are systematically investigated to evaluate their catalytic performance. B-Si@BP1 and B-Si@BP3 are selected for their good catalytic performance and low limiting potentials of -0.14 and -0.10 V, respectively. Meanwhile, the "capture-backdonation-recapture" mechanism is also verified by analyzing the results of adsorption energy and electron transfer. Our work broadens the ideas and lays the theoretical foundation for the development of heteronuclear metal-free double-atom catalysts in the future.

One Decade of Computational Studies on Single-Atom Alloys: Is In Silico Design within Reach?

ConspectusSingle-Atom alloys (SAAs) are an emerging class of materials consisting of a coinage metal (Cu, Ag, and Au) doped, at the single-atom limit, with another metal. As catalysts, coinage metals are rarely very active on their own, but when they are, they exhibit high selectivity. On the other hand, transition metals are usually very active but not as selective. Incorporating transition metals (guest elements) into coinage metals (host material) is therefore appealing for combining the activity and selectivity of each constituent in a balanced way. Additionally, first-principles calculations have shown that single atoms embedded in the surface of a coinage metal can exhibit emergent properties. Here, we describe how computational studies based on density functional theory (DFT) and kinetic Monte Carlo (KMC) simulations, often undertaken in close collaboration with experimental research groups, have shaped, over the past decade, the way we understand SAA catalysis.This Account reviews our contributions in elucidating the stability of SAAs, their electronic structure, and the way adsorbates interact and react on SAA catalytic surfaces. By studying in detail the processes that affect the stability of the SAA phase, we have shown that out of several bimetallic combinations of coinage metals with prominent Pt-group metals only PtCu and PdCu are stable surface alloys under vacuum. However, more surface alloy structures are possible in the presence of adsorbates because the latter can stabilize, via strong binding, dopants in the surface of the material. More interestingly, a large number of these surface alloys are resistant to the aggregation of dopant atoms into clusters, thereby favoring the SAA structure. These major results from DFT calculations serve as a guide for experimentalists to explore new SAA catalysts. Further analysis has shown that SAAs have a unique electronic structure with a very sharp d-band feature close to the Fermi level, analogous to the electronic structure of molecular entities. This is one of the reasons that SAAs are particularly sought after: although they are metallic nanoparticles, they have properties akin to those of homogeneous catalysts. In this context, we have contributed extensive screening studies, focusing on molecular fragments of catalytic relevance on a range of SAAs, which have driven the identification of new catalysts. We have also explored the rich chemistry of two-adsorbate systems via kinetic modeling, demonstrating how a spectator species with greater affinity for the dopant can modulate the reactivity of the catalyst via the so-called (punctured) molecular cork effect.Since the first experimental characterization of SAAs about a decade ago, theoretical models have been able to support and explain various experimental observations. These models have served as benchmarks for assessing the predictive capability of the underlying theoretical methods. In turn, the predictions that have been delivered have guided and continue to guide the experimental research efforts in the field. These advancements show that the in silico design of new SAA catalysts is now within reach.

Electroreduction of N2 to NH3 catalyzed by a Mn/Re(111) single-atom alloy catalyst with high activity and selectivity: a new insight from a first-principles study

Periodic density functional theory calculations show that a Mn/Re(111) single-atom alloy may be an excellent catalyst with high activity and selectivity for the electrocatalytic N2 reduction reaction.

Design strategies of two-dimensional metal-organic frameworks toward efficient electrocatalysts for N2 reduction: cooperativity of transition metals and organic linkers

Two-dimensional (2D) metal-organic frameworks (MOFs) serve as emerging electrocatalysts due to their high conductivity, chemical tunability, and accessibility of active sites. We herein proposed a series of 2D MOFs with different metal atoms and organic linkers with the formula M₃C₁₂X₁₂ (M = Cr, Mo, and W; X = NH, O, S, and Se) to design efficient nitrogen reduction reaction (NRR) electrocatalysts. Our theoretical calculations showed that metal atoms in M₃C₁₂X₁₂ can efficiently capture and activate N₂ molecules. Among these candidates, W₃C₁₂X₁₂ (X = O, S, and Se) show the best NRR performance due to their high activity and selectivity as well as low limiting potential (-0.59 V, -0.14 V, and -0.01 V, respectively). Moreover, we proposed a d-band center descriptor strategy to screen out the high activity and selectivity of M₃C₁₂X₁₂ for the NRR. Therefore, our work not only demonstrates a class of promising electrocatalysts for the NRR but also provides a strategy for further predicting the catalytic activity of 2D MOFs.

Two-dimensional transition metal borides as high activity and selectivity catalysts for ammonia synthesis

In comparison to defect/doping induced activity in materials, transition metal borides with exposed metal atoms, large specific surface area, and high active site density show advantages as durable and efficient catalysts for specific electrochemical reactions. In this work, ReB₂ for N₂ reduction reaction (NRR) for ammonia (NH₃) with a record-low limiting potential of U_L = -0.05 V and high Faraday efficiency (FE) of 100% is screened out from a new class of TMB₂. It is concluded that high pressure/temperature is favorable to N₂ adsorption and kinetic barrier minimization; the maximal turnover frequency (TOF) at 700 K and 100 bar is 1.24 × 10^-2 per s per site, which is comparable to that of the benchmark Fe₃/Al₂O₃ catalysts, achieving an extremely fast reaction rate. In addition, crystal orbital Hamilton population (COHP) of *N₂ reveals the intrinsic origin of N₂ activation by analyzing the d-2π* interactions, and integrated COHP could be a quantitative descriptor to describe the N₂ activation degree. It is evident that our results not only identify an efficient NRR electrocatalyst in particular, paving the way for sustainable NH₃ production, but also explain the chemical and physical origin of the activity, advancing the design principle for catalysts for various reactions in general.

Applying Machine Learning to Rechargeable Batteries: From the Microscale to the Macroscale

Emerging machine learning (ML) methods are widely applied in chemistry and materials science studies and have led to a focus on data-driven research. This Minireview summarizes the application of ML to rechargeable batteries, from the microscale to the macroscale. Specifically, ML offers a strategy to explore new functionals for density functional theory calculations and new potentials for molecular dynamics simulations, which are expected to significantly enhance the challenging descriptions of interfaces and amorphous structures. ML also possesses a great potential to mine and unveil valuable information from both experimental and theoretical datasets. A quantitative "structure-function" correlation can thus be established, which can be used to predict the ionic conductivity of solids as well as the battery lifespan. ML also exhibits great advantages in strategy optimization, such as fast-charge procedures. The future combination of multiscale simulations, experiments, and ML is also discussed and the role of humans in data-driven research is highlighted.