Determining the adsorption energies of small molecules with the intrinsic properties of adsorbates and substrates

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.

Regulating Surface Dipole Moments of TiO2 for the pH-Universal Cathodic Fenton-Like Process

Although electro-Fenton (EF) processes can avoid the safety risks raised by concentrated hydrogen peroxide (H2O2), the Fe(III) reduction has always been either unstable or inefficient at high pH, resulting in catalyst deactivation and low selectivity of H2O2 activation for producing hydroxyl radicals (•OH). Herein, we provided a strategy to regulate the surface dipole moment of TiO2 by Fe anchoring (TiO2-Fe), which, in turn, substantially increased the H2O2 activation for •OH production. The TiO2-Fe catalyst could work at pH 4-10 and maintained considerable degradation efficiency for 10 cycles. Spectroscopic analysis and a theoretical study showed that the less polar Fe-O bond on TiO2-Fe could finely tune the polarity of H2O2 to alter its empty orbital distribution, contributing to better ciprofloxacin degradation activity within a broad pH range. We further verified the critical role of the weakened polarity of H2O2 on its homolysis into •OH by theoretically and experimentally investigating Cu-, Co-, Ni-, Mn-, and Mo-anchored TiO2. This concept offers an avenue for elaborate design of green, robust, and pH-universal cathodic Fenton-like catalysts and beyond.

Optimization of Electrochemical Reduction of Biomass Derived 5-Hydroxymethylfurfural (HMF): A Volcano Plot and Bimetallic Catalysts

2,5-bis(hydroxymethyl)furan (BHMF) derived from 5-Hydroxymethylfurfural (HMF) through a hydrogenation process has extensive applications in the production of resins, polymers, and artificial fibers. However, screening out the candidate and then modulating the active site to optimize the catalyst for high yield of BHMF are currently insufficient. In this study, Gibbs free energy diagrams of the reduction of HMF on 13 metals were presented, along with the identification of the rate-determining step (RDS) with the highest reaction barrier for each metal. We attempted to construct a volcano plot for HMFRR reaction. Additionally, a strategy was proposed to adjust the reaction barriers of RDS by combining two appropriate metals. Further experiments confirmed that Pb with the lowest energy barrier exhibited the highest HMF conversion (BHMF selectivity) among single metals. The modified catalyst by doping Ag on Pb, further boosted the HMF conversion (BHMF selectivity) from 42.1 % (59.4 %) to 80.8 % (80.9 %), respectively. These results provide an approach to rationally design and construct the catalyst system for efficient conversion of HMF.

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.

Boosting CO2 piezo-reduction via metal-support interactions in Au/ZnO based catalysts

Confronting the challenge of climate change necessitates innovative approaches for the reduction of CO2 emissions. Metal-support interaction has been widely demonstrated to enable greatly improved performances in thermal-catalytic, photocatalytic and electrocatalytic CO2 reduction. However, its applicability and specifically its role in the emerging piezo-electrocatalytic CO2 reduction are unknown, severely hampering the utilizations of piezo-electrocatalysis in CO2 conversion. Herein, by adopting Au particles supported on ZnO (Au/ZnO) as a paradigm, it is found that the metal-support interaction can remarkably improve the separation and transfer of piezo-carriers and enhance CO2 adsorption. As a result, Au/ZnO demonstrates a substantially boosted activity for piezo-electrocatalytic CO2 reduction and the optimal sample exhibits a 37.3% increase in CO yield compared to the pristine ZnO. The integration of metal-support interactions opens a new avenue to the design of advanced piezo-electrocatalysts for CO2 reduction.

Nitrogen-Anchored Boridene Enables Mg-CO2 Batteries with High Reversibility

Nanoscale defect engineering plays a crucial role in incorporating extraordinary catalytic properties in two-dimensional materials by varying the surface groups or site interactions. Herein, we synthesized high-loaded nitrogen-doped Boridene (N-Boridene (Mo4/3(BnN1-n)2-mTz), N-doped concentration up to 26.78 at %) nanosheets by chemical exfoliation followed by cyanamide intercalation. Three different nitrogen sites are observed in N-Boridene, wherein the site of boron vacancy substitution mainly accounts for its high chemical activity. Attractively, as a cathode for Mg-CO2 batteries, it delivers a long-term lifetime (305 cycles), high-energy efficiency (93.6%), and ultralow overpotential (∼0.09 V) at a high current of 200 mA g-1, which overwhelms all Mg-CO2 batteries reported so far. Experimental and computational studies suggest that N-Boridene can remarkably change the adsorption energy of the reaction products and lower the energy barrier of the rate-determining step (*MgCO2 → *MgCO3·xH2O), resulting in the rapid reversible formation/decomposition of new MgCO3·5H2O products. The surging Boridene materials with defects provide substantial opportunities to develop other heterogeneous catalysts for efficient capture and converting of CO2.

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.

Interpreting chemisorption strength with AutoML-based feature deletion experiments

The chemisorption energy of reactants on a catalyst surface, [Formula: see text], is among the most informative characteristics of understanding and pinpointing the optimal catalyst. The intrinsic complexity of catalyst surfaces and chemisorption reactions presents significant difficulties in identifying the pivotal physical quantities determining [Formula: see text]. In response to this, the study proposes a methodology, the feature deletion experiment, based on Automatic Machine Learning (AutoML) for knowledge extraction from a high-throughput density functional theory (DFT) database. The study reveals that, for binary alloy surfaces, the local adsorption site geometric information is the primary physical quantity determining [Formula: see text], compared to the electronic and physiochemical properties of the catalyst alloys. By integrating the feature deletion experiment with instance-wise variable selection (INVASE), a neural network-based explainable AI (XAI) tool, we established the best-performing feature set containing 21 intrinsic, non-DFT computed properties, achieving an MAE of 0.23 eV across a periodic table-wide chemical space involving more than 1,600 types of alloys surfaces and 8,400 chemisorption reactions. This study demonstrates the stability, consistency, and potential of AutoML-based feature deletion experiment in developing concise, predictive, and theoretically meaningful models for complex chemical problems with minimal human intervention.

From Charge to Spin: An In-Depth Exploration of Electron Transfer in Energy Electrocatalysis

Catalytic materials play crucial roles in various energy-related processes, ranging from large-scale chemical production to advancements in renewable energy technologies. Despite a century of dedicated research, major enduring challenges associated with enhancing catalyst efficiency and durability, particularly in green energy-related electrochemical reactions, remain. Focusing only on either the crystal structure or electronic structure of a catalyst is deemed insufficient to break the linear scaling relationship (LSR), which is the golden rule for the design of advanced catalysts. The discourse in this review intricately outlines the essence of heterogeneous catalysis reactions by highlighting the vital roles played by electron properties. The physical and electrochemical properties of electron charge and spin that govern catalysis efficiencies are analyzed. Emphasis is placed on the pronounced influence of external fields in perturbing the LSR, underscoring the vital role that electron spin plays in advancing high-performance catalyst design. The review culminates by proffering insights into the potential applications of spin catalysis, concluding with a discussion of extant challenges and inherent limitations.

How spin state and oxidation number of transition metal atoms determine molecular adsorption: a first-principles case study for NH3

Understanding how the electronic state of transition metal atoms can influence molecular adsorption on a substrate is of great importance for many applications. Choosing NH3 as a model molecule, its adsorption behavior on defected SnS2 monolayers is investigated. The number of valence electrons n is controlled by decorating the monolayer with different transition metal atoms, ranging from Sc to Zn. Density-Functional Theory based calculations show that the adsorption energy of NH3 molecules oscillates with n and shows a clear odd-even pattern. There is also a mirror symmetry of the adsorption energies for large and low electron numbers. This unique behavior is mainly governed by the oxidation state of the TM ions. We trace back the observed trends of the adsorption energy to the orbital symmetries and ligand effects which affect the interaction between the 3σ orbitals (NH3) and the 3d orbitals of the transition metals. This result unravels the role which the spin state of TM ions plays in different crystal fields for the adsorption behavior of molecules. This new understanding of the role of the electronic structure on molecular adsorption can be useful for the design of high efficiency nanodevices in areas such as sensing and photocatalysis.

Machine Learning Prediction of Molecular Binding Profiles on Metal-Porphyrin via Spectroscopic Descriptors

The study of molecular adsorption is crucial for understanding various chemical processes. Spectroscopy offers a convenient and non-invasive way of probing structures of adsorbed states and can be used for real-time observation of molecular binding profiles, including both structural and energetic information. However, deciphering atomic structures from spectral information using the first-principles approach is computationally expensive and time-consuming because of the sophistication of recording spectra, chemical structures, and their relationship. Here, we demonstrate the feasibility of a data-driven machine learning approach for predicting binding energy and structural information directly from vibrational spectra of the adsorbate by using CO adsorption on iron porphyrin as an example. Our trained machine learning model is not only interpretable but also readily transferred to similar metal-nitrogen-carbon systems with comparable accuracy. This work shows the potential of using structure-encoded spectroscopic descriptors in machine learning models for the study of adsorbed states of molecules on transition metal complexes.

Critical Review of Pd-Catalyzed Reduction Process for Treatment of Waterborne Pollutants

Catalyzed reduction processes have been recognized as important and supplementary technologies for water treatment, with the specific aims of resource recovery, enhancement of bio/chemical-treatability of persistent organic pollutants, and safe handling of oxygenate ions. Palladium (Pd) has been widely used as a catalyst/electrocatalyst in these reduction processes. However, due to the limited reserves and high cost of Pd, it is essential to gain a better understanding of the Pd-catalyzed decontamination process to design affordable and sustainable Pd catalysts. This review provides a systematic summary of recent advances in understanding Pd-catalyzed reductive decontamination processes and designing Pd-based nanocatalysts for the reductive treatment of water-borne pollutants, with special focus on the interactions and transformation mechanisms of pollutant molecules on Pd catalysts at the atomic scale. The discussion begins by examining the adsorption of pollutants onto Pd sites from a thermodynamic viewpoint. This is followed by an explanation of the molecular-level reaction mechanism, demonstrating how electron-donors participate in the reductive transformation of pollutants. Next, the influence of the Pd reactive site structure on catalytic performance is explored. Additionally, the process of Pd-catalyzed reduction in facilitating the oxidation of pollutants is briefly discussed. The longevity of Pd catalysts, a crucial factor in determining their practicality, is also examined. Finally, we argue for increased attention to mechanism study, as well as precise construction of Pd sites under batch synthesis conditions, and the use of Pd-based catalysts/electrocatalysts in the treatment of concentrated pollutants to facilitate resource recovery.

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.

A weakened Fermi level pinning induced adsorption energy non-charge-transfer mechanism during O2 adsorption in silicene/graphene heterojunctions

Understanding the mechanisms of gas adsorption on a solid surface and making this process tunable are of great significance in fundamental science and industrial applications. Bond creation and charge transfer are often used to explain the origin of adsorption energy (Ead). However, in this study, a new mechanism is observed in O2 adsorption on pure silicene (PS) and silicene/graphene heterojunction (SGH) surfaces, in which the charge distribution remains almost unchanged, but Ead still has a significant change in the order of 0.3 eV. The weakened Fermi level pinning effect is found to be responsible for this interesting behavior and the variation of Ead is approximately equal to the change of work function. Furthermore, this effect is independent of the twist angles in the van der Waals SGH. Our results are consistent with experimental observations in overcoming the degradation of silicene in air.

Resolving Atomic-Scale Structure and Chemical Coordination in High-Entropy Alloy Electrocatalysts for Structure-Function Relationship Elucidation

The recent breakthrough in confining five or more atomic species in nanocatalysts, referred to as high-entropy alloy nanocatalysts (HEAs), has revealed the possibilities of multielemental interactions that can surpass the limitations of binary and ternary electrocatalysts. The wide range of potential surface configurations in HEAs, however, presents a significant challenge in resolving active structural motifs, preventing the establishment of structure-function relationships for rational catalyst design and optimization. We present a methodology for creating sub-5 nm HEAs using an aqueous-based peptide-directed route. Using a combination of pair distribution function and X-ray absorption spectroscopy, HEA structure models are constructed from reverse Monte Carlo modeling of experimental data sets and showcase a clear peptide-induced influence on atomic-structure and chemical miscibility. Coordination analysis of our structure models facilitated the construction of structure-function correlations applied to electrochemical methanol oxidation reactions, revealing the complex interplay between multiple metals that leads to improved catalytic properties. Our results showcase a viable strategy for elucidating structure-function relationships in HEAs, prospectively providing a pathway for future materials design.

Many-Body Methods for Surface Chemistry Come of Age: Achieving Consensus with Experiments

The adsorption energy of a molecule onto the surface of a material underpins a wide array of applications, spanning heterogeneous catalysis, gas storage, and many more. It is the key quantity where experimental measurements and theoretical calculations meet, with agreement being necessary for reliable predictions of chemical reaction rates and mechanisms. The prototypical molecule-surface system is CO adsorbed on MgO, but despite intense scrutiny from theory and experiment, there is still no consensus on its adsorption energy. In particular, the large cost of accurate many-body methods makes reaching converged theoretical estimates difficult, generating a wide range of values. In this work, we address this challenge, leveraging the latest advances in diffusion Monte Carlo (DMC) and coupled cluster with single, double, and perturbative triple excitations [CCSD(T)] to obtain accurate predictions for CO on MgO. These reliable theoretical estimates allow us to evaluate the inconsistencies in published temperature-programed desorption experiments, revealing that they arise from variations in employed pre-exponential factors. Utilizing this insight, we derive new experimental estimates of the (electronic) adsorption energy with a (more) precise pre-exponential factor. As a culmination of all of this effort, we are able to reach a consensus between multiple theoretical calculations and multiple experiments for the first time. In addition, we show that our recently developed cluster-based CCSD(T) approach provides a low-cost route toward achieving accurate adsorption energies. This sets the stage for affordable and reliable theoretical predictions of chemical reactions on surfaces to guide the realization of new catalysts and gas storage materials.

The Sticking of N2 on W(100) Surface: An Improvement in the Description of the Adsorption Dynamics Further Reconciling Theory and Experiment

The adsorption of nitrogen molecules on a (100) tungsten surface has been studied using a new potential energy surface in which long-range interactions are suitably characterized and represented by the Improved Lennard-Jones function. The new potential energy surface is used to carry out molecular dynamics simulations by adopting a semiclassical collisional method that explicitly includes the interaction with the surface phonons. The results of the sticking probability, evaluated as a function of the collision energy, are in good agreement with those obtained in the experiments and improve the already good comparison recently obtained with calculations performed using interactions from the Density Functional Theory method and corrected for long-range van der Waals contributions. The dependence of trapping probability on the surface temperature for a well-defined collision energy has also been investigated.

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.

Lewis Acid Probe for Basicity of Sulfide Electrolytes Investigated by 11B Solid-State NMR

Sulfide-based solid-state lithium-ion batteries (SSLIB) have attracted a lot of interest globally in the past few years for their high safety and high energy density over the traditional lithium-ion batteries. However, sulfide electrolytes (SEs) are moisture-sensitive which pose significant challenges in the material preparation and cell manufacturing. To the best of our knowledge, there is no tool available to probe the types and the strength of the basic sites in sulfide electrolytes, which is crucial for understanding the moisture stability of sulfide electrolytes. Herein, we propose a new spectral probe with the Lewis base indicator BBr3 to probe the strength of Lewis basic sites on various sulfide electrolytes by 11B solid-state NMR spectroscopy (11B-NMR). The active sulfur sites and the corresponding strength of the sulfide electrolytes are successfully evaluated by the proposed Lewis base probe. The probed strength of the active sulfur sites of a sulfide electrolyte is consistent with the results of DFT (density functional theory) calculation and correlated with the H2S generation rate when the electrolyte was exposed in moisture atmosphere. This work paves a new way to investigate the basicity and moisture stability of the sulfide electrolytes.

Modulating the Catalytic Properties of Bimetallic Atomic Catalysts: Role of Dangling Bonds and Charging

Bimetallic atomic catalysts (BACs) exhibit great potential in CO₂ electroreduction. However, modulation and improvement of their catalytic performance are still challenging. To address these issues, an intrinsic descriptor ψ based on the valence properties of active centers was used. The role of the dangling bonds and charging in modulating the catalytic properties of BACs called M₁ M₂ -N₆ -G (M₁ =Ru and Fe) was studied. It was shown that linear relationships between the adsorption energy of the C-species are broken under the effect of the dangling bonds and that they are restored with charging. However, charging has minor effects on the adsorption of the O-species. These findings enable screening promising BACs for CH₃ OH production. This research provides effective schemes for modulating the properties of catalysts, which is beneficial to enriching high-performance catalysts for various reactions.

Absorption wavelength (TD-DFT) and adsorption of metal chalcogen clusters with methyl nicotinate: Structural, electronic, IRI, SERS, pharmacological and antiviral studies (HIV and omicron)

The DFT B3LYP-LAND2DZ technique is used to examine interactions of Methyl nicotinate with copper selenide and zinc selenide clusters. The existence of reactive sites is determined using ESP maps and Fukui data. The energy variations between HOMO and LUMO are utilised to calculate various energy parameters. The Atoms in Molecules and ELF (Electron Localisation Function) maps are employed to investigate the topology of the molecule. The Interaction Region Indicator is used to determine the existence of non-covalent zones in the molecule. The UV-Vis spectrum using the TD-DFT method and DOS graphs are used to obtain the theoretical determination of electronic transition and properties. Structural analysis of the compound is obtained using theoretical IR spectra. To explore the adsorption of copper selenide and zinc selenide clusters on the Methyl nicotinate, the adsorption energy and theoretical SERS spectra are employed. Furthermore, pharmacological investigations are carried out to confirm the drug's non-toxicity. The compound's antiviral efficacy against HIV and Omicron is demonstrated via protein-ligand docking.

The ABC of Generalized Coordination Numbers and Their Use as a Descriptor in Electrocatalysis

The quest for enhanced electrocatalysts can be boosted by descriptor-based analyses. Because adsorption energies are the most common descriptors, electrocatalyst design is largely based on brute-force routines that comb materials databases until an energetic criterion is verified. In this review, it is shown that an alternative is provided by generalized coordination numbers (denoted by CN ¯ $\overline {{\rm{CN}}} $ or GCN), an inexpensive geometric descriptor for strained and unstrained transition metals and some alloys. CN ¯ $\overline {{\rm{CN}}} $ captures trends in adsorption energies on both extended surfaces and nanoparticles and is used to elaborate structure-sensitive electrocatalytic activity plots and selectivity maps. Importantly, CN ¯ $\overline {{\rm{CN}}} $ outlines the geometric configuration of the active sites, thereby enabling an atom-by-atom design, which is not possible using energetic descriptors. Specific examples for various adsorbates (e.g., *OH, *OOH, *CO, and *H), metals (e.g., Pt and Cu), and electrocatalytic reactions (e.g., O₂  reduction, H₂  evolution, CO oxidation, and reduction) are presented, and comparisons are made against other descriptors.

High-Throughput First-Principles Prediction of Interfacial Adhesion Energies in Metal-on-Metal Contacts

Adhesion energy, a measure of the strength by which two surfaces bind together, ultimately dictates the mechanical behavior and failure of interfaces. As natural and artificial solid interfaces are ubiquitous, adhesion energy represents a key quantity in a variety of fields ranging from geology to nanotechnology. Because of intrinsic difficulties in the simulation of systems where two different lattices are matched, and despite their importance, no systematic, accurate first-principles determination of heterostructure adhesion energy is available. We have developed robust, automatic high-throughput workflow able to fill this gap by systematically searching for the optimal interface geometry and accurately determining adhesion energies. We apply it here for the first time to perform the screening of around a hundred metallic heterostructures relevant for technological applications. This allows us to populate a database of accurate values, which can be used as input parameters for macroscopic models. Moreover, it allows us to benchmark commonly used, empirical relations that link adhesion energies to the surface energies of its constituent and to improve their predictivity employing only quantities that are easily measurable or computable.

Regulating the Coordination Environment of Single-Atom Catalysts Anchored on Thiophene Linked Porphyrin for an Efficient Nitrogen Reduction Reaction

The electrochemical nitrogen reduction reaction (NRR) offers a promising strategy to resolve high energy consumption in the nitrogen industry. Recently, the regulation of the electronic structure of single-atom catalysts (SACs) by adjusting their coordination environment has emerged as a rather promising strategy to further enhance their electrocatalytic activity. Herein, we design novel SACs supported by thiophene-linked porphyrin (TM-N₄/TP and TM-N_4-xB_x/TP, where TM = Sc to Au) as potential NRR catalysts using density functional theory calculations. Among these catalysts, TM-N₄/TP (TM = Ti, Nb, Mo, Ta, W, and Re) and TM-N₄/TP with a water bilayer (TM = Nb, Mo, W, and Re) show excellent activity (low limiting potential) but low selectivity. Encouragingly, we find that Mo-N₃B/TP, Mo-N₂B₂-2/TP, W-N₃B/TP, W-N₂B₂-2/TP, Re-N₃B/TP, Re-N₂B₂-2/TP, and Re-N₂B₂-1/TP serve as the most efficient NRR electrocatalysts, as they present stability, superior activity, better selectivity with low limiting potentials (-0.18 ∼ -0.90 V), and high Faradaic efficiencies (>99.80%). Based on microkinetic modeling, kinetic analysis of the NRR is performed and shows that the Re-N₂B₂-1/TP catalyst is more efficient for NH₃ formation. Additionally, multiple-level descriptors provide insight into the origin of NRR activity and enable fast prescreening among numerous candidates. This work provides a new perspective to design highly efficient catalysts for the NRR under ambient conditions.

Electrocatalytic Biomass Upgrading of Furfural using Transition-Metal Borides via Density Functional Theory Investigation

Electrocatalytic biomass upgrading has proven to be an effective technique for generating value-added products. Herein, the design and development of furfural upgrading using transition-metal borides (MBenes) with simultaneous production of hydrogen are presented. Using density functional theory, the stabilities, selectivities, and activities of 13 MBene candidates are systematically evaluated for furfural upgrading. This research suggests that Fe₂ B₂ can serve as a promising electrocatalyst for the formation of furoic acid (FAC), with a limiting potential of -0.15 V, and 5-hydroxy-2(5H)-furanone (HFO), with a limiting potential of -0.93 V. Furthermore, Fe₂ B₂ and Mn₂ Fe₂ are shown to exhibit favorable limiting potentials of -1.35 and -1.36 V, respectively, for producing 6-hydroxy-2.3-dihydro-6H-pyrano-3-one (HDPO), indicating that they may also serve as electrocatalysts. Based on Sabatier's principle, a descriptor (φ) of material properties is developed for screening catalysts with high catalytic activity considering the electronegativities and d-electron number of metals. Additionally, surface redox potential, electronic properties, and charge-density differences are determined for Fe₂ B₂ , which is estimated to exhibit high catalytic activity for the oxidation of furfural to FAC and HFO.

Adsorption Energy in Oxygen Electrocatalysis

Adsorption energy (AE) of reactive intermediate is currently the most important descriptor for electrochemical reactions (e.g., water electrolysis, hydrogen fuel cell, electrochemical nitrogen fixation, electrochemical carbon dioxide reduction, etc.), which can bridge the gap between catalyst's structure and activity. Tracing the history and evolution of AE can help to understand electrocatalysis and design optimal electrocatalysts. Focusing on oxygen electrocatalysis, this review aims to provide a comprehensive introduction on how AE is selected as the activity descriptor, the intrinsic and empirical relationships related to AE, how AE links the structure and electrocatalytic performance, the approaches to obtain AE, the strategies to improve catalytic activity by modulating AE, the extrinsic influences on AE from the environment, and the methods in circumventing linear scaling relations of AE. An outlook is provided at the end with emphasis on possible future investigation related to the obstacles existing between adsorption energy and electrocatalytic performance.

Exploring Structure-Sensitive Relations for Small Species Adsorption Using Machine Learning

Accurate prediction of adsorption energies on heterogeneous catalyst surfaces is crucial to predicting reactivity and screening materials. Adsorption linear scaling relations have been developed extensively but often lack accuracy and apply to one adsorbate and a single binding site type at a time. These facts undermine their ability to predict structure sensitivity and optimal catalyst structure. Using machine learning on nearly 300 density functional theory calculations, we demonstrate that generalized coordination number scaling relations hold well for oxygen- and high-valency carbon-binding species but fail for others. We reveal that the valency and the electronic coupling of a species with the surface, along with the site type and its coordination environment, are critical for small species adsorption. The model simultaneously predicts the adsorption energy and preferred site and significantly outperforms linear scalings in accuracy. It can expose the structure sensitivity of chemical reactions and enable enhanced catalyst activity via engineering particle shape and facet defects. The generality of our methodology is validated by training the model with transition metal data and transferring it to predict adsorption energies on single-atom alloys.

Chiral-Molecule-Based Spintronic Devices

Spintronics and molecular chemistry have achieved remarkable achievements separately. Their combination can apply the superiority of molecular diversity to intervene or manipulate the spin-related properties. It inevitably brings in a new type of functional devices with a molecular interface, which has become an emerging field in information storage and processing. Normally, spin polarization has to be realized by magnetic materials as manipulated by magnetic fields. Recently, chiral-induced spin selectivity (CISS) was discovered surprisingly that non-magnetic chiral molecules can generate spin polarization through their structural chirality. Here, the recent progress of integrating the strengths of molecular chemistry and spintronics is reviewed by introducing the experimental results, theoretical models, and device performances of the CISS effect. Compared to normal ferromagnetic metals, CISS originating from a chiral structure has great advantages of high spin polarization, excellent interface, simple preparation process, and low cost. It has the potential to obtain high efficiency of spin injection into metals and semiconductors, getting rid of magnetic fields and ferromagnetic electrodes. The physical mechanisms, unique advantages, and device performances of CISS are sequentially clarified, revealing important issues to current scientific research and industrial applications. This mini-review points out a key technology of information storage for future spintronic devices without magnetic components.

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.

Wearable anti-temperature interference strain sensor with metal nanoparticle thin film and hybrid ligand exchange

Anti-interference characteristics, whereby undesirable signal interference is minimized, are required for multifunctional sensor platforms. In this study, an anti-temperature-interference resistive-type strain sensor, which does not respond to temperature but only to strain, is designed. Anti-interference properties were achieved by modulating the temperature coefficient of resistance (TCR) of metal nanoparticles (NPs) through hybrid chemical treatment with organic and halide ligands that induce negative and positive TCRs, respectively. Consequently, a very low TCR of 1.9 × 10^-5 K^-1 was obtained. To investigate the origin of this near-zero TCR, analyses of correlated electrical, thermal, and mechanical properties were performed in addition to structural characterization and analysis. Density functional theory calculations and electrical percolation modeling were performed to illuminate the transport behavior in the near-zero-TCR NP thin films. Finally, we fabricated a high-performance anti-temperature-interference strain sensor using a solution process. The sensors detect a variety of strains, including those arising from large movements, such as wrist and knee movements, and fine movements, such as artery pulses or movements made during calligraphy, and did not respond to temperature changes.

Bridging scales between solid mechanics and surface chemistry

A continuum mechanics framework is used herein to model the strains induced in a micromechanical structure by surface phenomena such as adsorption. The resulting picture significantly differs from those of a liquid under surface tension. Considering a solid isotropic elastic material, it is shown that a sphere undergoes a non uniform deformation under surface adsorption. The direction of the surface’s displacement is additionally shown to depend on both the material and the sphere’s radius. It is also shown that modeling surface effects with an elastic membrane surrounding a Cauchy elastic material, the elastic energy is usually misestimated. The reported results also reveal that the overall response of a mechanical structure to surface adsorption strongly depends, at a given scaling, of the higher-grade elastic behavior of the material.

A Universal Descriptor for Complicated Interfacial Effects on Electrochemical Reduction Reactions

Supported catalysts have exhibited excellent performance in various reactions. However, the rational design of supported catalysts with high activity and certain selectivity remains a great challenge because of the complicated interfacial effects. Using recently emerged two-dimensional materials supported dual-atom catalysts (DACs@2D) as a prototype, we propose a simple and universal descriptor based on inherent atomic properties (electronegativity, electron type, and number), which can well evaluate the complicated interfacial effects on the electrochemical reduction reactions (i.e., CO₂, O₂, and N₂ reduction reactions). Based on this descriptor, activity and selectivity trends in CO₂ reduction reaction are successfully elucidated, in good agreement with available experimental data. Moreover, several potential catalysts with superior activity and selectivity for target products are predicted, such as CuCr/g-C₃N₄ for CH₄ and CuSn/N-BN for HCOOH. More importantly, this descriptor can also be extended to evaluate the activity of DACs@2D for O₂ and N₂ reduction reactions, with very small errors between the prediction and reported experimental/computational results. This work provides feasible principles for the rational design of advanced electrocatalysts and the construction of universal descriptors based on inherent atomic properties.

Computational screening of single-atom catalysts supported by VS2 monolayers for electrocatalytic oxygen reduction/evolution reactions

The development of highly efficient bifunctional electrocatalysts to boost oxygen reduction reaction (ORR) and oxygen evolution reaction (OER) is highly desirable for energy conversion and storage devices. Herein, by means of comprehensive first-principles computations, we systematically explored the catalytic activities of a series of single transition metal atoms anchored on two-dimensional VS₂ monolayers (TM@VS₂) for ORR/OER. Our results revealed that Ni@VS₂ exhibits low overpotentials for both ORR (0.45 V) and OER (0.31 V), suggesting its great potential as a bifunctional catalyst, which is mainly induced by its moderate interaction with oxygenated intermediates according to the established scaling relationship and volcano plot. Interestingly, the substituted doping of nitrogen heteroatoms into the VS₂ substrate can further effectively improve the ORR/OER activity of the active metal atom to achieve more eligible ORR/OER bifunctional catalysts. Our results not only propose a new class of potential bifunctional oxygen catalysts but also offer a feasible strategy for further tuning their catalytic activity.

Improving the Accuracy of Atomistic Simulations of the Electrochemical Interface

Atomistic simulation of the electrochemical double layer is an ambitious undertaking, requiring quantum mechanical description of electrons, phase space sampling of liquid electrolytes, and equilibration of electrolytes over nanosecond time scales. All models of electrochemistry make different trade-offs in the approximation of electrons and atomic configurations, from the extremes of classical molecular dynamics of a complete interface with point-charge atoms to correlated electronic structure methods of a single electrode configuration with no dynamics or electrolyte. Here, we review the spectrum of simulation techniques suitable for electrochemistry, focusing on the key approximations and accuracy considerations for each technique. We discuss promising approaches, such as enhanced sampling techniques for atomic configurations and computationally efficient beyond density functional theory (DFT) electronic methods, that will push electrochemical simulations beyond the present frontier.

Applications of Machine Learning in Alloy Catalysts: Rational Selection and Future Development of Descriptors

At present, alloys have broad application prospects in heterogeneous catalysis, due to their various catalytic active sites produced by their vast element combinations and complex geometric structures. However, it is the diverse variables of alloys that lead to the difficulty in understanding the structure-property relationship for conventional experimental and theoretical methods. Fortunately, machine learning methods are helpful to address the issue. Machine learning can not only deal with a large number of data rapidly, but also help establish the physical picture of reactions in multidimensional heterogeneous catalysis. The key challenge in machine learning is the exploration of suitable general descriptors to accurately describe various types of alloy catalysts, which help reasonably design catalysts and efficiently screen candidates. In this review, several kinds of machine learning methods commonly used in the design of alloy catalysts is introduced, and the applications of various reactivity descriptors corresponding to different alloy systems is summarized. Importantly, this work clarifies the existing understanding of physical picture of heterogeneous catalysis, and emphasize the significance of rational selection of universal descriptors. Finally, the development of heterogeneous catalytic descriptors for machine learning are presented.

Autonomous Reaction Network Exploration in Homogeneous and Heterogeneous Catalysis

Autonomous computations that rely on automated reaction network elucidation algorithms may pave the way to make computational catalysis on a par with experimental research in the field. Several advantages of this approach are key to catalysis: (i) automation allows one to consider orders of magnitude more structures in a systematic and open-ended fashion than what would be accessible by manual inspection. Eventually, full resolution in terms of structural varieties and conformations as well as with respect to the type and number of potentially important elementary reaction steps (including decomposition reactions that determine turnover numbers) may be achieved. (ii) Fast electronic structure methods with uncertainty quantification warrant high efficiency and reliability in order to not only deliver results quickly, but also to allow for predictive work. (iii) A high degree of autonomy reduces the amount of manual human work, processing errors, and human bias. Although being inherently unbiased, it is still steerable with respect to specific regions of an emerging network and with respect to the addition of new reactant species. This allows for a high fidelity of the formalization of some catalytic process and for surprising in silico discoveries. In this work, we first review the state of the art in computational catalysis to embed autonomous explorations into the general field from which it draws its ingredients. We then elaborate on the specific conceptual issues that arise in the context of autonomous computational procedures, some of which we discuss at an example catalytic system.

Exploring high-efficiency electrocatalysts of metal-doped two-dimensional C4N for oxygen reduction, oxygen evolution, and hydrogen evolution reactions by first-principles screening

The HER/ORR/OER on 3d, 4d, and 5d transition metal doped C4N are studied using DFT methods.

Improving the Activity of Electrocatalysts toward the Hydrogen Evolution Reaction, the Oxygen Evolution Reaction, and the Oxygen Reduction Reaction via Modification of Metal and Ligand of Conductive Two-Dimensional Metal-Organic Frameworks

Exploring efficient and stable electrocatalysts for the hydrogen evolution reaction (HER) and the oxygen evolution and reduction reactions (OER and ORR) is vital to the development of renewable energy technologies. Herein, on the basis of density functional theory (DFT) calculations, we systematically investigated 30 TMN_xO_4-x-HTP (TM = Fe, Co, Ni, Ru, Rh and Pd; x = 0-4; HTP refers to hexatriphenylene) analogs of conductive two-dimensional (2D) metal-organic frameworks (MOFs) as potential catalysts for HER, OER, and ORR. The results show the good stabilities and metallic features of TMN_xO_4-x-HTP. The interaction strength between intermediates and catalysts governs the catalytic activities, which can be modulated by tuning the TM atom and the local coordination number of N/O in catalysts. RhN₃O₁-HTP is an efficient bifunctional catalyst for HER and OER, and RhN₁O₃-HTP is a promising bifunctional catalyst for OER and ORR. Our findings highlight a potentially efficient class of electrocatalysts based on 2D MOF materials.

Achieving flexible large-scale reactivity tuning by controlling the phase, thickness and support of two-dimensional ZnO

Tuning surface reactivity of catalysts is an effective strategy to enhance catalytic activity towards a chemical reaction. Traditional reactivity tuning usually relies on a change of the catalyst composition, especially when large-scale tuning is desired. Here, based on density functional theory calculations, we provide a strategy for flexible large-scale tuning of surface reactivity, i.e. from a few tenths of electronvolts (eV) to multiple eV, merely through manipulating the phase, thickness, and support of two-dimensional (2D) ZnO films. 2D ZnO films have three typical phases, i.e. graphene, wurtzite, and body-centered-tetragonal structures, whose intrinsic stability strongly depends on the thickness and/or the chemical nature of the support. We show that the adsorption energy of hydrogen differs by up to 3 eV on these three phases. For the same phase, varying the film thickness and/or support can lead to a few tenths of eV to 2 eV tuning of surface reactivity. We further demonstrate that flexible large-scale tuning of surface reactivity has a profound impact on the reaction kinetics, including breaking the Brønsted-Evans-Polanyi relationship.

Screening and Understanding Li Adsorption on Two-Dimensional Metallic Materials by Learning Physics and Physics-Simplified Learning

Understanding and broad screening Li interaction energetics with surfaces are key to the development of materials for a wide range of applications including Li-based electrochemical capacitors, Li sensors, Li separation membranes, and Li-ion batteries. In this work, we build a high-throughput screening scheme to screen Li adsorption energetics on 2D metallic materials. First, density functional theory and graph convolution networks are utilized to calculate the minimum Li adsorption energies for some 2D metallic materials. The data is then used to find a dependence of the minimum Li adsorption energies on the sum of ionization potential, work function of the 2D metal, and coupling energy between Li⁺ and substrate, and the dependence is used to screen all 2D metallic materials. Physics-simplified learning by splitting the property into different contributions and learning or calculating each component is shown to have higher accuracy and transferability for machine learning of complex materials properties.

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.