Exploring Optimal Water Splitting Bifunctional Alloy Catalyst by Pareto Active Learning

Leveraging data mining, active learning, and domain adaptation for efficient discovery of advanced oxygen evolution electrocatalysts

Developing advanced catalysts for acidic oxygen evolution reaction (OER) is crucial for sustainable hydrogen production. This study presents a multistage machine learning (ML) approach to streamline the discovery and optimization of complex multimetallic catalysts. Our method integrates data mining, active learning, and domain adaptation throughout the materials discovery process. Unlike traditional trial-and-error methods, this approach systematically narrows the exploration space using domain knowledge with minimized reliance on subjective intuition. Then, the active learning module efficiently refines element composition and synthesis conditions through iterative experimental feedback. The process culminated in the discovery of a promising Ru-Mn-Ca-Pr oxide catalyst. Our workflow also enhances theoretical simulations with domain adaptation strategy, providing deeper mechanistic insights aligned with experimental findings. By leveraging diverse data sources and multiple ML strategies, we demonstrate an efficient pathway for electrocatalyst discovery and optimization. This comprehensive, data-driven approach represents a paradigm shift and potentially benchmark in electrocatalysts research.

Confined Flash Pt1/WCx inside Carbon Nanotubes for Efficient and Durable Electrocatalysis

Exploiting cost-effective hydrogen evolution reaction (HER) catalysts is crucial for sustainable hydrogen production. However, currently reported nanocatalysts usually cannot simultaneously sustain high catalytic activity and long-term durability. Here, we report the efficient synthesis and activity tailoring of a chainmail catalyst, isolated platinum atom anchored tungsten carbide nanocrystals encapsulated inside carbon nanotubes (Pt1/WCx@CNTs), by confined flash Joule heating technique. The instantaneous carbothermal reduction reaction enables the millisecond formation of Pt1/WCx nanostructures from CNT-encapsulated polyoxometalates, where nanotubes serve as both heating conductors and robust chainmails. The Pt1/WCx@CNTs exhibit prominent catalytic performance toward acid HER with a low overpotential of 45.2 mV at 10 mA cm-2 and long-term durability over 500 h of continuous running. Mechanism studies reveal the strong metal-support interaction on Pt1/WCx optimizes the charge redistribution at the Pt1-W2C interface and the hydrogen adsorption/desorption behavior. This study offers a potential avenue for ultrafast and activity-controllable synthesis of highly stable single-atom catalysts.

Present and Future of Emerging Catalysts in Gas Sensors for Breath Analysis

To rationalize the noninvasive disease diagnosis by breath analysis, developing a high-performance gas sensor with exceptional sensitivity and selectivity is important to detect trace biomarkers in complex exhaled breath under harsh conditions. Among the various technological innovations, catalyst design and synthesis techniques are the foremost challenges, because gas sensing properties are predominantly determined by surface chemical reactions governed by catalytic activities. Conventional nanoparticle-based catalysts, with their simple structural features, have technical limitations in achieving the requirement for accurate breath analysis. Innovative strategies have been pursued to synthesize unconventional catalyst types with enhanced catalytic capabilities. This Perspective provides a comprehensive overview of recent advancements in catalyst technology for chemiresistive-type gas sensors used in breath analysis. It discusses various emerging catalysts, such as doping catalysts, single-atom catalysts (SACs), bimetallic alloy catalysts, high-entropy alloy (HEA) catalysts, exsolution catalysts, and catalytic filter membranes, along with their unique chemical activation mechanisms that enhance gas sensing properties for detecting target biomarkers in exhaled breath. The review also explores novel strategies for catalyst design, including computational prediction, advanced synthesis techniques, and the integration of sensor arrays with artificial intelligence (AI) to improve diagnostic reliability. By highlighting the crucial role of these emerging catalysts, this review provides valuable insights into the catalytic, synthetic, and analytical aspects that are essential for advancing breath analysis technology.

N-Doped Carbon-Incorporated Cobalt-Iron Mixed-Metal Phosphide Nanoboxes as Efficient Bifunctional Catalysts for Overall Water Splitting

Optimizing the composition and structure of nanocatalysts is an efficient approach to achieving the top electrocatalytic performance. However, the construction of hollow nanocomposites composed of metal phosphides and highly conductive carbon to promote the electrocatalytic performance of metal phosphide-based catalysts is rarely reported. Herein, a CoFeP/C nanobox nanocomposite consisting of Co-Fe mixed-metal phosphides and N-doped carbon was successfully fabricated through an ion-exchange phosphidation strategy derived from ZIF-67 nanocubes. Benefiting from the synergistic effects between multiple components and the unique hollow structure, CoFeP/C nanoboxes can catalyze the alkaline oxygen evolution reaction (OER) and hydrogen evolution reaction (HER) with high activity and stability. Furthermore, in the construction of an alkaline water electrolyzer using CoFeP/C nanoboxes as both OER and HER catalysts, they were capable of efficiently splitting water with a current density of 10 mA cm-2 achieved by applying only 1.62 V of cell voltage and exhibited outstanding durability. Density functional theory calculations demonstrate that synergistic effects among multiple components in CoFeP/C nanoboxes can lower the hydrogen adsorption free energy of the HER and OER energy barrier of the rate-determining step, thus promoting the catalytic reactions. The design and synthesis of CoFeP/C nanoboxes highlight the importance of the composition and structural characteristics in achieving high-performance water electrolysis.

Unlocking the potential: machine learning applications in electrocatalyst design for electrochemical hydrogen energy transformation

Machine learning (ML) is rapidly emerging as a pivotal tool in the hydrogen energy industry for the creation and optimization of electrocatalysts, which enhance key electrochemical reactions like the hydrogen evolution reaction (HER), the oxygen evolution reaction (OER), the hydrogen oxidation reaction (HOR), and the oxygen reduction reaction (ORR). This comprehensive review demonstrates how cutting-edge ML techniques are being leveraged in electrocatalyst design to overcome the time-consuming limitations of traditional approaches. ML methods, using experimental data from high-throughput experiments and computational data from simulations such as density functional theory (DFT), readily identify complex correlations between electrocatalyst performance and key material descriptors. Leveraging its unparalleled speed and accuracy, ML has facilitated the discovery of novel candidates and the improvement of known products through its pattern recognition capabilities. This review aims to provide a tailored breakdown of ML applications in a format that is readily accessible to materials scientists. Hence, we comprehensively organize ML-driven research by commonly studied material types for different electrochemical reactions to illustrate how ML adeptly navigates the complex landscape of descriptors for these scenarios. We further highlight ML's critical role in the future discovery and development of electrocatalysts for hydrogen energy transformation. Potential challenges and gaps to fill within this focused domain are also discussed. As a practical guide, we hope this work will bridge the gap between communities and encourage novel paradigms in electrocatalysis research, aiming for more effective and sustainable energy solutions.

Machine learning-aided understanding of the structure-activity relationship: a case study of MoS2 supported metal-nonmetal pairs for the hydrogen evolution reaction

Understanding the structure-performance relationship is crucial for designing highly active electrocatalysts, yet this remains a challenge. Using MoS2 supported metal-nonmetal atom pairs (XTM@MoS2, TM = Sc-Ni, and X = B, C, N, O, P, Se, Te, and S) for the hydrogen evolution reaction (HER) as an example, we successfully uncovered the structure-activity relationship with the help of density functional theory (DFT) calculations and integrated machine learning (ML) methods. An ML model based on random forest regression was used to predict the activity, and the trained model exhibited excellent performance with minimal error. SHapley Additive exPlanations analysis revealed that the atom mass and covalent radius of the X atom (m_X and R_X) dominate the activity, and their higher values usually lead to better activity. In addition, four promising candidates, i.e., PCr@MoS2, SV@MoS2, SeTi@MoS2, and SeSc@MoS2, with excellent activity are selected. This work provides several promising catalysts for the HER but, more importantly, offers a workflow to explore the structure-activity relationship.

Automation and machine learning augmented by large language models in a catalysis study

Recent advancements in artificial intelligence and automation are transforming catalyst discovery and design from traditional trial-and-error manual mode into intelligent, high-throughput digital methodologies. This transformation is driven by four key components, including high-throughput information extraction, automated robotic experimentation, real-time feedback for iterative optimization, and interpretable machine learning for generating new knowledge. These innovations have given rise to the development of self-driving labs and significantly accelerated materials research. Over the past two years, the emergence of large language models (LLMs) has added a new dimension to this field, providing unprecedented flexibility in information integration, decision-making, and interacting with human researchers. This review explores how LLMs are reshaping catalyst design, heralding a revolutionary change in the fields.

Self-assembly prediction of architecture-controlled bottlebrush copolymers in solution using graph convolutional networks

The investigation of bottlebrush copolymer self-assembly in solution involves a comprehensive approach integrating simulation and experimental research, due to their unique physical characteristics. However, the intricate architecture of bottlebrush copolymers and the diverse solvent conditions introduce a wide range of parameter spaces. In this study, we investigated the solution self-assembly behavior of bottlebrush copolymers by combining dissipative particle dynamics (DPD) simulation results and machine learning (ML) including graph convolutional networks (GCNs). The architecture of bottlebrush copolymers is encoded by graphs including connectivity, side chain length, bead types, and interaction parameters of DPD simulation. Using GCN, we accurately predicted the single chain properties of bottlebrush copolymers with over 95% accuracy. Furthermore, phase behavior was precisely predicted using these single chain properties. Shapley additive explanations (SHAP) values of single chain properties to the various self-assembly morphologies were calculated to investigate the correlation between single chain properties and morphologies. In addition, we analyzed single chain properties and phase behavior as a function of DPD interaction parameters, extracting relevant physical properties for vesicle morphology formation. This work paves the way for tailored design in solution of self-assembled nanostructures of bottlebrush copolymers, offering a GCN framework for precise prediction of self-assembly morphologies under various chain architectures and solvent conditions.

Electron transfer at the heterojunction interface of CoP/MoS2 for efficient electrocatalytic hydrogen evolution reaction

Modulating the electronic states of electrocatalysts is critical for achieving efficient hydrogen evolution reaction (HER). However, how to develop electrocatalysts with superior electronic states is an urgent challenge that must be addressed. Herein, we prepared the CoP/MoS2 heterojunction with a microsphere morphology consisting of thin nanosheets using a facile two-step method. The catalyst's ultrathin nanosheet structure not only provides an extensive surface area for exposing active sites, but it also enables ion transport and bubble release. Electron transfer occurs between CoP and MoS2, optimizing the heterojunction's charge distribution and enhancing the intermediates' adsorption capabilities. As a result, the CoP/MoS2 heterojunction exhibits outstanding electrocatalytic hydrogen evolution activity with an overpotential of only 88 mV at a current density of 10 mA cm-2, which exceeds both the sulfide heterojunction Co9S8/MoS2 and the phosphide heterojunction CoP/CoMoP2. The experimental results and DFT calculation results show that the former has stronger synergistic effects and higher HER activity. This work sheds light on the exploration of efficient heterojunction electrocatalysts with excellent electronic structures.

In Situ Construction of Built-In Opposite Electric Field Balanced Surface Adsorption for Hydrogen Evolution Reaction

Achieving a balance between H-atom adsorption and binding with H2 desorption is crucial for catalyzing hydrogen evolution reaction (HER). In this study, the feasibility of designing and implementing built-in opposite electric fields (OEF) is demonstrated to enable optimal H atom adsorption and H2 desorption using the Ni3(BO3)2/Ni5P4 heterostructure as an example. Through density functional theory calculations of planar averaged potentials, it shows that opposite combinations of inward and outward electric fields can be achieved at the interface of Ni3(BO3)2/Ni5P4, leading to the optimization of the H adsorption free energy (ΔGH*) near electric neutrality (0.05 eV). Based on this OEF concept, the study experimentally validated the Ni3(BO3)2/Ni5P4 system electrochemically forming Ni3(BO3)2 through cyclic voltammetry scanning of B-doped Ni5P4. The surface of Ni3(BO3)2 undergoes reconstruction, as characterized by Grazing Incidence Wide-Angle X-ray Scattering (GIWAXS) and in situ Raman spectroscopy. The resulting catalyst exhibits excellent HER activity in alkaline media, with a low overpotential of 33 mV at 10 mA cm-2 and stability maintained for over 360 h. Therefore, the design strategy of build-in opposite electric field enables the development of high-performance HER catalysts and presents a promising approach for electrocatalyst advancement.

Expanding the Horizons of Machine Learning in Nanomaterials to Chiral Nanostructures

Machine learning holds significant research potential in the field of nanotechnology, enabling nanomaterial structure and property predictions, facilitating materials design and discovery, and reducing the need for time-consuming and labor-intensive experiments and simulations. In contrast to their achiral counterparts, the application of machine learning for chiral nanomaterials is still in its infancy, with a limited number of publications to date. This is despite the great potential of machine learning to advance the development of new sustainable chiral materials with high values of optical activity, circularly polarized luminescence, and enantioselectivity, as well as for the analysis of structural chirality by electron microscopy. In this review, an analysis of machine learning methods used for studying achiral nanomaterials is provided, subsequently offering guidance on adapting and extending this work to chiral nanomaterials. An overview of chiral nanomaterials within the framework of synthesis-structure-property-application relationships is presented and insights on how to leverage machine learning for the study of these highly complex relationships are provided. Some key recent publications are reviewed and discussed on the application of machine learning for chiral nanomaterials. Finally, the review captures the key achievements, ongoing challenges, and the prospective outlook for this very important research field.

Discovering High Entropy Alloy Electrocatalysts in Vast Composition Spaces with Multiobjective Optimization

High entropy alloys (HEAs) are a highly promising class of materials for electrocatalysis as their unique active site distributions break the scaling relations that limit the activity of conventional transition metal catalysts. Existing Bayesian optimization (BO)-based virtual screening approaches focus on catalytic activity as the sole objective and correspondingly tend to identify promising materials that are unlikely to be entropically stabilized. Here, we overcome this limitation with a multiobjective BO framework for HEAs that simultaneously targets activity, cost-effectiveness, and entropic stabilization. With diversity-guided batch selection further boosting its data efficiency, the framework readily identifies numerous promising candidates for the oxygen reduction reaction that strike the balance between all three objectives in hitherto unchartered HEA design spaces comprising up to 10 elements.

In Situ Electrodeposition of Ultralow Pt into NiFe-Metal-Organic Framework/Nickel Foam Nanosheet Arrays as a Bifunctional Catalyst for Overall Water Splitting

Exploring highly effective bifunctional electrocatalysts with surface structural advantages and synergistic optimization effects among multimetals is greatly important for overall water splitting. Herein, we successfully synthesized Pt-loaded NiFe-metal-organic framework nanosheet arrays grown on nickel foam (Pt-NiFe-MOF/NF) via a facile hydrothermal-electrodeposition process. Benefiting from large exposed specific surface, optimal electrical conductivity and efficient metal-support interaction endow Pt-NiFe-MOF/NF with highly catalytic performance, exhibiting small overpotential of 261 mV toward oxygen evolution reaction and 125 mV toward hydrogen evolution reaction at a current density of 100 mA cm-2 in alkaline medium. More significantly, the assembled water electrolyzer comprising the Pt-NiFe-MOF/NF//Pt-NiFe-MOF/NF couple demands a low cell voltage of 1.45 V to reach 10 mA cm-2. This work renders a viable approach to design dual-functional electrocatalysts with exceptional electrocatalytic activity and stability at high current density, showing the great prospect of water electrolysis for commercial application.

Research progress on direct borohydride fuel cells

The rapid development of industry has accelerated the utilization and consumption of fossil energy, resulting in an increasing shortage of energy resources and environmental pollution. Therefore, it is crucial to explore new energy storage devices using renewable and environment-friendly energy as fuel. Direct borohydride fuel cells (DBFCs) are expected to be a feasible and efficient energy storage device by virtue of the read availability of raw materials, non-toxicity of products, and excellent operational stability. Moreover, while utilizing H2O2 as an oxidant, a significant theoretical energy density of 17 kW h kg-1 can be achieved, indicating the broad application prospect of DBFCs in long-range operation and oxygen-free environment. This review summarizes the research progress on DBFCs in term of reaction kinetics, electrode materials, membrane materials, architecture, and electrolytes. In addition, we predict the future research challenges and feasible research directions, considering both performance and cost. We hope this review will help guide future studies on DBFCs.

Laser Synthesis of PtMo Single-Atom Alloy Electrode for Ultralow Voltage Hydrogen Generation

Maximizing atom-utilization efficiency and high current stability are crucial for the platinum (Pt)-based electrocatalysts for hydrogen evolution reaction (HER). Herein, the Pt single-atom anchored molybdenum (Mo) foil (Pt-SA/Mo-L) as a single-atom alloy electrode is synthesized by the laser ablation strategy. The local thermal effect with fast rising-cooling rate of laser can achieve the single-atom distribution of the precious metals (e.g., Pt, Rh, Ir, and Ru) onto the Mo foil. The synthesized self-standing Pt-SA/Mo-L electrode exhibits splendid catalytic activity (31 mV at 10 mA cm-2 ) and high-current-density stability (≈850 mA cm-2 for 50 h) for HER in acidic media. The strong coordination of Pt-Mo bonding in Pt-SA/Mo-L is critical for the efficient and stable HER. In addition, the ultralow electrolytic voltage of 0.598 V to afford the current density of 50 mA cm-2 is realized by utilization of the anodic molybdenum oxidation instead of the oxygen evolution reaction (OER). Here a universal synthetic strategy of single-atom alloys (PtMo, RhMo, IrMo, and RuMo) as self-standing electrodes is provided for ultralow voltage and membrane-free hydrogen production.

Designing Efficient Single-Atom Alloy Catalysts for Selective C═O Hydrogenation: A First-Principles, Active Learning and Microkinetic Study

Selective hydrogenation of α,β-unsaturated aldehydes into unsaturated alcohols is a process in high demand in organic synthesis, pharmaceuticals, and food production. This process requires the precise hydrogenation of C═O bonds, a challenge that requires a tailored catalyst. Single-atom alloys (SAAs), where individual atoms of one metal are distributed in a host metal matrix, offer a potential solution to this challenge. Nevertheless, identifying the appropriate SAA capable of targeted adsorption and the efficient activation of C═O bonds remains a substantial hurdle. In this work, we synergistically combine density functional theory (DFT) calculations, active learning, and microkinetic simulations to design SAAs for the efficient and selective hydrogenation of α,β-unsaturated aldehydes. We first comprehensively assessed the potential of 66 SAAs across 264 surfaces (including (100), (110), (111), and (320) crystal planes), to gauge their potential in activating C═C and C═O bonds. Our assessment unveiled the excellent selectivity of the Ti1Au SAA in activating C═O bonds. Moreover, our detailed DFT calculations further demonstrated the high catalytic activity of Ti1Au(320) and Ti1Au(111) surfaces with a low activation energy barrier of only 0.60 eV. Subsequently, we conducted microkinetic simulations on the selective hydrogenation process of crotonaldehyde, by selecting Ti1Au (320) and (111) surfaces as the catalysts and demonstrated that they exhibited a remarkable selectivity and nearly 100% conversion toward crotyl alcohol in the temperature range from 373 to 553 K. The present study not only reveals novel SAAs for targeted hydrogenation of α,β-unsaturated aldehydes but also establishes a promising path toward efficient design of selective hydrogenation catalysts more broadly.

D-Band Center Optimization of Edge-Rich Ultrathin RuZn Nanosheets With Moiré Superlattices for pH-Universal Hydrogen Evolution Reaction

Electrocatalytic hydrogen evolution reaction (HER) is a promising way to produce pure and clean hydrogen. However, the preparation of efficient and economical catalysts for pH-universal HER remains a challenging but rewarding task. Herein, ultrathin RuZn nanosheets (NSs) with moiré superlattices and abundant edges are synthesized. The RuZn NSs with unique structure exhibit superb HER performance with overpotentials of 11, 13, and 29 mV to achieve 10 mA cm-2 in 1 M KOH, 1 M PBS, and 0.5 M H2 SO4 , respectively, which is substantially lower than those of Ru NSs and RuZn NSs without moiré superlattices. Density functional theory investigations reveal that the charge transfer from Zn to Ru will lead the appropriate downshift of the d-band center of surface Ru atoms, thus accelerating hydrogen desorption from the Ru sites, lowering the dissociation energy barrier of water and greatly improving the HER performance. This work provides an effective design scheme for high-performance HER electrocatalysts over a wide pH range, and propose a general route to prepare Ru-based bimetallic nanosheets with moiré superlattices.