jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Mohamed Abu Shuheil | G. Padma Priya | Amaal Mohammed Ali | Yesudass Sasikumar | Ahmed Kareem Obaid Aldulaimi | Renu Sharma | A. C. Singh | Radwan Ali | Mosstafa Kazemi
Source: RSC Advances (article, 2026-01-01)
Field: Chemistry — Organic Chemistry

Recent advances in magnetic nanocatalysts for synthesis of imidazo[1,2- <i>a</i> ]pyridine frameworks Magnetic nanocatalysts (MNCs) have emerged as powerful and sustainable catalytic platforms for the synthesis of imidazo[1,2-<i>a</i>]pyridine derivatives, an important class of nitrogen-fused heterocycles with broad applications in medicinal chemistry, materials science, and agrochemistry. Their nanoscale dimensions, high surface area, and chemically tailorable surfaces provide enhanced catalytic activity, while their magnetic cores enable rapid and efficient recovery using an external magnet, eliminating the need for energy-intensive purification steps. Recent advances demonstrate that Fe<sub>3</sub>O<sub>4</sub>, CoFe<sub>2</sub>O<sub>4</sub>, γ-Fe<sub>2</sub>O<sub>3</sub>, and related magnetic nanomaterials-either alone or functionalized with metals, ligands, ionic liquids, or polymeric shells-can effectively promote classical condensations, Groebke-Blackburn-Bienaymé multicomponent reactions, Aldehyde-Amine-Alkyne coupling (A<sup>3</sup>-couplings), and oxidative cyclizations leading to diverse imidazo[1,2-<i>a</i>]pyridines under mild, green conditions. These catalysts often exhibit excellent stability, minimal metal leaching, and reusability across multiple cycles without significant loss of activity. Mechanistically, magnetic nanocatalysts facilitate carbonyl activation, imine formation, alkyne or isocyanide activation, and intramolecular cyclization, enabling high yields and broad substrate tolerance. Compared with conventional catalysts, they offer superior efficiency, selectivity, and environmental compatibility. This review summarizes key developments in magnetically recoverable catalytic systems for imidazo[1,2-<i>a</i>]pyridine synthesis and highlights their growing significance in advancing sustainable heterocyclic chemistry. Nanomaterial-based catalyst ; Catalysis ; Reusability ; Intramolecular force ; Imine ; Alkyne ; Isocyanide ; Surface modification Synthesis and Reactivity of Heterocycles ; Multicomponent Synthesis of Heterocycles ; Synthesis and bioactivity of alkaloids
Authors: Yinan Wang | Qiang Xiao | Can Liu | Wenjun He | Xinning Ren | Meifeng Xiao | Qijun He | Xue Pan | Fang He
Source: Chinese Medicine (article, 2026-01-13)
Field: Materials Science — Biomaterials

Supramolecular self-assembly in Traditional Chinese Medicine: molecular mechanisms, material basis of decoction efficacy, compatibility interpretation, and biomedical applications Traditional Chinese Medicine (TCM), characterized by multi-component and multi-target pharmacological effects, boasts millennia of clinical application. The complexity of its pharmacological material basis extends beyond individual chemical components to include physical structures formed among constituents. Supramolecular chemistry offers a new perspective for elucidating this basis. This review systematically summarizes recent advances in supramolecular self-assemblies within TCM, focusing on molecular formation mechanisms, key chemical component characteristics driving assembly, their roles in decoctions, and the supramolecular basis for "efficacy enhancement and toxicity reduction" in compatibility theory. The biomedical applications of supramolecular self‑assemblies in TCM are also reviewed. Ultimately, this work aims to offer new insights into TCM's material basis, clarify compatibility mechanisms, advance the supramolecular interpretation of TCM theories, and provide perspectives for TCM modernization and drug development. Supramolecular chemistry ; Compatibility (geochemistry) ; Traditional Chinese medicine ; Supramolecular assembly Supramolecular Self-Assembly in Materials ; Supramolecular Chemistry and Complexes ; Crystallography and molecular interactions
Authors: Wafaa Zriouel
Source: Turkish computational and theoretical chemistry/Turkish computational and theoretical chemistry : (article, 2026-01-16)
Field: Materials Science — Materials Chemistry

Rosemary Essential Oil as a Sustainable Corrosion Inhibitor for Copper: Quantum Chemical Insights, Characterization, Adsorption Mechanisms applying Monte Carlo, and POM analysis This study uses computational approaches to predict and investigate the inhibition behavior of rosemary essential oil (REO) as a green corrosion inhibitor. These approaches are based on calculating quantum parameters and Mulliken atomic charges using Density Functional Theory (DFT) combined with a Monte Carlo simulation to explain the adsorption mechanism and a POM (Petra/Osiris/Molinspiration) analysis. The chemical composition analysis revealed eucalyptol (49.01%), alpha-pinene (17.31%), and beta-caryophyllene (6.42%) as the major constituents. Quantum chemical calculations identified alpha-thujene, beta-myrcene, and alpha-pinene as key inhibitors based on their electron-donating abilities, moderate energy gaps, and higher softness values, indicating a strong potential for adsorption onto metal surfaces. Mulliken charge analysis highlighted the significance of oxygenated compounds, especially alpha-terpineol, due to its highly negative oxygen charges, which suggest strong interactions with metal surfaces. Monte Carlo simulations showed that gamma-cadinene exhibited the highest adsorption energies in both gas (-53.259 kJ/mol) and aqueous (-631.011 kJ/mol) phases, indicating a robust interaction with the copper surface. Humulene and alpha-terpineol also showed significant adsorption characteristics. The OSIRIS and Molinspiration assessments confirmed the molecules' environmental safety and balanced lipophilicity, which enhanced their corrosion inhibition capabilities. The results indicate that the corrosion inhibition of the Rosemary essential oil is due to a combination of physical and chemical adsorption mechanisms, with possible synergistic effects among its constituents, making it an effective and sustainable corrosion inhibitor. These computational insights provide a foundation for understanding REO's behavior before progressing to experimental electrochemical evaluations. Adsorption ; Corrosion inhibitor ; Quantum chemical ; Density functional theory ; Corrosion ; Mulliken population analysis ; HSAB theory ; Monte Carlo method Corrosion Behavior and Inhibition
Authors: Jai Kumar | Nadeem Hussain Solangi | Rana R. Neiber | Fangyuan Bai | Victor Charles | Pengfei Zhai | Zhuanpei Wang | Xiaowei Yang
Source: Nano-Micro Letters (article, 2026-01-04)
Field: Materials Science — Materials Chemistry

Advancing Energy Development with MBene: Chemical Mechanism, AI, and Applications in Energy Storage and Harvesting MXene derivatives are notable two-dimensional nanomaterials with numerous prospective applications in the domains of energy development. MXene derivative, MBene, diversifies its focus on energy storage and harvesting due to its exceptional electrical conductivity, structural flexibility, and mechanical properties. This comprehensive review describes the sandwich-like structure of the synthesized MBene, derived from its multilayered parent material and its distinct chemical framework to date. The fields of focus encompass the investigation of novel MBenes, the study of phase-changing mechanisms, and the examination of hex-MBenes, ortho-MBenes, tetra-MBenes, tri-MBenes, and MXenes with identical transition metal components. A critical analysis is also provided on the electrochemical mechanism and performance of MBene in energy storage (Li/Na/Mg/Ca/Li-S batteries and supercapacitors), as well as conversion and harvesting (CO<sub>2</sub> reduction, and nitrogen reduction reactions). The persistent difficulties associated with conducting experimental synthesis and establishing artificial intelligence-based forecasts are extensively deliberated alongside the potential and forthcoming prospects of MBenes. This review provides a single platform for an overview of the MBene's potential in energy storage and harvesting. MXenes ; Energy storage ; Electrochemical energy storage ; Chemical energy ; Energy transformation ; Mechanism (biology) ; Renewable energy ; Energy harvesting MXene and MAX Phase Materials ; Supercapacitor Materials and Fabrication ; Ammonia Synthesis and Nitrogen Reduction
Authors: Peng Zhou | Yiyang Lin | Zinuo Wang | Duo Wang | Łukasz Maj | Tao Zhang
Source: Corrosion Communications (article, 2026-01-01)
Field: Materials Science — Metals and Alloys

Knowledge of corrosion: A mini-review of the construction of solubility-precipitation phase diagram for corrosion prevention of alloys • The construction of solubility-precipitation phase diagram for hydroxides, oxides and other species was reviewed. • This step-by-step take-away guidance includes the correction of the diagram under elevated temperature. • Applications of the diagram in new corrosion-resistant alloy design, corrosion mechanism and surface treatment were reviewed. • The limitations and prospects of the diagram were briefly discussed. This work presents a short review on the construction process of precipitation diagrams of hydroxides, oxides and other species, which offers a step-by-step take-away guidance for readers. The utility of the diagram in alloy design, corrosion mechanism analysis, and surface treatment is reviewed, demonstrating its efficacy in providing first-order thermodynamic estimation of interfacial precipitation. Current limitations, including the inability to directly extract kinetic parameters and insufficient representation of multi-species interactions in complex environments, were also briefly discussed. Future advancements are envisioned through integration with artificial intelligence, enhancing predictive capabilities in corrosion and protection research. Phase diagram ; Alloy ; Corrosion ; Diagram ; CALPHAD ; Work (physics) ; Pourbaix diagram Hydrogen embrittlement and corrosion behaviors in metals ; Corrosion Behavior and Inhibition ; High-Temperature Coating Behaviors
Authors: Sümeyra Yamçıçıer | Çağatay Yamçıçıer
Source: International Journal of Hydrogen Energy (article, 2026-01-01)
Field: Materials Science — Materials Chemistry

Structural validation, electronic bonding, and realistic hydrogen storage properties of novel Li2BH5 and Li2AlH5 complex hydrides Hydrogen storage ; Density functional theory ; Orthorhombic crystal system ; Gravimetric analysis ; Hydrogen ; Ab initio ; Ab initio quantum chemistry methods Hydrogen Storage and Materials ; Fiber-reinforced polymer composites ; Thermodynamic and Structural Properties of Metals and Alloys
Authors: Di Zhang | Xue Jia | Yuhang Wang | Heng Liu | Qian Wang | Seong‐Hoon Jang | Daksh Shah | Songbo Ye | Hung Ba Tran | Hao Li
Source: Chemical Science (article, 2026-01-01)
Field: Materials Science — Materials Chemistry

Digital materials ecosystem: from databases to AI agents for autonomous discovery The concept of a digital materials ecosystem represents a new paradigm in materials research, where data, theory, and automation are integrated into a unified and iterative framework. By combining reliable databases, physical frameworks, and intelligent data analysis, materials discovery is evolving from empirical exploration toward a systematic and predictive science. The rapid growth of data and artificial intelligence (AI) has enabled the identification of complex structure-property relationships, while advances in automated synthesis and high-throughput characterization are closing the loop between prediction and validation. Looking forward, the field must focus on building trustworthy and benchmarked datasets, developing interpretable and high-precision models, and designing AI tools that embody human scientific reasoning. Equally important is ensuring standardization and consistency between digital inputs and experimental responses. Together, these efforts will transform materials discovery from data accumulation into genuine knowledge generation, paving the way for an autonomous and self-improving research ecosystem that accelerates both fundamental understanding and technological innovation. Automation ; Digital ecosystem ; Digital data ; Intelligent agent Machine Learning in Materials Science ; Sustainable Industrial Ecology ; Digital Transformation in Industry
Authors: Di Zhang | Yuanzheng Chen | Chuanyu Liu | Y. H. Liu | Hongliang Xin | Jiayu Peng | Pengfei Ou | H. C. Li
Source: Angewandte Chemie International Edition (article, 2026-02-17)
Field: Materials Science — Materials Chemistry

Accelerating Catalyst Materials Discovery With Large Artificial Intelligence Models The integration of artificial intelligence (AI) into catalysis is fundamentally reshaping the research paradigm of catalyst discovery. Unlike traditional trial-and-error approaches, AI-empowered data-driven technologies, particularly large AI models such as universal machine learning interatomic potentials (MLIPs) and large language models (LLMs), offer unprecedented capabilities in exploring complex spaces, predicting catalytic performance, and accelerating rational design. Standing at the forefront of data-driven science, we underscore how databases, universal MLIPs, and LLMs are revolutionizing the traditional catalysis paradigm and bridging the ontology-concept-computation-experiment continuum. We then demonstrate significant recent progress, and discuss their potential and challenges in the catalytic field. By leveraging cutting-edge universal MLIPs and LLMs, researchers can conduct large-scale simulations, highly efficient data acquisition, training, and prediction, and even self-directed research in the field of catalysis. Looking ahead, these advantages enable the rapid development of target catalysts, which will be propelled by integrated universal MLIPs, multimodal LLMs, and automation systems. Developments in these domains will pave the way toward AI-empowered closed-loop platforms and cross-disciplinary Digital Materials Ecosystems that broaden the discovery landscape and foster cross-materials innovation, marking the dawn of a new era in which catalyst materials discovery is perpetually accelerating. Field (mathematics) ; Bridging (networking) ; Automation ; Applications of artificial intelligence Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Artificial Intelligence in Healthcare and Education
Authors: Sachin Kumar Sharma | Sandra Gajević | Lokesh Kumar Sharma | Yogesh Sharma | Mohit Sharma | Saša Milojević | Slobodan Savić | Blaža Stojanović
Source: Crystals (article, 2026-01-25)
Field: Materials Science — Ceramics and Composites

From Processing to Performance: Innovations and Challenges in Ceramic-Based Materials In aerospace, defense, and energy systems, ceramic matrix composites (CMCs) are smart structural materials designed to function continuously in harsh mechanical, thermal, and oxidative conditions. Using high-strength fiber reinforcements and tailored interphases that enable damage-tolerant behavior, their creation tackles the intrinsic brittleness and low fracture toughness of monolithic ceramics. With a focus on chemical vapor infiltration, polymer infiltration and pyrolysis, melt infiltration, and additive manufacturing, this paper critically analyzes current developments in microstructural design, processing technologies, and interfacial engineering. Toughening mechanisms are examined in connection to multiscale mechanical responses, including controlled debonding, fiber bridging, fracture deflection, and energy dissipation pathways. Cutting-edge environmental barrier coatings are assessed alongside environmental durability issues like oxidation, volatilization, and hot corrosion. High-performance braking, nuclear systems, hypersonic vehicles, and turbine propulsion are evaluated as emerging uses. Future directions emphasize self-healing systems, ultra-high-temperature design, and environmentally friendly production methods. Brittleness ; Toughness ; Ceramic ; Turbine blade ; Durability ; Environmentally friendly ; Fracture toughness ; Aero engine ; Ceramic matrix composite Advanced ceramic materials synthesis ; Fiber-reinforced polymer composites ; High-Temperature Coating Behaviors
Authors: Jiwei Zhao | Peixuan Zhu | Zhibin Wen | Fanyi Tang | Bin Zheng | Rongrong Zhu | Haoliang Qian | Cen Qian | Huan Lu | HongSheng Chen
Source: PhotoniX (article, 2026-01-06)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Adaptive transparent cloaking tunnel enabled by Meta-Reinforcement-Learning Metasurfaces Abstract Conventional electromagnetic cloaking paradigms predominantly necessitate the encasing of static objects within predefined topological enclosures, fundamentally restricting invisibility to fixed, closed geometries. Realizing dynamic, adaptive concealment for arbitrary moving targets within an open, boundary-free aperture remains a formidable challenge. Here, we report a meta-reinforcement-learning metasurface (Meta 2 Surface) that enables the first experimental demonstration of a "transparent cloaking tunnel" (TCT)—an open corridor permitting the undetected passage of diverse objects. Distinguished from traditional adaptive cloak, the Meta 2 Surface employs a sensor-in-the-loop meta-policy governed by a task-adaptive hypernetwork. This architecture fuses real-time sensing with historical interaction trajectories to instantly synthesize impedance strategies that actively nullify object-dependent scattering with millisecond-scale latency. Comprehensive full-wave simulations and microwave experiments confirm robust, high-fidelity cloaking of diverse dynamic targets—varying in shape, size, material, and trajectory—even under abrupt object substitution. By transitioning invisibility from static encapsulation to a dynamic, open architecture, this work establishes a new paradigm for fusing artificial intelligence with reconfigurable metasurfaces to achieve cognitive, large-scale electromagnetic wave control. Cloaking ; Invisibility ; Metamaterial ; Microwave ; Scattering ; Electromagnetic radiation ; SPHERES Metamaterials and Metasurfaces Applications ; Advanced Wireless Communication Technologies ; Advanced Antenna and Metasurface Technologies
Authors: A. S. Shruthi | Abhilash Ravikumar
Source: Discover Electrochemistry. (article, 2026-01-03)
Field: Materials Science — Materials Chemistry

Machine learning-enhanced exploration of 2D MXene van der Waals heterostructures for energy storage Abstract The development of materials for advanced battery technologies is being revolutionized by two-dimensional (2D) MXene van der Waals (vdW) heterostructures, which offer improved regulation of interfacial chemistry, ion mobility, and electronic structure. Despite their considerable theoretical promise for energy storage applications, MXenes encounter challenges related to material instability and the extensive array of unexamined heterostructure combinations that impede practical implementation. This perspective explores the potential of machine learning (ML) to transform the identification of resilient and efficient 2D MXene van der Waals heterostructures for electrochemical energy storage. Machine learning approaches can achieve orders of magnitude speedup over traditional DFT methods while maintaining chemical accuracy. We examine present computational constraints, particularly the difficulties in precisely simulating intricate dynamic processes and van der Waals interactions within MXene systems. The amalgamation of density functional theory (DFT) with machine learning methodologies presents a promising strategy for assessing several prospective 2D MXene configurations while preserving high predictive precision. Through the analysis of recent advancements in MXene-based systems, including vanadium carbide (V 2 CT x ) achieving experimental capacities up to 631 mAh g −1 , double transition metal VNbCT x reaching 520.5 mAh g −1 , andniobium carbide (Nb 2 CT x ) demonstrating versatile multi-ion storage capabilities across lithium, sodium, and potassium systems, we illustrate how integrated ML–DFT frameworks can expedite systematic discovery initiatives for dependable and efficient materials that are crucial for actualizing the potential of 2D MXene van der Waals heterostructures in advanced energy storage applications. Graphical abstract MXenes ; van der Waals force ; Heterojunction ; Density functional theory ; Energy storage ; Battery (electricity) MXene and MAX Phase Materials ; 2D Materials and Applications ; Machine Learning in Materials Science
Authors: Ran Chen | Yuanwei He | Zitao Chen | Haodong Ruan | Ying Pan | Alireza Nezamzadeh-Ejhieh | Jianqiang Liu | Chengyu Lu
Source: Dalton Transactions (article, 2026-01-01)
Field: Chemistry — Inorganic Chemistry

Machine learning and AI empowering metal–organic frameworks: synthesis, performance prediction and therapeutic application Machine learning (ML), a subset of artificial intelligence (AI), has emerged as a powerful tool to address the bottlenecks in metal-organic framework (MOF) research. With the exponential growth of structural libraries rendering traditional trial-and-error experimentation intractable, data-driven models enable efficient high-throughput screening and accurate prediction of MOF properties. In this review, we discussed the mechanism of ML and the database of MOFs. Then the latest applications of ML in the field of MOFs are highlighted, including the prediction of the synthesis route, crystal structure, ability of drug loading, ability to adsorb gases, and research progress of ML in diagnosing diseases. Finally, the challenges and limitations of ML in MOFs research are also discussed. Studying various ML algorithms to solve the performance prediction problems of MOFs in practical applications will help uncover the intrinsic connections between MOF features and specific target properties, enhance the efficiency of materials science research, and promote the efficient application of ML in MOFs. Field (mathematics) ; Rendering (computer graphics) ; Performance prediction ; Mechanism (biology) ; Predictive modelling Metal-Organic Frameworks: Synthesis and Applications ; Nanocluster Synthesis and Applications ; X-ray Diffraction in Crystallography