jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Kalpana Gour
Source: Zenodo (CERN European Organization for Nuclear Research) (article, 2026-02-06)
Field: Materials Science — Materials Chemistry

AI for Scientific Discovery and Sustainability: A Humanoid Perspective Abstract Artificial intelligence is revolutionizing scientific discovery and sustainability efforts by accelerating research cycles and optimizing resource use. This paper explores these impacts through recent advancements, drawing on real-world examples to highlight AI’s transformative role. Keywords Artificial intelligence, scientific discovery, sustainability, generative AI, MatterGen, A-Lab, materials science, climate modeling, precision agriculture, edge computing, AI ethics Transformative learning ; Scientific discovery ; Perspective (graphical) ; Generative grammar ; Resource (disambiguation) ; Scientific modelling Machine Learning in Materials Science ; Innovation, Sustainability, Human-Machine Systems ; Alexander von Humboldt Studies
Authors: Zuhong Lin | Daoyuan Ren | Kai Ran | Jing Zhi Sun | Songlin Yu | Xuefeng Bai | Xiaotian Huang | Haiyang He | Pengxu Pan | Ying Fang | Zhanglin Li | Haipu Li | Jingjing Yao
Source: Transactions of Materials Research (article, 2026-01-01)
Field: Chemistry — Inorganic Chemistry

Reshaping MOFs synthesis conditions mining with a dynamic multi-agents framework of large language model Accurately identifying the synthesis conditions of metal-organic frameworks (MOFs) is essential for guiding experimental design, yet remains challenging because relevant information in the literature is often scattered, inconsistent, and difficult to interpret. We present MOFh6, a large-language-model-driven system that reads raw articles or crystal codes and converts them into standardized synthesis tables. It links related descriptions across paragraphs, unifies ligand abbreviations with full names, and outputs structured parameters ready for use. MOFh6 achieved 99% extraction accuracy, resolved 90.8% of abbreviation cases across five major publishers, and maintained a precision of 0.93 ± 0.01. Processing a full text takes 9.6s, locating synthesis descriptions 36 s, with 100 papers processed for 4.24$. By replacing static database lookups with real-time extraction, MOFh6 reshapes MOFs synthesis research, accelerating the conversion of literature knowledge into practical synthesis protocols, enabling scalable, data-driven materials discovery. • LLM-powered MOFh6 unifies fragmented synthesis knowledge into structured data. • Transforms MOF synthesis informatics with scalable and cost-efficient workflow. • End-to-end system supports literature mining, structure query, and MOFs visualization. • 7 × faster processing, 76% lower cost, advancing MOF synthesis information discover. Scalability ; Information extraction ; Informatics ; Raw data ; Program synthesis ; Software Metal-Organic Frameworks: Synthesis and Applications ; Machine Learning in Materials Science ; Inorganic Chemistry and Materials
Authors: Kalpana Gour
Source: Open MIND (article, 2026-02-06)
Field: Materials Science — Materials Chemistry

AI for Scientific Discovery and Sustainability: A Humanoid Perspective Abstract Artificial intelligence is revolutionizing scientific discovery and sustainability efforts by accelerating research cycles and optimizing resource use. This paper explores these impacts through recent advancements, drawing on real-world examples to highlight AI’s transformative role. Keywords Artificial intelligence, scientific discovery, sustainability, generative AI, MatterGen, A-Lab, materials science, climate modeling, precision agriculture, edge computing, AI ethics Transformative learning ; Scientific discovery ; Perspective (graphical) ; Generative grammar ; Resource (disambiguation) ; Scientific modelling Machine Learning in Materials Science ; Innovation, Sustainability, Human-Machine Systems ; Alexander von Humboldt Studies
Authors: Halidu Abdulai | Cristina Manresa-Yee | Hergys Rexha | Sebastien Lafond
Source: Machine Learning Science and Technology (article, 2026-03-18)
Field: Materials Science — Materials Chemistry

Zero-shot evidence-grounded extraction of blood–brain barrier nanoparticle design parameters with open-weight language models Abstract Data-driven materials design and discovery require high-quality, machine-readable data. However, curating data from unstructured text is a bottleneck. Advances in natural language processing–especially attention-based large language models–enable automated information extraction. Here, we present a zero-shot extraction–synthensis framework that coordinates a two-stage ensemble of smaller, open-weight models ( <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mrow> <mml:mtext>⩽</mml:mtext> </mml:mrow> </mml:math> 27B parameters) to extract blood–brain barrier (BBB) nanoparticle design parameters from the literature. The Stage-2 synthesis step enforces fidelity by requiring textual evidence for every extracted value. On a 50-article gold-standard set, the framework outperforms Claude Sonnet 4 on key parameters by F 1 -score: Zeta potential (96.2% vs 87.1%), Particle number (73.7% vs 54.3%), and Particle concentration (40.0% vs 18.6%). Applied at scale, we extract 28–32 BBB-relevant parameters per article across 526 articles. We publicly release the machine-readable dataset to support downstream modeling and benchmarking. Our approach demonstrates an accessible, reproducible path to accelerated data curation for AI-driven materials design. Language model ; Key (lock) ; Information extraction ; Fidelity ; Data modeling ; Extraction (chemistry) ; Particle (ecology) ; Path (computing) Machine Learning in Materials Science ; Gold and Silver Nanoparticles Synthesis and Applications ; Nanoparticles: synthesis and applications
Authors: Gaurav Tiwari | Ruchi Tiwari | Anand B. Mundada | Pooja Anand Mundada | Rakhee Maheshwari | Shashi Ravi Suman Rudrangi | Mahendar Porika | Radhika Tippani | Pankaj Sharma
Source: Journal of Biomaterials Science Polymer Edition (article, 2026-03-17)
Field: Materials Science — Materials Chemistry

Nanotoxicology meets smart polymers: advancing safety-by-design nanomaterials Smart polymers have played great role in enhancing nanomedical application areas in drug delivery, diagnosis and environment sensitive treatment systems. Nonetheless, their highly adaptive and structurally diverse nature leads to more concern toward their methods of action and their safety in the nanoscale. Thus, this review seeks to provide an informed account of how smart polymers can act as vectors of biomedical advancement as well as drivers of biological interactions that have not had a favorable outcome. Using a PRISMA 2020-informed reporting principle, this work presents a narrative and critical synthesis of <i>in vitro</i> and <i>in vivo</i> investigating the nanotoxicology of smart polymer-based nanosystems. Accordingly, no formal meta-analysis was undertaken. Oxidative stress, immune response, and organ injury are observed as the critical issues, whereas the factors, including the surface modification of nanoparticles and biodegradation, are the significant predictors of safer nanoparticles. In addition, it highlights high-throughput screening, artificial intelligence based modeling and quantitative or physiologically based simulations as some of the promising approaches to predictive toxicology to support safe-by-design strategies. These findings show that there is a need for polymer type regulation and ethical measures to promote safe use of smart polymers in future health care sectors. Nanotoxicology ; Nanomaterials ; Nanoparticle ; Defence industry ; Nanomedicine Nanoparticles: synthesis and applications ; Dendrimers and Hyperbranched Polymers ; Effects and risks of endocrine disrupting chemicals
Authors: Tao Cheng | Chunchun Li | Ziwei Ye | Jian Ye | Steven E. J. Bell | Yikai Xu
Source: Materials Horizons (article, 2026-01-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Self-assembled monolayers for bio-analytical SERS applications: from single to mixed component SAMs Self-assembled monolayers (SAMs) represent an elegant and powerful strategy for achieving precise control over the surface chemistry of plasmonic nanomaterials. This capability has significantly advanced both fundamental and applied research in surface-enhanced Raman spectroscopy (SERS), a technique whose performance is critically dependent on the chemical nature of the enhancing substrate. While single-component SAMs have served as a cornerstone in foundational SERS studies, their functionality is inherently limited, rendering them inadequate for complex real-world applications. Mixed-component SAMs offer a compelling solution by enabling the integration of multiple functional ligands on a single plasmonic interface, thereby overcoming these limitations. Nevertheless, the routine and reliable deployment of mixed component SAMs in SERS faces fundamental challenges, particularly in their controlled synthesis and precise characterization. As a result, despite their widespread empirical use, the field of mixed-component SAMs remains underdeveloped and insufficiently reviewed. This article aims to address this gap by first outlining the fundamentals of single-component SAMs, then delving into the synthesis strategies and characterization techniques specific to mixed component SAMs. We emphasize the unique role of SERS as a powerful <i>in situ</i> tool for probing key parameters of mixed monolayers. Using biological applications as a primary example, we illustrate how the multifunctionality afforded by mixed component SAMs leads to enhanced sensitivity, selectivity, stability, and accuracy in quantitative SERS analysis. Finally, we discuss the persisting challenges in the rational design of mixed component SAMs, highlighting the need for further mechanistic research and the integration of artificial intelligence. Component (thermodynamics) ; Plasmon ; Monolayer ; Characterization (materials science) ; Self-assembled monolayer ; Rendering (computer graphics) ; Raman spectroscopy Gold and Silver Nanoparticles Synthesis and Applications ; Plasmonic and Surface Plasmon Research ; Advanced biosensing and bioanalysis techniques
Authors: John Zima | Evan Jensen | Denise Drane | Roel Tempelaar
Source: ChemRxiv (article, 2026-01-15)
Field: Materials Science — Materials Chemistry

Assignment Promoting Use of Generative AI as an Active Learning Tool in General Chemistry The value of generative artificial intelligence (AI) for teaching and learning is currently hotly debated. Concerns regarding the accuracy of information produced by generative AI as well as student over-reliance on this tool coexist with excitement about tailored opportunities that AI may provide for educational purposes. Student ability to responsibly utilize AI is growing into a key competency that we expect can be co-taught with domain course contents. Driven by this idea, we developed an assignment for first-year undergraduate students in a general chemistry course with the aim to promote the use of AI as an active learning tool. By having students judge AI responses to advanced chemistry-related questions, this assignment is designed to promote the assessment of information accuracy and combat over-reliance. Here, we detail the assignment and report the aggregate student responses that we received while implementing this assignment in our course. Generative grammar ; Active learning (machine learning) ; Domain (mathematical analysis) ; Key (lock) ; Generative model ; Problem-based learning Machine Learning in Materials Science ; Various Chemistry Research Topics ; Innovative Teaching Methods
Authors: Cheng Wang | Mengru Zhang | Tong Zhang | Haitao Du | Hongmin Xia | Renwei Guan | Fengdan Guo | X X Li | Mingming Wang | Xiuwen Jia | Ping Wang
Source: Trends in Food Science & Technology (article, 2026-01-14)
Field: Materials Science — Biomaterials

Marine biomaterials for advanced food packaging: From sourcing to intelligent functional design and commercial prospects Commercialization ; Active packaging ; Food packaging ; Consistency (knowledge bases) ; Packaging engineering ; Quality (philosophy) ; Sustainability Nanocomposite Films for Food Packaging ; Polymer composites and self-healing ; biodegradable polymer synthesis and properties
Authors: Xueming Sun | Jiahong Wei | Wenkai Cao | Weihua Li
Source: Progress in Organic Coatings (article, 2026-01-06)
Field: Materials Science — Materials Chemistry

Metal-organic frameworks-based smart anti-corrosion composite coatings: Recent advances, mechanisms, and future perspectives Composite number ; Corrosion ; Coating ; Dual (grammatical number) ; Sustainability ; Smart material Corrosion Behavior and Inhibition ; Metal-Organic Frameworks: Synthesis and Applications ; Polymer composites and self-healing
Authors: Jiani Man | Guojun Weng | Jian-jun Li | JW Zhao | Yubo Guo | Haoyu Zhang | Jian Zhu
Source: SSRN Electronic Journal (preprint, 2026-01-01)
Field: Materials Science — Materials Chemistry

Au@AgPt Nanozymes with Synergistic Plasmonic-Catalytic Sites for Multimodal Large Model-Empowered Dual-Mode Diagnostics of Helicobacter pylori in Saliva Colorimetry ; Nanoprobe ; Detection limit ; Helicobacter pylori ; Plasmon ; Raman scattering Advanced Nanomaterials in Catalysis ; Nanoplatforms for cancer theranostics ; Gold and Silver Nanoparticles Synthesis and Applications
Authors: Bhuva Akash and Manjali Rana
Source: Zenodo (CERN European Organization for Nuclear Research) (article, 2026-01-15)
Field: Chemistry — Analytical Chemistry

Magnetic Analytical Extraction for Detection of Antibiotic Residues in Food Samples Antibiotic residues in food of animal origin present a persistent threat to consumer safety and contribute to the emergence of antimicrobial resistance. Conventional sample-preparation techniques such as liquid–liquid extraction (LLE) and solid-phase extraction (SPE) are effective but are often time-consuming, solvent-intensive, and difficult to automate. Magnetic analytical extraction (MAE) — an umbrella term that includes magnetic solid-phase extraction (MSPE), magnetic solid-phase microextraction (MSPME), magnetic stir-bar sorptive extraction (MSBSE), magnetic matrix solid-phase dispersion (M-MSPD), and dispersive liquid–liquid microextraction (DLLME) variants — offers rapid, selective, and greener alternatives. MAE uses functionalized magnetic nanoparticles (MNPs) or magnetic matrices to selectively capture analytes from complex matrices; analytes are recovered by a small volume of eluent and the magnetic sorbent is separated with an external magnet, eliminating filtration or centrifugation. This review synthesizes the principles, sorbent chemistries, analytical coupling, and applications of MAE for antibiotic residue monitoring in milk, meat and eggs, discusses current limitations (matrix effects, selectivity, and multi-class analysis) and outlines plausible directions for future development including molecularly imprinted polymers (MIPs), magnetic ionic liquids, automation, and AI-guided method optimization. Magnetic nanoparticles ; Extraction (chemistry) ; Analyte ; Sorbent ; Magnetic separation ; Molecularly imprinted polymer ; Complex matrix Analytical chemistry methods development ; Pharmaceutical and Antibiotic Environmental Impacts ; Pesticide Residue Analysis and Safety
Authors: Bing He | Kaixuan Wang | Zhou Jiang | Yueyu Zhang | Maxim Avdeev | Siqi Shi
Source: National Science Review (article, 2026-02-13)
Field: Materials Science — Materials Chemistry

Towards a sustainable high-quality materials data ecosystem: frameworks and strategies Abstract High-quality data has increasingly been recognized as a critical catalyst in advancing artificial intelligence-driven materials discovery and design. However, the current data generation and management systems in materials science are often deficient, resulting in an abundance of low-quality data characterized by heterogeneous formats, incomplete entries, and semantic inconsistencies. Such fragmented data landscape limits direct access to reliable datasets, making it inevitable to allocate substantial resources toward data consolidation and governing existing data. Here, we propose a comprehensive quality-controlled framework encompassing all three principal stages of the research lifecycle—acquisition, management, and utilization. By establishing stage-specific requirements, materials data quality can be concurrently optimized and enhanced throughout the materials research and development process. Furthermore, we propose five strategic initiatives to operationalize this framework, including an integrated computational-experimental ‘data factory’ for data acquisition and an automated toolkit for data quality assessment. This work aims to embed quality-governed production directly into scientific inquiry, fostering a sustainable and robust materials data ecosystem. Operationalization ; Consolidation (business) ; Data quality ; Principal (computer security) ; Data management ; Work (physics) ; Quality (philosophy) ; Data integration Machine Learning in Materials Science ; Scientific Computing and Data Management ; Research Data Management Practices
Authors: Zhongshan Chen | Jianen Li | Qiao Ma | Xishi Tai | Jielong Lei | Xiangke Wang
Source: Chinese Chemical Letters (article, 2026-01-01)
Field: Materials Science — Materials Chemistry

Covalent-organic frameworks: An intelligent platform for photocatalytic uranium reduction and separation Uranium ; Photocatalysis ; Valence (chemistry) ; Key (lock) ; Charge carrier Covalent Organic Framework Applications ; Radioactive element chemistry and processing ; Advanced Photocatalysis Techniques
Authors: Kerim Karabacak
Source: Applied Energy (article, 2026-02-03)
Field: Materials Science — Electronic, Optical and Magnetic Materials

A comprehensive review of Supercapacitor integration into wind turbines Wind power ; Turbine ; Supercapacitor ; Offshore wind power ; Energy storage ; Cost of electricity by source ; System integration ; Renewable energy Supercapacitor Materials and Fabrication ; Electric and Hybrid Vehicle Technologies ; Advanced battery technologies research
Authors: B. Poornima | A. Harika Devi | V. Madhu Sree | I. Praseeda | V. Nityasree
Source: Asian Journal of Pharmaceutical Analysis (article, 2026-01-27)
Field: Chemistry — Spectroscopy

Beyond Chromatograms: Green Analytical Frontiers in the Nutraceutical Era The global nutraceutical sector is undergoing a paradigm shift from conventional, resource-intensive analytical practices to sustainable, green approaches that align with environmental, economic, and ethical imperatives. This review examines the application of Green Analytical Chemistry (GAC) principles in nutraceutical analysis, focusing on innovative extraction methods, miniaturized and solvent-free techniques, AI-assisted workflows, and blockchain-enabled transparency. It critically examines the limitations of classical chromatographic techniques (HPLC, GC-MS, TLC) in terms of solvent consumption, waste generation, and energy demand, while showcasing greener alternatives such as FTIR, NIR, Raman spectroscopy, ultrasound-assisted extraction, supercritical fluid extraction, and natural deep eutectic solvents (NaDES). Analytical challenges, ranging from matrix complexity, bioavailability issues, and compound stability to regulatory validation and scalability, are addressed through advancements in chemometrics, portable devices, and real-time monitoring platforms. The review emphasizes the growing demand for clean-label, traceable nutraceuticals and the role of digital innovations, including AI-driven optimization and digital twin simulations, in reducing waste and trial costs. Cross-sector collaboration between academia, industry, and regulatory bodies (EFSA, FDA, FSSAI) is advocated to mainstream sustainable practices, establish ISO-certified green methodologies, and harmonize global standards. By moving beyond chromatograms to environmentally conscious, technologically advanced analytics, the nutraceutical industry can ensure product efficacy, consumer safety, and long-term ecological balance. Nutraceutical ; Renewable energy ; Sustainable development ; Deep eutectic solvent ; Supercritical fluid extraction ; Green growth Analytical Chemistry and Chromatography ; Chemistry and Chemical Engineering ; Chromatography in Natural Products
Authors: Daichi Egami | Hiroyuki Yoshida
Source: Journal of Electron Spectroscopy and Related Phenomena (article, 2026-01-01)
Field: Materials Science — Materials Chemistry

Automated determination of ionization energy and electron affinity from UPS and LEIPS spectra based on piecewise polynomial fitting Precise determination of ionization energy (IE) and electron affinity (EA) is essential for elucidating the electronic structure of semiconductors. In the solid state, these quantities are most accurately obtained from the spectral onsets of ultraviolet photoelectron spectroscopy (UPS) and low-energy inverse photoelectron spectroscopy (LEIPS), respectively. Conventional onset determination relies on manual fitting, which is time-consuming and requires experience. For future automated measurements, automated data analysis will be indispensable. Here, we propose an automated analysis approach that combines piecewise polynomial fitting with tangent intersection to extract IE and EA from UPS and LEIPS spectra. Applied to various organic semiconductors and polymer samples, the automated results exhibit excellent agreement with manual analysis, achieving accuracies of ±0.002 eV for UPS and ±0.02 eV for LEIPS—well below the commonly assumed uncertainties and instrumental resolutions of these techniques. The method demonstrates robust performance even for spectra with low signal-to-noise ratios or unclear onset, with no outliers exceeding 0.1 eV. This framework offers a reliable solution for automated spectral analysis, paving the way for large-scale database development and data-driven materials discovery. • Automated method extracts IE and EA from UPS and LEIPS spectra accurately. • Piecewise polynomial fitting with tangent intersection ensures robust onset detection. • Achieves ±0.002 eV for UPS and ±0.02 eV for LEIPS, closely matching manual analysis. • Works reliably for spectra with low S/N ratios or gradual onsets without outliers. • Enables integration with automated systems for large-scale materials informatics. Piecewise ; Polynomial ; Spectral line ; Intersection (aeronautics) ; Outlier ; Tangent ; Ionization ; Ionization energy Machine Learning in Materials Science ; Electron and X-Ray Spectroscopy Techniques ; Organic Electronics and Photovoltaics
Authors: Shuhei Naganuma | Jiro Kitagawa
Source: arXiv (Cornell University) (preprint, 2026-03-24)
Field: Materials Science — Materials Chemistry

Experimental investigation of magnetic properties of MnFeCo$_{4}$Si$_{2}$ discovered by GNoME AI-driven inorganic materials research has garnered significant attention due to its ability to reduce the time, labor, and cost associated with experiments. An AI model known as GNoME, recently developed by Google DeepMind, is particularly fascinating because it is integrated with the Materials Project open database. The experimental verification of compounds identified by GNoME is a crucial process for advancing AI-driven materials research. Here, we focus on the magnetic compound MnFeCo$_{4}$Si$_{2}$ (Materials ID: mp-3203253), which possesses a layered-like structure. Consistent with the GNoME prediction, MnFeCo$_{4}$Si$_{2}$ crystallizes in a rhombohedral structure with a single-phase nature. We have characterized its magnetic properties and determined that MnFeCo$_{4}$Si$_{2}$ is a soft ferromagnet with a Curie temperature of 1039 K. Curie temperature ; Ferromagnetism ; Focus (optics) ; Curie ; Magnet ; Process (computing) Machine Learning in Materials Science ; Heusler alloys: electronic and magnetic properties ; Chemical and Physical Properties of Materials