Identifying active sites is decisive for optimizing catalysts, but this remains challenging, especially in high-entropy materials with multiple random sites. Here, we developed an attention-enhanced, multiobjective predictive model to precisely identify active sites and their corresponding overpotentials, a key parameter for catalytic activity. Using this model to predict the overpotential of oxygen evolution reaction (OER) process and doping formation energies in high-entropy CoOOH materials, we screened 17,500 catalysts and identified 8 with optimal catalytic activity. Subsequent automated synthesis and validation found a high-performance catalyst, TiFeNiZn-CoOOH, which exhibited an exceptional OER overpotential of 263 millivolts at 100 milliamperes per square centimeter. Feature importance and statistical analysis of more than 5 million structures confirmed that Zn consistently shows the highest active site occupation probability, and the [CoNiZn] coordination yields the lowest overpotential. Electronic structure analysis revealed that Zn activates gap states, critically lowering the OER energy barrier. This work paves a broad avenue for screening high-performance catalysts with identified catalyst structures.

Electrochemical nitrate reduction offers a sustainable route for ammonia synthesis, of which the practical application is still limited by competing hydrogen evolution at industrial current density. The fundamental obstacle lies in the presence of excess *H not fully consumed by the nitrogen-containing intermediates during the hydrogenation processes. Here, the hydrogenation pathway on Cu2O was regulated by cerium doping for promoting the nitrate-to-ammonia conversion. On pristine Cu2O with the Cu0/Cu+ sites, the NO3--to-*NO and subsequent *NO-to-NH3 processes follow the Langmuir-Hinshelwood (L-H) and Eley-Rideal (E-R) hydrogenation routes, respectively. Apart from the above sites, Ce-Cu2O possesses additional Cu-O-Ce sites to enable the L-H pathway for the overall NO3RR process. Balancing the intermediates hydrogenation pathways could avoid the fierce H2 evolution derived from the residual *H, which guaranteed the high ammonia Faradaic efficiency (96.48%) for Ce-Cu2O. This work offers mechanistic guidance for regulating the hydrogen transfer routes in other multistep electrocatalytic hydrogenation reactions.

Magnetorheological fluids (MRFs) exhibit considerable application potential in precision polishing. However, studies on the influence mechanism of microstructural evolution on the macroscopic magnetorheological properties of MRFs are scarce. This study explores the effects of the hydroxyl, methyl, and carboxyl groups of amino acid ionic liquids (AAILs) on the magnetorheological properties of MRFs using AAILs as the stabilizer, water as the base liquid, and carbonyl iron (CI) as magnetic particles by combining machine learning interatomic potentials (MLIPs) and experiments. The AAILs effectively enhance the magnetorheological properties of the MRFs. Among the analyzed samples, the MRF containing hydroxyl groups exhibit the highest performance. This is because the associated AAIL forms an electrical double layer on the CI surface via electrostatic adsorption and simultaneously establishes hydrogen bonds with water molecules, forming a hydrogen-bond network that suppresses CI agglomeration and sedimentation and enhances the magnetorheological properties of the MRFs. This sample shows the highest interfacial adsorption energy and carrier-liquid molecular interaction energy, generating the densest electric double layer structure and the largest number of hydrogen bonds, resulting in the highest performance. This study combines MLIPs with experimental analysis to provide theoretical foundation and technical guidance for developing new MRFs.

Sluggish kinetics caused by 16-electron transfer hinders development of wide-temperature-range sodium-sulfur batteries. Here we report Sn-doped CoNiS multipods with an amorphous-crystalline interwoven structure. Employed as a positive electrode catalyst, the resulting sodium-sulfur battery exhibits a discharge capacity of 1320.8 mAh g-1 at 3 A g-1 after 1200 cycles at room temperature, together with stable and high-capacity electrochemical performance ranged from -20 to 50 °C. It has been evidenced that the amorphous/crystalline interfaces generated by Sn doping can adjust the microelectronic environment of Co and Ni atoms, optimize their adsorption energy toward sodium polysulfide intermediates through Co-S and Ni-S bonding, and thus decrease the energy barrier of polysulfide conversion. This interfacial regulation efficiently lowers the energy barrier of the rate-determining step and facilitates the overall reaction kinetics over a wide temperature range. This work provides an efficient amorphous/crystalline interface engineering strategy to develop high-performance catalysts.

Two-dimensional (2D) materials have shown broad application prospects in fields such as energy, environment, and aerospace owing to their unique electrical, mechanical, thermal, and other properties. With the development of artificial intelligence (AI), the discovery and design of novel 2D materials have been significantly accelerated. However, due to the lack of basic theories of material synthesis, identifying reliable synthesis processes for theoretically designed materials is a challenge. The emergence of large language model offers approaches for the reliability prediction of material synthesis processes. However, its development is limited by the lack of publicly available data sets of material synthesis processes. To address this, we present the Material Synthesis 2025 (MatSyn25), a large-scale open data set of 2D material synthesis processes. MatSyn25 contains 163,240 pieces of synthesis process information extracted from 85,160 high-quality research articles, each including basic material information and detailed synthesis process steps. Based on MatSyn25, we developed MatSyn AI, which specializes in material synthesis, and provided an interactive web platform that enables multifaceted exploration of the data set (https://matsynai.stpaper.cn/). MatSyn25 is publicly available, allowing the research community to build upon our work and further advance AI-assisted materials science.

The disparity between the complex structures of synthesized materials and their simplified computational models leads to deviations between theoretically calculated and experimental performance. To narrow this gap, we introduce the statistical descriptor φ, which is defined as the proportion of high-activity configurations in a given element combination. By considering the activity distribution of multiple structures rather than relying on a single model structure, φ can more accurately quantify macroscopic catalytic activity. Using the Seq-Equiformer model, a graph neural network we developed by augmenting EquiformerV2 with LSTM to capture dynamic structural changes during oxygen evolution reaction, we predict overpotentials for 250 million structures of 3d transition metal doped CoOOH. Based on these predictions, the value of φ for each element combination is calculated, and six optimal dopant combinations with the highest φ values are determined. For the leading MnFeNiCu combination, Bayesian optimization-driven AI experiments further optimize the elemental ratios. After only 40 experimental iterations, exploring 0.44% of the search space, the catalyst Mn0.07Fe0.09Ni0.14Cu0.01Co0.69OOH is identified, delivering an overpotential of 246.5 mV at 100 mA cm-2 and retaining 98.5% activity over 1000 h at 1 A cm-2. In validation, the statistical descriptor achieves 80% accuracy in identifying the top catalysts, a 30% improvement over single-structure screening, which evaluates the element combination based on the best configuration. The integration of statistical modeling, machine learning, and autonomous experimentation offers a powerful strategy to accelerate catalyst discovery and enhance prediction accuracy.

The disparity between the complex structures of synthesized materials and their simplified computational models leads to deviations between theoretically calculated and experimental performance. To narrow this gap, we introduce the statistical descriptor phi, which is defined as the proportion of high-activity configurations in a given element combination. By considering the activity distribution of multiple structures rather than relying on a single model structure, phi can more accurately quantify macroscopic catalytic activity. Using the Seq-Equiformer model, a graph neural network we developed by augmenting EquiformerV2 with LSTM to capture dynamic structural changes during oxygen evolution reaction, we predict overpotentials for 250 million structures of 3d transition metal doped CoOOH. Based on these predictions, the value of phi for each element combination is calculated, and six optimal dopant combinations with the highest phi values are determined. For the leading MnFeNiCu combination, Bayesian optimization-driven AI experiments further optimize the elemental ratios. After only 40 experimental iterations, exploring 0.44% of the search space, the catalyst Mn0.07Fe0.09Ni0.14Cu0.01Co0.69OOH is identified, delivering an overpotential of 246.5 mV at 100 mA cm-2 and retaining 98.5% activity over 1000 h at 1 A cm-2. In validation, the statistical descriptor achieves 80% accuracy in identifying the top catalysts, a 30% improvement over single-structure screening, which evaluates the element combination based on the best configuration. The integration of statistical modeling, machine learning, and autonomous experimentation offers a powerful strategy to accelerate catalyst discovery and enhance prediction accuracy.

Developing iron-free transition-metal based catalysts to overcome ion-leaching degradation is still a critical challenge due to their inherent low activity and poor electron transfer characteristics for oxygen evolution reaction (OER). Herein, an iron-free catalytic material integrating amorphous Ce-doped NiOOH with Ni nanocrystals was synthesized via a one-step electrodeposition method, achieving scalable fabrication of a uniform anode over 600 cm2. It shows remarkably low overpotentials of 190 mV and 332 mV to achieve 10 and 1000 mA cm- 2 in 1 M KOH, respectively. Moreover, it maintains 98.7 % activity retention after 1000 h of continuous operation at 1000 mA cm- 2. XPS, XANES, in-situ Raman spectroscopy and density functional theory (DFT) calculations demonstrate that Ni nanocrystals significantly enhance the interfacial and bulk charge transport in NiOOH, while Ce doping substantially promotes both the pre-oxidation process and OER kinetics. This work provides insights for designing iron-free catalytic materials with high alkaline OER performance.

The dual-site synergistic catalytic mechanism on NiFeOOH suggests weak adsorption of Ni sites and strong adsorption of Fe sites limited its activity toward alkaline oxygen evolution reaction (OER). Large-scale density functional theory (DFT) calculations confirm that Co doping can increase Ni adsorption, while the metal vacancy can reduce Fe adsorption. The combined two factors can further modulate the atomic environment and optimize the free energy toward oxygen-containing intermediates, thus enhancing the OER activity. Accordingly, we used Co doping and Cr vacancies to fabricate an amorphous catalyst of VCr,Co-NiFeOOH. It provides an OER overpotential of 239 mV at 100 mA cm-2 and high stability over 500 h at 500 mA cm-2 with a ∼98% potential retention. The resulting water electrolyzer based on an anion exchange membrane (AEM) exhibits a remarkable performance of 1 A cm-2 at 1.68 V in 1 M KOH. XPS, soft-XAS, and XANES combined with Bader charge analysis results reveal that the regulation of the local microenvironment can increase the valence state of Ni by Co doping, thus improving the adsorption energy on Ni sites. The Cr vacancy can alleviate the strong adsorption on Fe sites. DFT calculations confirm that the synergistic effect of Co doping and Cr vacancies can redistribute the charge on the Ni/Fe sites, optimize the d-band center of Ni and Fe, and endow the catalyst with Ni-Fe dual sites to reduce the energy barrier of the OER rate-determining step.

Understanding the mechanisms of oxygen anion electrochemical reactions within crystals has long perplexed electrochemical scientists and hindered the structural design and composition optimization of Li-ion cathode materials. Machine learning interatomic potentials (MLIP) are transforming the landscape by enabling high-accuracy atomistic modeling on a large scale in materials science and chemistry. The diversity and comprehensiveness of the dataset are fundamental to building a high-accuracy MLIP. Here, we constructed a Li1.2–xMn0.6Ni0.2O2 (x = 0–1.04) dataset that includes over 15,000 chemical non-equilibrium and chemical equilibrium structures. Using this dataset, we trained an MLIP model (multistate equilibrium potential, named MSEP) with test accuracies of 0.008 eV/atom and 0.153 eV/Å for energy and force, respectively. Through MSEP-MD simulations, we identify a kinetically viable O-redox mechanism in which the formation of transient interlayer O22−, O2− or O3− intermediates drives out-of-plane Mn and Ni migration, resulting in O2 molecules forming within the bulk structure. O3− intermediates have a certain ability to capture O2, which may help alleviate the formation of lattice O2.

In recent years, machine learning interatomic potentials (ML-IPs) have attracted extensive attention in materials science, chemistry, biology, and various other fields, particularly for achieving higher precision and efficiency in conducting large-scale atomic simulations. This review, situated in the ML-IP applications in cross-scale computational models of materials, offers a comprehensive overview of structure sampling, structure descriptors, and fitting methodologies for ML-IPs. These methodologies empower ML-IPs to depict the dynamics and thermodynamics of molecules and crystals with remarkable accuracy and efficiency. More efficient and advanced techniques from interdisciplinary research field play an important role in opening a wide spectrum of applications spanning diverse temporal and spatial dimensions. Therefore, ML-IP method renders the stage for future research and innovation promising revolutionary opportunities across multiple domains.

Due to the high cost of ultra-pure water supply and the mismatch between water sources and renewable energy distribution, the large-scale production of green hydrogen through seawater electrolysis has generated significant interest. This presents an attractive potential technology within the framework of carbon-neutral energy production. However, owing to the complex composition of seawater, particularly the competitive oxidation reactions and corrosion issues involving Cl − , seawater electrolysis has suffered from low selectivity and poor stability in oxygen evolution reaction (OER), which severely impact the efficiency of hydrogen production and hinder the practical applications. To further promote in-depth research and practical applications of seawater electrolysis, this review introduces the principles, key advantages, and challenges of seawater electrolysis. Specifically, the design strategies are categorized for highly active OER electrocatalysts for seawater electrolysis, including catalyst design, design of chemical reaction systems, and other special process design. To ensure long-term operational stability of seawater electrolysis, various strategies such as employing self-supporting materials, surface protection strategies, and electrolyzer design, are discussed. Finally, current challenges and future prospects for the industrialization of seawater electrolysis are proposed and discussed. It is expected that this review provides new insights for large-scale seawater-based hydrogen production in the future.

Large Language Models (LLMs), such as GPT-4, are precipitating a new "industrial revolution" by significantly enhancing productivity across various domains. These models encode an extensive corpus of scientific knowledge from vast textual datasets, functioning as near-universal generalists with the ability to engage in natural language communication and exhibit advanced reasoning capabilities. Notably, agents derived from LLMs can comprehend user intent and autonomously design, plan, and utilize tools to execute intricate tasks. These attributes are particularly advantageous for materials science research, an interdisciplinary field characterized by numerous complex and time-intensive activities. The integration of LLMs into materials science research holds the potential to fundamentally transform the research paradigm in this field.

Density functional theory (DFT) calculations demonstrate neighboring Pt atoms can enhance the metal activity of NiCoP for hydrogen evolution reaction (HER). However, it remains a great challenge to link Pt and NiCoP. Herein, we introduced curvature of bowl-like structure to construct Pt/NiCoP interface by adding a minimal 1 ‰-molar-ratio Pt. The as-prepared sample only requires an overpotential of 26.5 and 181.6 mV to accordingly achieve the current density of 10 and 500 mA cm −2 in 1 M KOH. The water dissociation energy barrier ( E a ) has a ~43 % decrease compared with NiCoP counterpart. It also shows an ultrahigh stability with a small degradation rate of 10.6 μV h −1 at harsh conditions (500 mA cm −2 and 50 °C) after 3000 hrs. X-ray photoelectron spectroscopy (XPS), soft X-ray absorption spectroscopy (sXAS), and X-ray absorption fine structure (XAFS) verify the interface electron transfer lowers the valence state of Co/Ni and activates them. DFT calculations also confirm the catalytic transition step of NiCoP can change from Heyrovsky (2.71 eV) to Tafel step (0.51 eV) in the neighborhood of Pt, in accord with the result of the improved H ads at the interface disclosed by in situ electrochemical impedance spectroscopy (EIS) and scanning electrochemical microscopy (SECM) tests.

Single-component electrocatalysts generally lead to unbalanced adsorption of OH- and urea during urea oxidation reaction (UOR), thus obtaining low activity and selectivity especially when oxygen evolution reaction (OER) competes at high potentials (>1.5 V). Herein, a cross-alignment strategy of in situ vertically growing Ni(OH)2 nanosheets on 2D semiconductor g-C3N4 is reported to form a hetero-structured electrocatalyst. Various spectroscopy measurements including in situ experiments indicate the existence of enhanced internal electric field at the interfaces of vertical Ni(OH)2 and g-C3N4 nanosheets, favorable for balancing adsorption of reaction intermediates. This heterojunction electrocatalyst shows high-selectivity UOR compared to pure Ni(OH)2, even at high potentials (>1.5 V) and large current density. The computational results show the vertical heterojunction could steer the internal electric field to increase the adsorption of urea, thus efficiently avoiding poisoning of strongly adsorbed OH- on active sites. A membrane electrode assembly (MEA)-based electrolyzer with the heterojunction anode could operate at an industrial-level current density of 200 mA cm-2. This work paves an avenue for designing high-performance electrocatalysts by vertical cross-alignments of active components.

The in situ characterization of the heterostructure active sites during the hydrogen evolution reaction (HER) process and the direct elucidation of the corresponding catalytic structure-activity relationships are essential for understanding the catalytic mechanism and designing catalysts with optimized activity. Hence, exploring the underlying reasons behind the exceptional catalytic performance necessitates a detailed analysis. Herein, we employed scanning electrochemical microscopy (SECM) to in situ image the topography and local electrocatalytic activity of 1T/2H MoS2 heterostructures on mixed-phase molybdenum disulfide (MoS2) with 20 nm spatial resolution. Our measurements provide direct data about HER activity, enabling us to differentiate the superior catalytic performance of 1T/2H MoS2 heterostructures compared to other active sites on the MoS2 surface. Combining this spatially resolved electrochemical information with density functional theory calculations and numerical simulations enables us to reveal the existence of hydrogen spillover from the 1T MoS2 surface to 1T/2H MoS2 heterostructures. Furthermore, it has been verified that hydrogen spillover can significantly enhance the electrocatalytic activity of the heterostructures, in addition to its strong electronic interaction. This study not only contributes to the future investigation of electrochemical processes at nanoscale active sites on structurally complex electrocatalysts but also provides new design strategies for improving the catalytic activity of 2D electrocatalysts.

Urea electrolysis can convert urea from urea-rich wastewater to hydrogen for environmental protection and sustainable energy production. However, the sluggish kinetics of urea oxidation reaction (UOR) requires valence-variable sites that are generally active at high anodic overpotentials. Herein, a robust ceramic coating is constructed with coupled tungsten nitride (WN)/nickel carbide (Ni3C) nanoparticles to achieve valence-stable catalytic sites with outstanding UOR performance. Various characterization results indicate strong interfacial electron transfer from WN to Ni3C in coupled nanoparticles, which enables reservation of Ni2+ sites without self-oxidation during UOR, quite distinct from the kinetically slow Ni3+ OOH-catalyzed UOR pathway. Theoretical calculations show that the coupled effect in WN/Ni3C leads to enhanced electron transfer from catalytic sites to adsorbed urea, and W sites are thermodynamically favorable for UOR. This efficiently lowers the barrier of rate-determining step (RDS: *CO-N-2.-> *CO center dot OH), thus enabling fast UOR kinetics and a low potential of 1.336 V at 100 mAcm(-2), which identifies this ceramic coating as one of the best UOR electrocatalysts. This work opens a new avenue for design of stable and active sites in ceramics coatings toward advanced electrocatalytic applications.

Piezoelectricity in 2D transition metal dichalcogenides (TMDs) has attracted considerable interest because of their excellent flexibility and high piezoelectric coefficient compared to conventional piezoelectric bulk materials. However, the ability to regulate the piezoelectric properties is limited because the entropy is constant for certain binary TMDs other than multielement ones. Herein, in order to increase the entropy, a ternary TMDs alloy, Mo1-xWxS2, with different W concentrations, is synthesized. The W concentration in the Mo1-xWxS2 alloy can be controlled precisely in the low-supersaturation synthesis and the entropy can be tuned accordingly. The Mo0.46W0.54S2 alloy (x = 0.54) has the highest configurational entropy and best piezoelectric properties, such as a piezoelectric coefficient of 4.22 pm V-1 and a piezoelectric output current of 150 pA at 0.24% strain. More importantly, it can be combined into a larger package to increase the output current to 600 pA to cater to self-powered applications. Combining with excellent mechanical durability, a mechanical sensor based on the Mo0.46W0.54S2 alloy is demonstrated for real-time health monitoring.

The existence of hydrogen isotopes in tritium breeding blankets indicates important research value for the realization of tritium self-sufficiency in fusion. The positive states of deuterium trapped by different irradiation defects in LiAlO2 (one of the typical ternary solid tritium breeders) have previously been indicated as O-D vibration absorption peaks in situ FT-IR after deuterium ion irradiation. The present study focused on the possible non-O-D state of deuterium which could not be directly observed by FT-IR in irradiated LiAlO2. In situ FT-IR and thermal desorption spectroscopy (TDS) was performed to single-crystal LiAlO2 irradiated by 3 keV D-2(+) at 380 K, 440 K and 500 K respectively. The increase of the total intensity of O-D vibration absorption peaks with the release of deuterium as D-2 suggests the existence of non O-D state. The non-O-D state is considered to be related to the deuterium trapped by oxygen vacancy. According to the density functional theory calculation, the valence state of hydrogen trapped by the O vacancy in LiAlO2 is-0.865. The transitions of different states (positive versus negative, different positive states) with the irradiation temperature and their effect on the release of chemical forms of deuterium have also been discussed in this paper. All above will contribute to further understanding on existence and behavior of tritium in neutron-irradiated solid breeders. (c) 2021 Elsevier B.V. All rights reserved.