Materials Science Domain Research Articles

Advances in Light Promoted Transformation of N-Heterocyclic Carbene-Ligated Boryl Radical

Organoboron compounds are integral to modern life, with extensive applications in synthesis, materials science, medicine, and various other domains closely linked to human endeavors. NHC-BH3, noted for its stability, ease of synthesis, and high reactivity as a boryl radical precursor, has emerged as a key focus in boryl radical chemistry. Recently, the visible-light-induced single electron transfer (SET) and hydrogen atom transfer (HAT) processes have garnered considerable interest, presenting innovative strategies for generating boryl radicals from NHC-BH3. In the context of this review, our focus is on the synthesis of C-B and X-B bonds under visible light irradiation, facilitated by NHC-BH3. Furthermore, we explored the role of NHC-BH3 as a hydrogen donor or halogen atom transfer reagent in the construction of C-C bond.

How Does a Generative Large Language Model Perform on Domain-Specific Information Extraction?─A Comparison between GPT-4 and a Rule-Based Method on Band Gap Extraction.

The advent of generative Large Language Models (LLMs) has greatly impacted the field of Natural Language Processing. However, it is inconclusive how generative LLMs perform on domain-specific information extraction tasks. This study compares the performance of GPT-4 and a rule-based information extraction method based on ChemDataExtractor on band gap information extraction, a task that has important implications for the materials science domain. No training data is required for either method, which is desirable because there is a lack of training data in the materials science domain compared with a variety of material information that is of interest. Manual evaluation on 415 randomly selected articles showed that the GPT-4 model achieved a higher level of accuracy in extracting materials' band gap information than the rule-based method (Correctness 87.95% vs 51.08%, Partial correctness 11.33% vs 36.87%, incorrectness 0.72% vs 12.05%). Further analysis of the errors reveals the strengths and weaknesses of the GPT-4 model compared to the rule-based method. The GPT-4 model shows stronger performance in interdependency resolution and complicated material name recognition, while it also has weaknesses in hallucination, identifying band gap values, and identifying band gap types. Revised prompt based on the error analysis leads to improved accuracy for GPT-4. To the best of our knowledge, this study is the first to compare the GPT-4 model and ChemDataExtractor for the band gap extraction task. This study provides evidence to support using generative LLMs for domain-specific information extraction tasks.

SS-DNN: A hybrid strang splitting deep neural network approach for solving the Allen–Cahn equation

The Allen–Cahn equation is a fundamental partial differential equation that describes phase separation and interface motion in materials science, physics, and various other scientific domains. The presence of interfacial width (ϵ) between two stable phases, associated with a nonlinear term, is a small positive parameter which makes the problem more challenging to solve as ϵ approaches zero. This paper proposes a novel hybrid deep splitting method to efficiently and accurately solve the Allen–Cahn equation in a convex polygonal domain in Rd(d=1,2,3). The method combines the benefits of deep learning and splitting strategies, leveraging the strengths of both approaches. Essentially, a second-order splitting method is employed to split the Allen–Cahn equation into two simpler linear and non-linear sub-problems. While the nonlinear sub-problem can be solved analytically, the deep neural network is utilized to approximate the linear sub-problem. By integrating deep learning into the splitting strategy, we achieve a more efficient and accurate solution for the Allen–Cahn equation, demonstrating promising results. We also derive an error estimate for the proposed hybrid method. Modified space adaptivity and transform learning techniques are employed to enhance the efficiency of the neural network.

Recent Advances in Piezoelectric Coupled with Photocatalytic Reaction System: Synergistic Mechanism, Enhancement Factors, and Application.

The field of photocatalysis has demonstrated numerous advantages in the domains of environmental protection, energy, and materials science. However, conventional modification methods fail to simultaneously enhance carrier separation efficiency, redox capacity, and visible light absorption solely through light activation due to the intrinsic band structure limitations of photocatalysts. In addition to modification methods, the introduction of an external field, such as a piezoelectric field, can effectively address deficiencies in each step of the photocatalytic process and enhance the overall performance. The assistance of a piezoelectric field overcomes the limitations inherent in traditional photocatalytic systems. Hence, this review provides a comprehensive overview of recent advancements in piezoelectric-assisted photocatalysis and thoroughly investigates the interaction between the alternating piezoelectric field and photocatalytic processes. Various ideas for synergistic enhancement of the piezoelectric and photocatalytic properties are also explored. This multifield catalytic system shows remarkable performance in stability, pollutant degradation, and energy conversion, distinguishing it from single catalytic systems. Finally, an in-depth analysis is conducted to address the challenges and prospects associated with piezoelectric photocatalysis technology.

Assessment of Cement Mortar Strength Mixed with Waste Copper Mine Tailings (CT) by Applying Gradient Boosting Regressor and Grid Search Optimization Machine Learning Approach

Growing environmental concerns and resource scarcity, the construction industry must investigate sustainable materials that reduce waste while improving building material properties. This study investigates the viability of using waste copper mine tailings (CT) as a partial replacement for river sand in cement mortar, assessing the impact on mechanical strength and developing a predictive model using a Gradient Boosting Regressor (GBR) and Grid Search Optimization (GSO). Copper tailings mix designs ranging from 0% to 50% replacement by volume (river sand) were developed, with a constant cement quantity while varying the proportions of sand and tailings. The experiments were carried out at Poornima University in Jaipur, using standard protocols to prepare specimens and measure their compressive strength for 0% CT to 50% CT as volume percent in the mixture. The results showed that the addition of copper tailings up to 20% (3CT2) had highly increased the mortar strength, while mix design 6CT3 showed the best strength at 30% CT. Beyond this threshold, strength declined, thus indicating an optimal replacement level. In the final step of the research, the GBR-GSO-based machine learning approach was employed for developing the predictive model for compressive strength in mortar with different contents of copper tailing for mix types 3CT and 6CT. The predictions obtained by the developed model were in very good agreement with empirical data, thus supporting the potential of machine learning to predict material performance for guiding the use of unconventional additives in construction. It could be said that this work not only represents the material properties of copper-tailing-infused mortar but also showcases state-of-the-art machine learning techniques in the building materials science domain while opening new paths for more innovative and sustainable building practices.

Recent Advances in Main Group Coordination Driven Porphyrins: A Comprehensive Review

AbstractThe novel and exciting class of porphyrin‐based compounds are the main group coordination‐driven porphyrins (MGCPs) with a central main group element into the porphyrin macrocycle. MGCPs have unique properties, reactivity, and potential applications in catalysis, sensing, biomedicine etc. This comprehensive review article discusses recent advances in the field of main group coordination driven porphyrins and explores some of its potential uses in solar cells, catalysis, antimicrobial, optoelectronic devices, sensing, and catalysis. The addition of main group elements to porphyrin systems has resulted in the production of entirely novel compounds that have intriguing qualities including increased stability and catalytic activity. Significant modifications in these unique compounds features are apparent through the analysis of their structural properties. It encompasses FTIR, proton NMR, electrical and optical characterisation in addition to an electrochemical analysis of those parameters. They serve as significant building blocks for the creation of cutting‐edge materials and technologies in a variety of scientific and technical disciplines because to their special qualities and adaptability. Researchers working in the domains of coordination chemistry, catalysis, and materials science will find this study to be of interest since it offers a thorough examination of current developments in main group coordination driven porphyrins and their prospective applications. Overall, main group porphyrins are used in many different fields, such as sensing, catalysis, and optoelectronic devices.

Machine-Learning Prediction of Curie Temperature from Chemical Compositions of Ferromagnetic Materials.

Room-temperature ferromagnets are high-value targets for discovery given the ease by which they could be embedded within magnetic devices. However, the multitude of potential interactions among magnetic ions and their surrounding environments renders the prediction of thermally stable magnetic properties challenging. Therefore, it is vital to explore methods that can effectively screen potential candidates to expedite the discovery of novel ferromagnetic materials within highly intricate feature spaces. To this end, we explore machine-learning (ML) methods as a means to predict the Curie temperature (Tc) of ferromagnetic materials by discerning patterns within materials databases. This study emphasizes the importance of feature analysis and selection in ML modeling and demonstrates the efficacy of our gradient-boosted statistical feature-selection workflow for training predictive models. The models are fine-tuned through Bayesian optimization, using features derived solely from the chemical compositions of the materials data, before the model predictions are evaluated against literature values. We have collated ca. 35,000 Tc values and the performance of our workflow is benchmarked against state-of-the-art algorithms, the results of which demonstrate that our methodology is superior to the majority of alternative methods. In a 10-fold cross-validation, our regression model realized an R2 of (0.92 ± 0.01), an MAE of (40.8 ± 1.9) K, and an RMSE of (80.0 ± 5.0) K. We demonstrate the utility of our ML model through case studies that forecast Tc values for rare-earth intermetallic compounds and generate magnetic phase diagrams for various chemical systems. These case studies highlight the importance of a systematic approach to feature analysis and selection in enhancing both the predictive capability and interpretability of ML models, while being devoid of potential human bias. They demonstrate the advantages of such an approach over a mere reliance on algorithmic complexity and a black-box treatment in ML-based modeling within the domain of computational materials science.

Bridging length scales in hard materials with ultra-small angle X-ray scattering - a critical review.

Owing to their exceptional properties, hard materials such as advanced ceramics, metals and composites have enormous economic and societal value, with applications across numerous industries. Understanding their microstructural characteristics is crucial for enhancing their performance, materials development and unleashing their potential for future innovative applications. However, their microstructures are unambiguously hierarchical and typically span several length scales, from sub-ångstrom to micrometres, posing demanding challenges for their characterization, especially for in situ characterization which is critical to understanding the kinetic processes controlling microstructure formation. This review provides a comprehensive description of the rapidly developing technique of ultra-small angle X-ray scattering (USAXS), a nondestructive method for probing the nano-to-micrometre scale features of hard materials. USAXS and its complementary techniques, when developed for and applied to hard materials, offer valuable insights into their porosity, grain size, phase composition and inhomogeneities. We discuss the fundamental principles, instrumentation, advantages, challenges and global status of USAXS for hard materials. Using selected examples, we demonstrate the potential of this technique for unveiling the microstructural characteristics of hard materials and its relevance to advanced materials development and manufacturing process optimization. We also provide our perspective on the opportunities and challenges for the continued development of USAXS, including multimodal characterization, coherent scattering, time-resolved studies, machine learning and autonomous experiments. Our goal is to stimulate further implementation and exploration of USAXS techniques and inspire their broader adoption across various domains of hard materials science, thereby driving the field toward discoveries and further developments.

Unexplored catalytic potency of a magnetic CoFe2O4/Ni-BDC MOF composite for the one-pot sustainable synthesis of 5-substituted 1-H tetrazoles

Within the domain of materials science and engineering, significant progress has led to the emergence of innovative materials based on magnetic MOFs, positioning them as promising candidates in the field of catalysis. This research aims to elaborate on the development of a finely tailored magnetic MOF composite; Ni-BDC (Nickel benzene-1,4-dicarboxylate) microflakes which was synthesized through a straightforward in-situ solvothermal approach. Extensive investigations into the structural properties of the resultant hybrid composite were carried out using various characterization techniques encompassing X-ray diffraction (XRD), Fourier-transform infrared spectroscopy (FT-IR), field emission scanning electron microscopy (FE-SEM), transmission electron microscopy (TEM), X-ray photoelectron spectroscopy (XPS), Brunauer-Emmett-Teller (BET) analysis, atomic absorption spectroscopy (AAS), energy-dispersive X-ray spectroscopy (EDX), thermogravimetric analysis (TGA) and vibrating sample magnetometry (VSM). The developed material demonstrated remarkable efficiency in expediting the synthesis of a diverse array of pharmaceutically active 5-substituted 1H-tetrazole derivatives through a multicomponent coupling reaction involving substituted aldehydes, malononitrile, and sodium azide as key components. A noteworthy feature of this approach is its adherence to environmentally friendly reaction conditions, thus making it superior in comparison to previously established methodologies. Furthermore, a plausible mechanistic pathway has been proposed to elucidate the impressive performance of the developed catalytic system in the targeted reaction. It is anticipated that the current research will not only contribute to the purposeful designing of advanced magnetic MOF-based architectures but will also play a pivotal role in promoting sustainable practices in industrial processes, addressing pressing environmental and economic concerns.

Investigation of thermodynamic, magnetic, structural and electronic properties of full-Heusler Co2TiZ (Z=Si,Ge,Sn) alloys

Our expedition into the domain of materials science was an immersive journey, where we tapped into the dynamic synergy between density functional theory (DFT) and the FP-LAPW method. Guided by the enigmatic allure of full-Heusler Co2TiZ (Z=Si, Ge, Sn) alloys, our inquiry embarked on a quest marked by extensive analysis and profound discovery. Throughout this meticulous odyssey, we meticulously unraveled the complex tapestry woven by the structural, magnetic, and electronic properties inherent in both cubic CuHg2Ti-type and AlCu2Mn-type structures. Among the myriad revelations unearthed in our expedition, none stood as starkly as the remarkable stability exhibited by the ferromagnetic state, a stark departure from its nonmagnetic counterpart. This stability was further underscored by the presence of integer magnetic moments, serving as a defining characteristic of the ferromagnetic state. As our journey into the heart of materials science progressed, we peeled back layers of complexity to reveal a fascinating dichotomy within the density of states and band structure. Here, we witnessed a symphony of contrasting behaviors: while majority spins embraced semiconductor characteristics, minority spins danced to the metallic tune, painting a vivid portrait of the alloys' intrinsic half-metallic essence. Leveraging the GGA-PBE methodology, our investigation meticulously probed both fundamental and half-metallic gaps, thus unveiling a comprehensive portrait of the materials' electronic landscape. Moreover, our inquiry meticulously preserved a direct band gap at the X → X point, an essential facet imbued with profound implications for the materials' optoelectronic attributes. These insights, far from being confined to the realm of academic curiosity, serve as catalysts for transformative progress in materials design and optimization. By unraveling the intricate interplay between structure and electronic behavior, our findings lay a sturdy foundation for pioneering applications across diverse fields such as spintronics, catalysis, and energy conversion. In summation, our odyssey stands as a beacon guiding the way toward a new epoch of functional materials, poised to transcend the boundaries of technological innovation and unleash unprecedented capabilities across multifarious domains.

A MATLAB Toolbox for Findable, Accessible, Interoperable, and Reusable Atom Probe Data Science.

Atom probe tomography (APT) data analytics have traditionally been based on manual analytics by researchers. As newer atom probes together with focused ion beam-based specimen preparation have opened APT to many more materials, yielding much more complex mass spectra, building up a systematic understanding of the pathway from raw data to final interpretation has increasingly become important. This demands a system in which the data and treatment can be traced, ideally by any interested party. Such an approach of findable, accessible, interoperable, and reusable (FAIR) data and analysis policies is becoming increasingly important, not just in APT. In this paper, we present a toolbox, written in MATLAB, which allows the user to store the raw and processed data in a standardized FAIR format (hierarchical data format 5) and process the data in a largely scriptable environment to minimize manual user input. This allows for the experiment data to be interchanged without owner explanations and the analysis to be reproduced. We have devised a metadata scheme that is extensible to other experiments in the materials science domain. With this toolbox, collective knowledge can be built up, and a large number of data sets can be analyzed in a fully automated fashion.

Unveiling yield strength of metallic materials using physics-enhanced machine learning under diverse experimental conditions

In the materials science domain, the accurate prediction of the yield strength of metallic compositions has often resulted in extensive experimental endeavors, leading to inefficiencies in both time and resources. Here, we introduce an innovative approach to predict yield strength, which can be applied to a variety of metallic substances ranging from the simplest pure metals to the most intricate alloys under varying temperatures and strain rates. The fusion of grain boundary sliding mechanism and cutting-edge machine-learning algorithm forges an expansive framework, which can help realize the critical factors influencing yield strength. The validity and wide applicability of the proposed framework were rigorously confirmed through experimental evaluations conducted on selected Fe-based alloys, such as Fe60Ni25Cr15, Fe60Ni30Cr10, and Fe64Ni15Co8Mn8Cu5. This breakthrough study significantly streamlines experimental design processes, optimizes resource utilization, and marks a significant leap forward in creating a reliable predictive framework for realizing material properties.

Development of electrophoresed anti-corrosion coating for copper from functionalized multi layered graphene nano-sheets synthesized through anionic acidic electrochemical exfoliation

Following the discovery of graphene, a two-dimensional carbonaceous substance, numerous possibilities in materials science domain have evolved, one being its function as a corrosion inhibitor. This investigation undertakes the synthesis of functionalized multi-layered graphene nanosheets (FMLGNs) from pyrolytic graphite sheets through electrochemical exfoliation involving four distinct acid-based solutions (H2SO4, HCLO4, HNO3 and combination of the above three in 1:1:1 ratio). Post-exfoliation analysis of FMLGNs included X-ray diffraction (XRD) wherein 2θ values were reported at 26° confirming graphene's presence. Raman spectroscopy reiterated the same observation along with an increase in ID/IG ratio to ≈ 1 hinting at the presence of functional groups and defects. UV–visible spectroscopy indicating π-π* transitions, Fourier transform infrared spectroscopy (FTIR) showing CO stretching and X-ray photoelectron spectroscopy (XPS) with a C/O ratio = 3.12 gave an idea about the functionalization in FMLGNs. Average particle sizes of FMLGNs was in the range of 400–500 nm and zeta potential measurements showed stable dispersion behaviour (≈−40 mV). Utilizing scanning electron microscopy (SEM), atomic force microscopy (AFM) and transmission electron microscopy (TEM), authors observed a layered (10–15 layers), overlapping shape of FMLGNs. Electrophoretic deposition (EPD) method was employed to coat FMLGNs onto copper substrates under various iterative conditions (number of layers and time of deposition) which produced depositions of ≈20–30 μm. SEM images displayed adequate coverage of graphene sheets on copper surfaces, backed by energy-dispersive x-ray spectroscopy (EDS). Crack propagation resistance (CPR) values of 34.03 N highlighted better adherence than other similar coatings. The coated copper samples were subjected to cyclic voltammetry (CV), potentio-dynamic polarization (PDP) and electrochemical impedance (EIS) tests in 3.5 wt% NaCl solution for analysing the inhibitory impacts of coatings. An inhibition efficiency of 85 % and charge transfer resistance of 5535 Ω.cm2 was observed, which highlighted better corrosion mitigation than reported literatures. The authors also identified that 1 layered coatings showed better reliability than two layered coatings.

Synthesis, Spectroscopic and Antibacterial Studies of Some N-Phenylpyridinium Chloride Derivatives

Aim and Background: This study represents a dedicated effort to advance organic chemistry and contribute to the development of innovative therapeutic agents through the synthesis, spectroscopic characterization, and antibacterial activities of N-phenylpyridinium chloride derivatives. Heterocyclic compounds, integral to vital natural products, have spurred interest for their potential incorporation into the design of biologically active molecules. Methodology: The rigorous methodology employed stringent laboratory conditions, utilizing high-grade reagents, and implementing solvent purification through distillation and crystallization. The synthesis involved refluxing pyridine and 1-chloro-2, 4-dinitrobenzene in ethanol, resulting in N-2, 4-dinitrophenylpyridinium chloride. Derivatization with aniline produced 5-anilino N-phenyl-2, 4-pentadienylideniminium chloride, undergoing cyclization and meticulous purification. Result: Antibacterial evaluations demonstrated significant efficacy, with 1-(2-chlorophenyl) pyridinium chloride exhibiting pronounced sensitivity against E. coli and S. aureus. Paper chromatography revealed strong affinities for the stationary phase, indicative of their inherently polar nature. Fourier transform infrared (FTIR) spectroscopy provided insights into diverse functional groups. Conclusion: The Meticulous synthesis of N-phenylpyridinium chloride derivatives has yielded compounds with notable antibacterial properties, showcasing their potential applications in both medical and materials science domains. This study concludes by emphasizing the critical importance of continued exploration in this promising research trajectory, highlighting the essential intersection of organic chemistry with advancements in therapeutic innovation.

Nanotech for Fertilizers and Nutrients-Improving Nutrient use Efficiency with Nano-Enabled Fertilizers

The development of nano-enabled fertilizers presents new opportunities to improve crop nutrient use efficiency and reduce environmental impacts of agriculture. Nanoparticles, nano capsules, and nano clays can be engineered to control the release rate of nutrients to better match crop demands over time. Slow-release nano fertilizers may enhance nutrient absorption by plants while mitigating nutrient losses to the environment. Additionally, nano fertilizers can facilitate co-delivery of nutrients, growth regulators, and pesticides, allowing for more precise crop management practices. This review synthesizes current research on synthesis techniques, characterization methods, and agronomic testing results for a range of nano fertilizer products. Key nutrient carriers reviewed include mesoporous silica nanoparticles, layered double hydroxides, cellulose nanocrystals, and halloysite nanotubes loaded with nitrogen, phosphorus, potassium, and micronutrients. Release kinetics depend on nano fertilizer composition, size, and shape, as well as environmental conditions. Field studies indicate positive impacts of nano fertilizers on crop yield, nutrient use efficiency, and pest resistance compared to conventional fertilizer formulations. However, questions remain regarding large-scale feasibility, economic viability, environmental fate, and biological impacts of nano-enabled fertilizers. Ongoing interdisciplinary research across the domains of materials science, agronomy, ecology, and economics is required to develop nano fertilizers that maximize production efficiency while minimizing risks.

Latest Innovation in Robotics

Robotics is the science of creating and assembling tangible robots to enhance automation and creativity. Engineering and computer science are combined in the field of robotics, which deals with the creation, manufacturing, and use of robots. Robotics is undergoing a fast evolution with ground-breaking discoveries that are changing entire sectors and societal contexts. Recent advances in AI have accelerated robotics' progress toward more flexibility and autonomy. There are many different types of robotics. A robot could be an artificial intelligence (AI) device that looks like a person, or it could be a robotic application like robotic process automation, which resembles how people interact with software to carry out repetitive, rule-based tasks. The continued convergence of robotics with artificial intelligence, materials science, and other interdisciplinary domains holds the potential to open up new avenues for automation and human-robot cooperation. This study offers an overview of the most recent advancements in robotics, including significant technological innovations, cutting-edge applications, and developing trends

Efficient surrogate models for materials science simulations: Machine learning-based prediction of microstructure properties

Determining, understanding, and predicting the so-called structure–property relation is an important task in many scientific disciplines, such as chemistry, biology, meteorology, physics, engineering, and materials science. Structure refers to the spatial distribution of, e.g., substances, material, or matter in general, while property is a resulting characteristic that usually depends in a non-trivial way on spatial details of the structure. Traditionally, forward simulations models have been used for such tasks. Recently, several machine learning algorithms have been applied in these scientific fields to enhance and accelerate simulation models or as surrogate models. In this work, we develop and investigate the applications of six machine learning techniques based on two different datasets from the domain of materials science: data from a two-dimensional Ising model for predicting the formation of magnetic domains and data representing the evolution of dual-phase microstructures from the Cahn–Hilliard model. We analyze the accuracy and robustness of all models and elucidate the reasons for the differences in their performances. The impact of including domain knowledge through tailored features is studied, and general recommendations based on the availability and quality of training data are derived from this.

Expanding from materials to biology inspired by biomineralization

AbstractBiomineralization is the intricate process by which living organisms orchestrate the formation of organic–inorganic composites by regulating the nucleation, orientation, growth, and assembly of inorganic minerals. As our comprehension of biomineralization principles deepens, novel strategies for fabricating inorganic materials based on these principles have emerged. Researchers can also harness biomineralization strategies to tackle challenges in both materials' science and biomedical fields, demonstrating a thriving research field. This review begins by introducing the concept of biomineralization and subsequently shifts its focus to a recently discovered chemical concept: inorganic ionic oligomers and their cross‐linking. As a novel approach for constructing inorganic materials, the inorganic ionic oligomer‐based strategy finds applications in biomimetic regeneration and repair of hard tissues, such as teeth and bones. Aside from innovative methods for material fabrication, biomineralization has emerged as an alternative method for tackling biomedical challenges by integrating materials with biological organisms, facilitating advancements in biomedical fields. Emerging material‐biological integrators play a critical role in areas like vaccine improvement, cancer therapy, universal blood transfusion, and arthritis treatment. This review highlights the profound impact of biomineralization in the development and design of high‐performance materials that go beyond traditional disciplinary boundaries, potentially promoting breakthroughs in materials science, chemical biology, biomedical, and numerous other domains.

High-entropy alloy nanomaterials for electrocatalysis.

High-entropy alloys (HEAs) exhibit a remarkable capacity to modulate geometric and electronic structures for the construction of catalysts with unpredictable and exceptional performance, and have attracted substantial acclaim within the domain of materials science. In this comprehensive review, we present a thorough summary of the synthesis and multiple applications of HEAs in the realm of electrocatalysis. Our review encompasses the diverse synthesis methodologies of HEA nanomaterials and their pivotal roles in amplifying electrocatalytic performance in hydrogen evolution reactions (HERs), oxygen evolution reactions (OERs), oxygen reduction reactions (ORRs), alcohol oxidation reactions (AORs), and CO2 reduction reactions (CO2RRs), and more. Furthermore, we address the intricate challenges and promising avenues that lie ahead in this research area. Reviewing recent breakthroughs, emerging paradigms, and prospects on the horizon, it becomes increasingly evident that HEAs harbor immense potential to reshape the landscape of energy conversion and storage, and emerge as paramount contenders for the development of cutting-edge electrocatalytic materials that hold the key to a sustainable energy future.

MaScQA: investigating materials science knowledge of large language models

Different materials science domains from which questions are present in Materials Science Question Answering (MaScQA) database.