Qiang Wang

Electrochemical CO2 reduction reaction (CO2RR) is a promising route for the conversion of CO2 into valuable carbon products. Nitrogen–carbon (NC)-supported single-atom catalysts (SACs), particularly those with peripheral coordination geometries, have attracted considerable attention. In the present work, we investigate two N-coordinating Co-SAC geometries, pyridinic (CoPriN4) and pyrrolic (CoProN4), constructed at the periphery of idealized graphene fragments for the CO2-to-CO conversion pathway. Our density functional theory (DFT) calculations, performed within the computational hydrogen electrode (CHE) framework, demonstrate that incorporating five- and six-membered NC ring structures in the outer shell modulates the inner-shell electronic properties of the N-doped systems, influencing electron transfer behavior and inducing d–p hybridization between the Co center and the coordinating N atoms. In particular, CoProN4 exhibits stronger d–p hybridization, resulting in weakened *CO desorption relative to CoPriN4. This electronic modulation optimizes the thermodynamic adsorption energies of key intermediates (*COOH and *CO) and thermodynamically disfavors the competing hydrogen evolution reaction, thereby lowering the predicted thermodynamic limiting potential for CO2RR. As the present study is based on static thermodynamic modeling of idealized graphene fragments and does not account for explicit kinetic barriers or solvent dynamics, the proposed peripheral design strategy offers a useful thermodynamic perspective for understanding structure–property relationships in NC-supported Co-SACs for electrochemical CO2 reduction.

Cobalt carbide catalysts have attracted significant attention due to their high activity and light-hydrocarbon selectivity in the direct conversion of syngas, yet navigating their multidimensional parameter space typically relies on a trial-and-error screening process. In this work, an active-learning (AL)-assisted machine-learning (ML) framework was developed to predict the CO conversion and light-hydrocarbon selectivity over cobalt carbide catalysts. A data set containing 140 catalyst systems and 658 experimental records has been mined from experimental literature. To improve model generalization for small-sample data sets, catalyst-composition-stratified sampling was integrated with AL during model development. Several regression algorithms were systematically compared by using grid search and cross-validation. The optimal model achieved test set R2 values of 0.86 and 0.87 for CO conversion and light-hydrocarbon selectivity, respectively. Interpretation using Shapley additive explanations indicated that CO conversion is primarily influenced by reaction conditions, particularly temperature and H/C ratio, whereas light-hydrocarbon selectivity shows stronger dependence on catalyst structural features, including pore size and promoter content. Partial dependence plot analysis further revealed nonlinear interactions among key descriptors, suggesting different controlling factors for the catalytic activity and product distribution. Pareto optimization revealed an intrinsic tradeoff between CO conversion and light-hydrocarbon selectivity, and the predicted Pareto-optimal catalyst configurations showed good agreement with representative reported experimental catalysts. The proposed framework demonstrates the potential of integrating AL, interpretable ML, and Pareto optimization for catalyst evaluation and multiobjective optimization using small-sample catalytic data sets.

Recent studies have demonstrated that precise modulation of the coordination environment around metal centers can significantly influence the catalytic activity, inspiring interest in the rational design of efficient CO2 reduction reaction (CO2RR) catalysts through coordination engineering. In this work, a series of single-atom catalysts (SACs) with asymmetric coordination environments were systematically constructed based on heteroatom-doped graphene frameworks, aiming to elucidate the structure−activity relationships governing CO2RR performance. By integrating density functional theory (DFT) calculations with machine learning (ML) approaches, the limiting potential (UL) was employed as the target descriptor. EXtreme Gradient Boosting Regression (XGBR) and Sure Independence Screening and Sparsifying Operator (SISSO) algorithms were adopted to construct a black-box predictive model. An interpretable descriptor model, achieving coefficients of determination of R2 = 0.96 and R2 = 0.95, respectively, validated the reliability and generalizability of the established models. The results revealed that, compared to conventional M-N4 SACs with symmetric coordination environments, the introduction of asymmetric coordination could further enhance the CO2RR catalytic activity of Co-, Fe-, and Mn-based SACs, with notable performance variations across different metal centers. Four SACs with superior catalytic potential, namely, Co-N3O, Co-N3S, Fe-N4O2, and Mn-N4N3, were identified. Electronic structure analysis indicated that coordination environment modulation drove an upshift of the metal center d band toward the Fermi level, strengthening interactions with key reaction intermediates and optimizing adsorption energies to enhance catalytic activity. The DFT−ML strategy established herein provides a systematic framework for the rational design of SACs toward efficient CO2RR and offers generalizable principles for coordination engineering in electrocatalysis.

Magnesium oxide (MgO) is attractive for CO2 utilization. However, the electronic inertness has limited its application in electrochemical catalysis. Herein, spin-polarized density functional theory (DFT) calculations with the BEEF-vdW functional were performed to evaluate the activity and selectivity of transition-metal-doped MgO (TM-MgO) catalysts for electrochemical CO2 reduction reaction (eCO2RR). Co-, Pd-, and Pt-doped MgO were identified as promising candidates for selective formic acid (HCOOH) production. Mechanistic analysis shows that TM doping induces electron redistribution in MgO, activating neighboring Mg sites while enabling charge transfer between TM dopants and reaction intermediates. The competition between Mg-site activation and TM-intermediate charge transfer determines the site preference and binding strength of the key OCHO* intermediate. Bader charge, density of states, and COHP analyses support this mechanism. This work provides design principles for oxide-based electrocatalysts toward selective HCOOH production from eCO2RR.

The solid-state electrolytes (SSE) are gaining tremendous attention in designing rechargeable batteries with remarkable energy density and safety features for next-generation energy storage device applications. The rational design of SSE with promising ionic conductivity, higher electrochemical stability windows, and stable electrode-electrolyte interfaces remain a formidable challenge, traditionally hindered by trial-and-error experimentation and computationally expensive theoretical simulations. Here, we systematically review the recent breakthroughs in the artificial intelligence (AI)-driven design of SSE, spanning electrochemical stability and ionic conductivity domains, with a particular focus on how machine learning (ML) and deep learning (DL) are fundamentally transforming the discovery and optimization landscape. We critically discuss the synergy between first-principles density functional theory (DFT), molecular dynamics (MD) simulations, and advanced AI algorithms including supervised and unsupervised learning (SL, UL), graph neural networks (GNNs), and Machine Learning Interatomic Potentials (MLIP) that collectively enable accurate prediction of ionic conductivity, elucidation of ion transport mechanisms, and high-throughput screening (HTS) of vast chemical spaces. Emphasis is placed on descriptor engineering that bridges atomic-level structural features (e.g., lattice parameters, activation energies, defect chemistry) with macroscopic electrochemical performance, as well as the emerging paradigm of closed-loop, self-driving laboratories for autonomous materials discovery. Furthermore, AI-guided strategies have demonstrated remarkable interfacial ionic transport mechanism. Despite these transformative advances, persistent challenges including data scarcity, limited descriptor transferability, discrepancies between theoretical predictions and experimental realization, remain significant challenges. Looking forward, the convergence of AI with high-throughput experimentation and multiscale modeling promises to redefine SSE discovery, accelerating the deployment of high-performance all solid-state batteries (ASSBs) for sustainable energy storage.

To precisely control reaction selectivity in hydrogermylation, it is essential to understand how transition metals activate the Ge–H bond. This study investigates Ge–H bond activation in germane using laser-ablated group IV transition metal atoms (Ti, Zr and Hf) by employing a combination of matrix isolation infrared spectroscopy and quantum chemical calculations. The reaction between Ti and germane atoms occurs in a spin-conserved manner, requiring photocatalysis and leading to the formation of HTi(μ-H)₃Ge. This product features three three-center-two-electron hydrogen-bridged bonds, arising from electron donation from the Ge–H σ bonds to the vacant d orbitals of Ti. In contrast, reactions involving Zr and Hf atoms with germane are spontaneous multistate reactions. Spin-forbidden transitions between triplet and singlet state potential energy surfaces reduce the reaction barriers along the pathways, resulting in the formation of H₂M(μ-H)₂Ge (where M represents Zr or Hf). Topological analysis reveals that H₂M(μ-H)₂Ge is characterized by an M–Ge covalent bond. Additionally, three-center-two-electron bonds are formed in H₂M(μ-H)₂Ge via hydrogen-bridged bonds. This work provides valuable theoretical insights into the activation of Ge–H bonds by transition metals.

Pyridine-based graphynes anchoring second-row transition metals (TM-pdGY) are excellent electrocatalysts for the CO ₂ RR. The regulation of the overall electronic structure of TM-pdGYs by the TM–N coordination is the origin of the high CO ₂ RR activity.

The direct conversion of syngas to higher oxygenates presents a fundamental challenge in simultaneously achieving high CO conversion, superior oxygenate selectivity, and minimal undesired C₁ byproducts. Here, we develop a series of multifunctional Cu_xPd₁/SiO₂|CoMn catalysts with granule stacking architecture, which overcome the challenge by precisely controlling the spatial arrangement of active sites and the intermediate transport pathway. Systematic optimization reveals a distinct volcano-shaped relationship on Pd loadings, with the Cu₂₈Pd₁/SiO₂|CoMn composite emerging as the optimal candidate. Such a catalyst achieves an exceptional oxygenates molar selectivity of 44.4% (C₂₊OH/ROH = 95.4%) while maintaining low C₁ products (6.4% CO₂ and 5.7% CH₄) at considerable 27.3% CO conversion. Mechanistic studies reveal that the breakthrough stems from precise control of spatial intimacy of functional components, optimized mass balance between CH_xO* and CH_x*, and isolated Pd atom-mediated hydrogen spillover effects. Based on spectroscopic evidence with theoretical calculations, we propose a synergistic catalytic system wherein PdCu single-atom alloys facilitate H₂ activation and CH_xO* formation through hydrogen spillover, while Co⁰-Co₂C interfaces produce abundant CH_x* species. The synergistic interaction enables the migration of CH_xO* intermediates from single-atom alloy sites to Co⁰-Co₂C interfaces, where they undergo further insertion into CH_x* species, ultimately leading to hydrogenation and formation of higher oxygenates.