jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Hamid Mushtaq | Simona Piccolella | J. A. Mendiola | Lídia Montero | Elena Ibáñez | Severina Pacifico
Source: Foods (article, 2025-01-17)
Field: Chemistry — Organic Chemistry

Recovery of Bioactive Constituents from Olive Leaf Pruning Waste of Five Different Cultivars: A Comparison of Green Extraction Techniques to Maximize Health Benefits Sustainable agro-waste revaluation is critical to enhance the profitability and environmental footprint of the olive oil industry. Herein, the valorization of olive leaf pruning waste from five cultivars ('Caiazzana', 'Carolea', 'Itrana', 'Leccino', and 'Frantoio') employed green extraction methods to recover compounds with potential health benefits. Sequential ultrasound-assisted maceration (UAM) in <i>n</i>-hexane and ethanol was compared with a compressed fluid extraction strategy consisting of supercritical fluid extraction (SFE) and pressurized liquid extraction (PLE) for their efficiency in recovering distinct classes of bioactives. Chemical profiling by UHPLC-HR-MS/MS (ultra-high-performance liquid chromatography high-resolution tandem mass spectrometry) and GC-MS (gas chromatography mass spectrometry) showed that UAM-EtOH effectively extracted polyphenols (especially luteolin derivatives) and triterpenes (notably maslinic acid), while PLE yielded the highest amount of secoiridoids (e.g., secologanoside). PLE extracts showed better antiradical activities, putatively due to a higher content of flavonoids, secoiridoids, and HCA derivatives than UAM-EtOH ones, as these latter also contained 20-40% (cultivar-dependent) of triterpenes. SFE extracts with a higher concentration of fatty acids and triterpenes showed moderate antioxidant activities but very high AChE inhibition. This study highlights the importance of selecting appropriate extraction methodologies based on the target bioactive compounds and underscores the potential of olive leaf extracts for sustainable bio-products. Pruning ; Cultivar ; Extraction (chemistry) ; Horticulture ; Biology ; Botany ; Biotechnology ; Chemistry ; Chromatography Edible Oils Quality and Analysis ; Phytochemicals and Antioxidant Activities ; Essential Oils and Antimicrobial Activity
Authors: Fabian T. Faul | Laurent Cronier | Ali H. Alhulaymi | A. Douglas Stone | Philipp del Hougne
Source: Advanced Science (article, 2025-02-25)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Agile Free‐Form Signal Filtering and Routing with a Chaotic‐Cavity‐Backed Non‐Local Programmable Metasurface Filter synthesis is an inverse problem that is traditionally approached rationally by engineering the coupling between selected pairs of lumped resonators. The implicit restriction to spatially disjoint resonators strongly limits the design space, making it challenging to build extremely tunable filters. Here, agile free-form signal filtering and routing are demonstrated with an alternative purely-optimization-based approach leveraging a multi-parameter programmable system with many spatially overlapping modes. The approach is largely insensitive to system details other than the programmable system configuration. In the fabricated prototype, all ports and tunable meta-elements are strongly coupled via a quasi-2D chaotic cavity such that the meta-elements' configuration efficiently controls the transfer function between the ports. The all-metallic device enables low-loss and ultra-wideband (UWB) tunability (7.5-13.5 GHz) and guarantees signal-strength-independent linearity. First, theoretical predictions about reflectionless and transmissionless scattering modes (including transmissionless exceptional points) are experimentally confirmed. Second, these transfer function zeros are imposed at desired frequencies within an UWB range. Third, low-loss reflectionless programmable signal routing is achieved. Fourth, the trade-off between routing fidelity and bandwidth is investigated, achieving 20 dB discrimination over 10 MHz bandwidth. Fifth, UWB-tunable multi-band filtering is demonstrated that rejects (< -24 dB) or passes (≥ -1 dB) signals in specified bands whose centers, widths and number are reprogrammable. Chaotic ; Routing (electronic design automation) ; Agile software development ; SIGNAL (programming language) ; Computer science ; Electronic engineering ; Materials science ; Engineering ; Embedded system ; Artificial intelligence ; Software engineering Metamaterials and Metasurfaces Applications ; Advanced Antenna and Metasurface Technologies ; Antenna Design and Analysis
Authors: Reha Artan | Ayşegül Tepe
Source: Journal of Elasticity (article, 2025-05-30)
Field: Materials Science — Materials Chemistry

In-Plane Static Behavior of Nonlocal Curved Nanobeams Resting on an Elastic Foundation Determined Using the Initial Value Method Abstract Curved nanobeams are fundamental components of nanoscale systems, such as nano and microelectromechanical systems, where accurate modeling of small-scale effects is essential for achieving high precision. This study introduces a novel framework for analyzing the in-plane static behavior of curved nanobeams resting on an elastic foundation, particularly for small displacements. Nonlocal constitutive equations are derived based on nonlocal elasticity theory and solved using the initial value method and the approximate transfer matrix approach to address the challenges arising from the high degree of statical indeterminacy. A convergence analysis is conducted, showing that the proposed method provides a systematic and computationally efficient solution. A parametric analysis reveals that the nonlocal parameter, elastic foundation, and opening angle significantly influence the displacements in the tangential and normal directions, the rotation and bending moment in the binormal direction, the shear force in the normal direction, and the axial force in the tangential direction. These findings further elucidate the mechanics of curved nanostructures, contributing to the design and optimization of nanoscale devices. Foundation (evidence) ; Plane (geometry) ; Mathematical analysis ; Value (mathematics) ; Mathematics ; Physics ; Classical mechanics ; Mechanics ; Geometry ; Statistics Nonlocal and gradient elasticity in micro/nano structures ; Composite Structure Analysis and Optimization ; Numerical methods in engineering
Authors: Jia-Lan Chen | Xuechun Jiang | Feng Li | Jinze Zhu | Jianwen Zhao | Jin‐Xun Liu | Wei‐Xue Li
Source: Nature Communications (article, 2025-10-15)
Field: Materials Science — Materials Chemistry

Collectivity effect in cluster catalysis under operational conditions Subnanometer metal clusters are promising catalysts but are limited by structural heterogeneity and dynamics under operational conditions. Herein, we employ artificial intelligence-enhanced multiscale modeling, integrated with statistical analysis, to exhaustively explore the catalytic sites of cluster catalysts under reaction conditions. We discover that numerous sites across varying sizes, compositions, isomers, and locations collectively contribute to overall activity due to their high intrinsic activity and abundance. The collectivity of active sites, despite their distinct local environments, configurations and reaction mechanisms, arises from their high intrinsic activity and considerable population. Data-driven machine learning reveals that this collectivity is governed by the balance between local atomic coordination and the adsorption energy. Using CO oxidation on Cu/CeO<sub>2</sub> as an example, we validate the collective effect via agreement between computed mechanisms/kinetics and experimental data. This work provides insights into active sites in heterogeneous catalysis and highlights the potential of leveraging collective effects in cluster catalysis. Cluster (spacecraft) ; Catalysis ; Work (physics) ; Adsorption ; Heterogeneous catalysis ; Detailed balance Catalytic Processes in Materials Science ; Catalysis and Oxidation Reactions ; Machine Learning in Materials Science
Authors: Shuai Lv | Lyuzhang Peng | Wentiao Wu | Yufan Yao | Shizhe Jiao | Wei Hu
Source: Materials Genome Engineering Advances (article, 2025-05-27)
Field: Materials Science — Materials Chemistry

Bridging language models and computational materials science: A prompt‐driven framework for material property prediction Abstract Large language models (LLMs) have demonstrated effectiveness in interpreting complex data. However, they encounter challenges in specialized applications, such as predicting material properties, due to limited integration with domain‐specific knowledge. To overcome these challenges, we introduce MatAgent, an artificial intelligence (AI) agent that combines computational chemistry tools, such as first‐principles (FP) calculations, with the capabilities of LLMs to predict key properties of materials. By leveraging prompt engineering and advanced reasoning techniques, MatAgent integrates a series of tools and acquires domain‐specific knowledge in the field of material property prediction, enabling it to accurately predict the properties of materials without the need for predefined input structures. The experimental results indicate that MatAgent achieves a significant improvement in prediction accuracy and efficiency. As a novel approach that integrates LLMs with FP calculation tools, MatAgent highlights the potential of combining advanced computational techniques to enhance material property predictions, representing a significant advancement in computational materials science. Bridging (networking) ; Property (philosophy) ; Computer science ; Epistemology ; Philosophy Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Computational Drug Discovery Methods
Authors: Suyeon Bae | Mingyu Jeon | Hoi Ri Moon
Source: Chemical Communications (review, 2025-01-01)
Field: Chemistry — Inorganic Chemistry

Text mining in MOF research: from manual curation to large language model-based automation The rapid expansion of metal-organic framework (MOF) literature presents both a rich resource and a significant challenge for knowledge extraction. Text mining, which enables the conversion of unstructured scientific texts into structured, machine-readable data, has emerged as a key tool for accelerating data-driven research in the MOF domain. This review traces the development of text mining approaches in MOF research, from early manual curation and rule-based methods to recent breakthroughs powered by large language model (LLM)-based automation. We discuss the foundational role of natural language processing (NLP) and machine learning (ML) techniques such as named entity recognition and vector embedding models, followed by an in-depth analysis of LLM-based frameworks that enable flexible, scalable, and context-aware information extraction. Additionally, we introduce and compare their accuracy, and explore their diverse applications-including prediction of synthesizability, materials properties, and thermal stability. We conclude with a perspective on future directions for text mining in MOF research, including its integration into interactive graphical user interfaces, autonomous laboratories, multi-agent AI systems, and multi-modal LLM frameworks that can process textual, visual, and structural information in a unified way. This review aims to provide a foundational understanding for both experimental and computational researchers interested in adopting or advancing text mining methods in the MOF field. Automation ; Computer science ; Data curation ; Resource (disambiguation) ; Data science ; Metal-organic framework ; Information retrieval ; Chemistry ; Engineering Metal-Organic Frameworks: Synthesis and Applications ; Machine Learning in Materials Science
Authors: Obinna Onyebuchi Barah | Mauro David | Matthew Joseph
Source: Journal of Materials Science Composites (review, 2025-08-11)
Field: Materials Science — Surfaces, Coatings and Films

Atomic-scale characterization: a review of advances in microscopy, spectroscopy, and machine learning Atomic-scale characterization is pivotal in elucidating structure–property relationships across quantum materials, energy systems, and biological nanostructures. This review critically examines recent advances in high-resolution imaging, aberration-corrected TEM/STEM, cryo-EM, scanning probe microscopy (SPM), and helium ion microscopy (HIM), alongside spectroscopies such as electron energy loss spectroscopy (EELS), tip-enhanced Raman spectroscopy (TERS), and atom probe tomography (APT). Particular focus is placed on their integration with in-situ/operando environments and AI-driven workflows, enabling real-time, multimodal analysis at sub-ångström resolutions. We propose a unified framework combining machine learning, unsupervised clustering, and automated data pipelines to accelerate insight extraction and materials design. Case studies highlight this convergence: perovskite solar cells reached 25.7% efficiency through defect passivation guided by TEM; silicon–carbon anodes retained over 80% capacity across 1,000 cycles via nanostructure-informed optimization; cryo-EM resolved biomolecular assemblies below 2 Å with direct electron detection; and 4D-STEM enabled atomic-scale 3D reconstructions in cathodes with 0.3 nm precision. These tools have revealed critical structure–function linkages, such as lithium heterogeneity in nickel-manganese-cobalt (NMC) cathodes driving capacity fade, and PbI₂ segregation at perovskite grain boundaries impairing photovoltaic performance. Persistent challenges-resolution-dose tradeoffs, dataset reproducibility, and global disparities in instrumentation access are also assessed. Future directions include quantum-enhanced metrology and cloud-based remote experimentation. This review presents an integrated, forward-looking perspective on the fusion of atomic-scale metrology and autonomous experimentation, outlining a strategic roadmap to accelerate materials discovery while foregrounding sustainability, equity, and open-access principles often overlooked in prior literature. Not applicable. Characterization (materials science) ; Microscopy ; Spectroscopy ; Atomic units ; Materials science ; Scale (ratio) ; Nanotechnology ; Physics ; Optics ; Astronomy Electron and X-Ray Spectroscopy Techniques ; Electronic and Structural Properties of Oxides ; Advanced Materials Characterization Techniques
Authors: Runqiu Shen | Guihua Luo | An Su
Source: Processes (article, 2025-08-23)
Field: Materials Science — Materials Chemistry

Bayesian Optimization for Chemical Synthesis in the Era of Artificial Intelligence: Advances and Applications This review highlights recent advances in the application of Bayesian optimization to chemical synthesis. In the era of artificial intelligence, Bayesian optimization has emerged as a powerful machine learning approach that transforms reaction engineering by enabling efficient and cost-effective optimization of complex reaction systems. We begin with a concise overview of the theoretical foundations of Bayesian optimization, emphasizing key components such as Gaussian process-based surrogate models and acquisition functions that balance exploration and exploitation. Subsequently, we examine its practical applications across various chemical synthesis contexts, including reaction parameter tuning, catalyst screening, molecular design, synthetic route planning, self-optimizing systems, and autonomous laboratories. In addition, we discuss the integration of emerging techniques, such as noise-robust methods, multi-task learning, transfer learning, and multi-fidelity modeling, which enhance the versatility of Bayesian optimization in addressing the challenges and limitations inherent in chemical synthesis. Bayesian optimization ; Bayesian probability ; Artificial intelligence ; Computer science ; Machine learning Machine Learning in Materials Science ; Innovative Microfluidic and Catalytic Techniques Innovation ; Process Optimization and Integration
Authors: Yoel Zimmermann | Adib Bazgir | Alexander Al‐Feghali | Mehrad Ansari | Joshua D. Bocarsly | L. Catherine Brinson | Yuan Chiang | Defne Çırcı | Min–Hsueh Chiu | Nathan Daelman | Matthew L. Evans | Abhijeet Gangan | Janine George | Hassan Harb | Ghazal Khalighinejad | Sartaaj Takrim Khan | Sascha Klawohn | Magdalena Lederbauer | Soroush Mahjoubi | Bernadette Mohr | Seyed Mohamad Moosavi | Aakash Ashok Naik | Aleyna Beste Ozhan | Dieter Plessers | Aritra Roy | Fabian Schöppach | Philippe Schwaller | Carla Terboven | Katharina Ueltzen | Yue Wu | Shang Zhu | Jan Janßen | Calvin Li | Ian Foster | Ben Blaiszik
Source: Machine Learning Science and Technology (article, 2025-08-29)
Field: Materials Science — Materials Chemistry

32 examples of LLM applications in materials science and chemistry: towards automation, assistants, agents, and accelerated scientific discovery Large language models (LLMs) are reshaping many aspects of materials science and chemistry research, enabling advances in molecular property prediction, materials design, scientific automation, knowledge extraction, and more. Recent developments demonstrate that the latest class of models are able to integrate structured and unstructured data, assist in hypothesis generation, and streamline research workflows. To explore the frontier of LLM capabilities across the research lifecycle, we review applications of LLMs through 32 total projects developed during the second annual LLM hackathon for applications in materials science and chemistry, a global hybrid event. These projects spanned seven key research areas: (1) molecular and material property prediction, (2) molecular and material design, (3) automation and novel interfaces, (4) scientific communication and education, (5) research data management and automation, (6) hypothesis generation and evaluation, and (7) knowledge extraction and reasoning from the scientific literature. Collectively, these applications illustrate how LLMs serve as versatile predictive models, platforms for rapid prototyping of domain-specific tools, and much more. In particular, improvements in both open source and proprietary LLM performance through the addition of reasoning, additional training data, and new techniques have expanded effectiveness, particularly in low-data environments and interdisciplinary research. As LLMs continue to improve, their integration into scientific workflows presents both new opportunities and new challenges, requiring ongoing exploration, continued refinement, and further research to address reliability, interpretability, and reproducibility. Scientific discovery ; Automation ; Nanotechnology ; Data science ; Engineering ; Computer science ; Engineering ethics ; Chemistry ; Materials science ; Mechanical engineering ; Psychology ; Cognitive science Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides ; Advanced Materials Characterization Techniques
Authors: Hugo Christian Monroy-Ramírez | Jorge Alejandro Salto-Sevilla | Scarlet Arceo-Orozco | Fernando Caloca-Camarena | Roberto Flores-Peña | Edgar R. López-Mena | Marina Galicia‐Moreno | Juan Armendáriz‐Borunda
Source: Journal of Materials Science Materials in Engineering (article, 2025-05-26)
Field: Materials Science — Materials Chemistry

Cerium oxide nanoparticles: a promising nanotherapy approach for chronic degenerative diseases Abstract Oxidative stress is a process caused by excess-free radicals and reactive oxygen species (ROS), which contribute to the onset and progression of various chronic degenerative diseases. Consequently, interest has grown in studying natural and synthetic antioxidant compounds that restore normal ROS metabolism, alleviating the damage in affected tissues. Despite the above, several preclinical and clinical studies evaluating these therapies have shown some contradictory results since sometimes the use of these molecules has not shown the desired therapeutic efficacy, or in some cases, the use of these antioxidants has been related to the appearance of harmful effects due to their lack of specificity, to the associated adverse effects, in addition to the fact that some ROS are essential for normal physiological functions. On the other hand, nanotechnology is one of the most widely used technological advances in the drug industry. Targeted delivery of active ingredients combines knowledge of pharmacology, pharmaceutical development, polymer sciences, conjugate chemistry, and molecular biology to improve the pharmacokinetics of active ingredients, decreasing their toxicity, immunogenicity, and biorecognition. Cerium oxide nanoparticles (CeO 2 -NPs) have emerged as a novel therapeutic alternative with high antioxidant capacity, demonstrating beneficial effects in both in vivo and in vitro studies for treating chronic degenerative disorders. CeO 2 -NPs are only activated when ROS levels reach a harmful threshold, which protects healthy cells from unwanted effects. Their therapeutic potential has been demonstrated in multiple fields of medicine, including oncology, neurology, ophthalmology, cardiology, and hepatology. Therefore, this review addresses the potential applications of CeO 2 -NPs evaluated in animal and in vitro models of chronic degenerative diseases, which help visualize and position them as an effective and safe therapeutic alternative for treating this condition. Cerium oxide ; Nanoparticle ; Oxide ; Cerium ; Materials science ; Medicine ; Nanotechnology ; Metallurgy Advanced Nanomaterials in Catalysis ; Carbon and Quantum Dots Applications ; Nanoparticles: synthesis and applications
Authors: Zhongli Zhang | Zhensheng Wu | Shuai Zheng | Haitao Yang | Fuqiang Tian
Source: Polymer (article, 2025-01-08)
Field: Materials Science — Polymers and Plastics

Molecular simulation and experimental investigation of enhanced thermal and dielectric properties in mica-filled polyimide films Polyimide ; Mica ; Materials science ; Composite material ; Dielectric ; Thermal ; Polymer chemistry ; Optoelectronics ; Layer (electronics) ; Thermodynamics Synthesis and properties of polymers ; Silicone and Siloxane Chemistry ; Dielectric materials and actuators