jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Md. Fahad Jubayer | Mahmud Hasan | Md Khurram Monir Rabby | Md Mozammel Hoque | Masudur Rahman | Md Abdur Rashid Sarker
Source: ChemRxiv (article, 2026-03-02)
Field: Chemistry — Analytical Chemistry

Real-Time Detection of Starch Gelatinization Using YOLO-based Deep Learning Models This research proposes a novel, vision-based approach for real-time detection and confirmation of starch gelatinization using the state-of-the-art YOLO (You Only Look Once) deep learning models to overcome the limitations of traditional methods, which are manual and subjective for industrial control. The primary contribution of this work is twofold: it introduces a first-of-its-kind image-based framework for starch gelatinization detection, and it conclusively demonstrates that YOLO models provide a highly accurate and practical solution for automated, non-contact process control. A custom dataset was developed by capturing temporal images of a heated potato starch solution, with each frame annotated as "Non-Gelatinized" or "Gelatinized". Four YOLO architectures (v8, v9, v11, v12) were trained and evaluated on the developed dataset. All models demonstrated exceptional and nearly identical performance, achieving maximum precision, recall, and F1-scores (1.0), alongside a high mean Average Precision Deep learning ; Starch gelatinization ; Starch ; Process (computing) ; Frame (networking) ; Pattern recognition (psychology) Spectroscopy and Chemometric Analyses ; Food composition and properties ; Potato Plant Research
Authors: Jinming Fan | Chao Qian | Wilhelm T. S. Huck | William E. Robinson | Shaodong Zhou
Source: ArXiv.org (article, 2026-03-03)
Field: Materials Science — Materials Chemistry

ChemFlow:A Hierarchical Neural Network for Multiscale Representation Learning in Chemical Mixtures Accurate prediction of the physicochemical properties of molecular mixtures using graph neural networks remains a significant challenge, as it requires simultaneous embedding of intramolecular interactions while accounting for mixture composition (i.e., concentrations and ratios). Existing approaches are ill-equipped to emulate realistic mixture environments, where densely coupled interactions propagate across hierarchical levels - from atoms and functional groups to entire molecules - and where cross-level information exchange is continuously modulated by composition. To bridge the gap between isolated molecules and realistic chemical environments, we present ChemFlow, a novel hierarchical framework that integrates atomic, functional group, and molecular-level features, facilitating information flow across these levels to predict the behavior of complex chemical mixtures. ChemFlow employs an atomic-level feature fusion module, Chem-embed, to generate context-aware atomic representations influenced by the mixture state and atomic characteristics. Next, bidirectional group-to-molecule and molecule-to-group attention mechanisms enable ChemFlow to capture functional group interactions both within and across molecules in the mixture. By dynamically adjusting representations based on concentration and composition, ChemFlow excels at predicting concentration-dependent properties and significantly outperforms state-of-the-art models in both concentration-sensitive and concentration-independent systems. Extensive experiments demonstrate ChemFlow's superior accuracy and efficiency in modeling complex chemical mixtures. Embedding ; Artificial neural network ; Representation (politics) ; Molecule ; Group (periodic table) ; Feature (linguistics) ; Chemical process ; Intramolecular force ; Complex system Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Graph Neural Networks
Authors: Ann Zabludoff | Chen-Yu Chuang | Parker Thomas Johnson | Yichen Liu | Brina Bianca Martinez | Neev Shah | Lucille Steffes | Gabriel Glen Weible
Source: ArXiv.org (article, 2026-03-27)
Field: Materials Science — Materials Chemistry

Can LLMs Produce Original Astronomy Research in a Semester? A Graduate Class Experiment We discuss the results of using large language models (LLMs) to conduct original scientific research in an unfamiliar subject area during the Fall 2025 semester. Students in a graduate astronomy and astrophysics course were asked to test whether LLMs could help them complete research tasks faster and at a level of detail and accuracy required for scientific publication. Most students employed LLMs for a total of 5-10 hours. While all students completed a draft paper on an unsolved problem related to galaxies by semester's end, their impressions of the models' value varied. About half thought that the models saved them time. Many noted that LLMs failed to provide appropriately detailed insights or steps to addressing open, niche questions over a several-month timeframe. The LLMs also frequently (about 20% of the time) returned false citations, links, or summaries of papers. The models struggled with generating complex functional code, accessing online packages or Application Programming Interfaces (APIs), and retrieving astronomical datasets from existing archives. In writing code and in chats, the LLMs made implicit, overly simplifying assumptions and often doubled down even after being corrected. Given the rapid pace of LLM development, new models may soon address at least some of these issues and thus significantly enhance research productivity. Yet students expressed concerns about how LLM use might dampen creativity and reflection during the research process. To improve learning experiences in future semesters, the class will first discuss LLM best practices and limitations. Students will be encouraged to explore free online resources for tips for generative model applications and will decide for themselves whether to use LLMs for their research project. This white paper was not written using LLMs. Class (philosophy) ; Pace ; Test (biology) ; Value (mathematics) ; Creativity ; Graduate students Machine Learning in Materials Science ; Computational and Text Analysis Methods ; Professional Masters Programs Analysis
Authors: Harshit Sethi | Markus Junttila | Orlando J. Silveira | Adam S. Foster
Source: arXiv (Cornell University) (article, 2026-01-01)
Field: Materials Science — Materials Chemistry

Automated structure discovery for Tip Enhanced Raman Spectroscopy Tip-Enhanced Raman Spectroscopy (TERS) provides nanoscale chemical fingerprints alongside high-resolution topographic mapping of molecules, offering a powerful tool for materials discovery. However, TERS image datasets are challenging to interpret and typically demand time-consuming, computationally intensive quantum-chemistry calculations. To overcome this problem, we present an encoder-decoder model trained and evaluated on simulated TERS images of planar molecules, enabling direct prediction of molecular structures from spectral simulated data with high accuracy. Our approach demonstrates the feasibility of automating molecular structure identification from TERS images, bypassing traditional manual analysis. These findings provide a foundation for extending machine learning methods to experimental TERS datasets, potentially accelerating molecular discovery by integrating nanoscale spectroscopy with automated computational analysis. Nanoscopic scale ; Raman spectroscopy ; Planar ; Identification (biology) ; Characterization (materials science) ; Spectroscopy ; Pattern recognition (psychology) Machine Learning in Materials Science ; Spectroscopy Techniques in Biomedical and Chemical Research ; Gold and Silver Nanoparticles Synthesis and Applications
Authors: Zehao Chen | Gongxun Li | Tianxiang Ai | Yifei Li | Zixuan Huang | Wang Zhou | Fuzhen Zhuang | Xianglong Liu | Jianxin Li | Deqing Wang | Yikun Ban
Source: arXiv (Cornell University) (article, 2026-02-09)
Field: Materials Science — Materials Chemistry

Weak-Driven Learning: How Weak Agents make Strong Agents Stronger As post-training optimization becomes central to improving large language models, we observe a persistent saturation bottleneck: once models grow highly confident, further training yields diminishing returns. While existing methods continue to reinforce target predictions, we find that informative supervision signals remain latent in models' own historical weak states. Motivated by this observation, we propose WMSS (Weak Agents Can Make Strong Agents Stronger), a post-training paradigm that leverages weak checkpoints to guide continued optimization. By identifying recoverable learning gaps via entropy dynamics and reinforcing them through compensatory learning, WMSS enables strong agents to improve beyond conventional post-training saturation. Experiments on mathematical reasoning and code generation datasets show that agents trained with our approach achieve effective performance improvements, while incurring zero additional inference cost. Inference ; Entropy (arrow of time) ; Code (set theory) ; Principle of maximum entropy ; Language model Machine Learning in Materials Science ; Machine Learning and Data Classification ; Topic Modeling
Authors: Hafiz Muhammad Adil
Source: Preprints.org (preprint, 2026-03-31)
Field: Materials Science — Biomaterials

Development of Cellulose Nanofiber Based Nanopapers as Sustainable Alternatives to Plastic Derived Industrial Materials Petroleum products have led to challenges for the environment on a global scale, such as pollution from microplastics, persistent waste in the environment that is not biodegradable, and an increasing carbon footprint due to plastic production. In quest of sustainable alternatives, environmentally friendly cellulose nanofiber (CNF) based nanopapers have gained potential as biodegradable materials to replace most of plastic derived industrial products. (Cellulose nanofibers derived from renewable biomass that possesses small dimensions which exhibit excellent mechanical properties, low density, high optical transparency, and very good barrier properties against gases and oils.) Advantages like these render CNF nanopapers promising for flexible electronics, packaging materials, coatings, filtration membranes and sustainable structural materials. This study aims to provide a conceptual framework for the development of cellulose nanofiber (CNF) based nanopapers as substitutes for common plastic materials by investigating their performance in industrial applications. This framework combines biomass extraction techniques, nanofibrillation processing, fabrication of nanopaper and material performance validation. Numerical simulations reveal that CNF nanopapers offer mechanical and barrier performance approaching synthetic polymer films with substantial decrease in environmental footprint. The findings demonstrate that cellulose based nanopapers provide a scalable route for sustainable material innovation in industry. Bio nanomaterials hold great promise for contributing to the sustainability of circular manufacturing systems as they can address environmental sustainability challenges linked with plastic based materials. Cellulose ; Nanofiber ; Nanocellulose ; Environmentally friendly ; Carbon footprint ; Sustainability ; Plastic pollution ; Environmental pollution ; Nanomaterials Advanced Cellulose Research Studies ; Electrospun Nanofibers in Biomedical Applications ; Natural Fiber Reinforced Composites
Authors: Yongqi Jin | Jun-Jie Wang | Fanjie Xu | Xiaohong Ji | Zhifeng Gao | Linfeng Zhang | Guolin Ke | Rong Zhu | Weinan E
Source: Nature Communications (article, 2026-04-02)
Field: Materials Science — Materials Chemistry

NMR-Solver: automated structure elucidation via large-scale spectral matching and physics-guided fragment optimization Nuclear Magnetic Resonance (NMR) spectroscopy is one of the most powerful and widely used tools for molecular structure elucidation in organic chemistry. However, the interpretation of NMR spectra to determine unknown molecular structures remains a labor-intensive and expertise-dependent process, particularly for complex or novel compounds. Although recent methods have been proposed for molecular structure elucidation, they often underperform in real-world applications due to inherent algorithmic limitations and limited high-quality data. Here, we present NMR-Solver, a practical and interpretable framework for the automated determination of small organic molecule structures from <sup>1</sup>H and <sup>13</sup>C NMR spectra. Our method introduces an automated framework for molecular structure elucidation, integrating large-scale spectral matching with physics-guided molecular optimization that exploits atomic-level structure-spectrum relationships in NMR. We evaluate NMR-Solver on simulated benchmarks, curated experimental data from the literature, and real-world experiments, demonstrating its strong generalization, robustness, and practical utility in real-life scenarios. By integrating computational NMR analysis, deep learning, and interpretable chemical reasoning into a unified system, it facilitates scalable, automated, and chemically meaningful molecular structure elucidation, establishing a generalizable paradigm for solving inverse problems in molecular science. Fragment (logic) ; Matching (statistics) ; Molecular dynamics ; Nuclear magnetic resonance spectroscopy ; Inverse ; Molecule ; Molecular recognition ; Molecular model Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Molecular spectroscopy and chirality
Authors: Michael J. Sell | Joan A. López | Karen T. Bayona | Angeles Ivón Rodríguez-Villarreal | Josep Farré-Lladós | Jasmina Casals-Terré
Source: Microchemical Journal (article, 2026-04-01)
Field: Chemistry — Analytical Chemistry

The future of cortisol detection: Molecularly imprinted polymers enhanced by artificial intelligence Molecularly imprinted polymer ; Polymer ; Applications of artificial intelligence ; Artificial kidney Analytical chemistry methods development ; Polymer Surface Interaction Studies ; Hydrogels: synthesis, properties, applications
Authors: Jason Daza  Collier
Source: Universitat de Girona Digital Repository (Universitat de Girona) (article, 2026-01-14)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Soft magnetic alloys customized for spring magnets ENG- Spring magnets have gained popularity as an economical and sustainable alternative to rare-earth permanent magnets. A spring magnet is a composite material that achieves magnetic exchange coupling between soft and hard magnetic particles. Therefore, optimizing these magnetic particles is vital to the successful manufacture of a spring magnet. This thesis is focused on optimizing two Fe-based soft magnetic alloys, obtained by mechanical alloying, that could act like a magnetic reinforcement for a hard magnetic alloy to obtain a spring magnet. The optimization of these alloys is achieved by favouring their nanocrystallinity and reducing the average particle size. The results clearly show a successful optimization for longer milling times and an even further optimization with the addition of a process control agent (PCA) during milling. The addition of a PCA also favours the formation of additional metastable tetragonal phases. These tetragonal phases, which are magnetically harder, alter the direct correlation between the soft magnetic response of the alloy with their optimization. However, a successful correlation between the optimization of the alloys and their magnetic response is obtained. The final part of the study focuses on composites. The first composite corresponds to a first attempt at a spring magnet between one of the Fe-based soft magnetic alloys of the thesis and a commercial iron oxide acting as the harder magnetic phase. Full exchange coupling between particles is not achieved. The second composite is an additive manufactured polymer-metallic material. The results show how a structurally functional polymer-metallic composite can be manufactured and could be an alternative to the more traditional sintering techniques that require high temperature and pressure Magnet ; Spring (device) ; Alloy ; Composite number ; Coupling (piping) ; Magnetic separation ; Solenoid ; Magnetic nanoparticles ; Magnetic field ; Magnetic shape-memory alloy Magnetic Properties of Alloys ; Metallic Glasses and Amorphous Alloys ; Magnetic Properties and Applications
Authors: John D. Ponis | Kenna Ashen | S. Chakraborty | George Agbeworvi | Michelle A. Smeaton | Chengdong Wang | A.M. Jessel | Douglas H. Fabini | Fanni Jurànyi | D. L. Quintero-Castro | Nick A. Shepelin | Dariusz Jakub Gawryluk | Katherine Jungjohann | Shruti Hariyani | Xiaofeng Qian | Sarbajit Banerjee
Source: Journal of the American Chemical Society (article, 2026-02-19)
Field: Materials Science — Polymers and Plastics

Alkali-Metal Interlocking of 2D V <sub>4</sub> O <sub>10</sub> Sheets Defines Discretized Interlayer Shear Relationships Low-dimensional materials manifest structural anisotropy, quantum confinement, and tightly bound excitonic states, which make them attractive building blocks that can be assembled within three-dimensional laterally stitched heterostructures, stacked van der Waals solids, and complex moiré superlattices. Ion intercalation in the galleries between layered materials provides a means of modifying interlayer separation and coupling, but it is also known to drive the shearing of the layers. In this article, we explore the distinct ligand coordination environments afforded by vanadyl oxygens of singular [V<sub>4</sub>O<sub>10</sub>] sheets and examine how the size, polarizability, and stoichiometry of Group I cations sandwiched between such layers determine the interlocking of the sheets in stacked structures. Based on the topochemical insertion of alkali-metal ions into the layered λ-V<sub>2</sub>O<sub>5</sub>, we identify seven types of guest ion coordination sites discretized into four distinct regimes of interlayer shear in units of half octahedral widths. The coordination preferences of intercalated cations govern how they interlock 2D [V<sub>4</sub>O<sub>10</sub>] sheets and engender specific shear conformations. We present evidence that static and dynamic disorder in guest ion arrangement modulate the magnetic structure of the intercalated compounds based on electrostatic polarization, localization of charge and spin density, and lattice distortion. The results illustrate the use of topochemical ion insertion to modulate stacking relationships and magnetic transition characteristics. Stacking ; Ion ; Intercalation (chemistry) ; van der Waals force ; Shearing (physics) ; Shear (geology) ; Ferromagnetism ; Octahedron Transition Metal Oxide Nanomaterials ; Physics of Superconductivity and Magnetism ; Advanced Condensed Matter Physics
Authors: Lyudmila Slobodkina | Roksana Muzyka | Marcin Sajdak | Szymon Sobek | Sebastian Werle
Source: Reviews in Chemical Engineering (article, 2026-03-16)
Field: Materials Science — Polymers and Plastics

Recent trends in textile waste recycling: an overview Abstract The global textile industry is an important economic driver, but at the same time it generates serious environmental challenges. These challenges are twofold: the vast amounts of textile waste generated each year and the substantial climate impacts caused mainly during the production phase. According to the European Environment Agency, about 80 % of the total climate impact of textiles derives from production, 14 % from the use phase (washing, drying, ironing), 3 % from distribution and retail, and 3 % from end-of-life stage, which includes collection, sorting, recycling, incineration, and landfilling. By 2030, global apparel (clothing + footwear) consumption is projected to rise from around 70 million tons today to approximately 105 million tons. Despite this growth, less than half of textile items are collected for reuse or recycling after use, and only a very small fraction – under 1 % – are recycled into new garments. This gap is primarily due to technical barriers: the processing of mixed fibers, chemical finishing and additives, as well as the lack of scalable and cost-competitive processing systems. This review examines current work on textile waste processing, taking into account technological, regulatory, economic, and environmental factors. It also identifies innovations and strategies for developing a circular economy. Reuse ; Textile ; Textile industry ; Clothing ; Work (physics) ; Consumption (sociology) ; Global-warming potential ; Production (economics) ; Cleaner production Textile materials and evaluations ; Dyeing and Modifying Textile Fibers ; Sustainable Supply Chain Management
Authors: Marek Florkowski
Source: Energies (article, 2026-01-20)
Field: Materials Science — Materials Chemistry

Challenges and Trends in High-Voltage Insulation of Electric Vehicle Devices There are observed unprecedented dynamics in transportation electrification—especially in electric vehicles (even being tested as autonomous units in some regions). The expected improvements in charging and driving distances strive toward higher power levels of passenger cars, public transportation, and trucks, thus leading to elevations of on-board voltage levels. It is expected that the kilovolt level will be crossed soon, thus implying testing at a few kV. To achieve efficient power conversion while maintaining high-power density, new classes of wide-band semiconductors are being implemented; however, fast-switching and ultra-short rise times may result in faster electrical insulation deterioration. The challenges and trends in the development of the high-voltage insulation of various EV components are analyzed. Insulation performance evaluation criteria are discussed, including partial discharges and monitoring approaches. In this context, the development of the transportation segment’s electrification is closely connected with high-voltage insulation problems. Electrification ; Voltage ; Power (physics) ; Electric vehicle ; Insulation system ; Electric power High voltage insulation and dielectric phenomena ; Advanced Battery Technologies Research ; Magnetic Field Sensors Techniques
Authors: Jasper De Landsheere | Maximilian P.-P. Kovar | Konstantin Mark | Leon Ganser | Leonard Galustian | Johannes Karwounopoulos | Charlotte Gerhaher | Esther Heid
Source: ChemRxiv (article, 2026-01-09)
Field: Materials Science — Materials Chemistry

Toward On-the-Fly Prediction of Reaction Energetics for High-Throughput Screening Machine learning is increasingly used to predict reaction properties such as barrier heights, reaction energies, rates, or yields, as well as the underlying molecular geometries, including transition state structures. While such predictions have the potential to provide mechanistic insight for high-impact applications such as synthesis planning and reaction optimization, the field remains at an early stage of development. This Perspective discusses and critically assesses the current state-of-the art in reaction property prediction, highlighting the key limitations related to data availability and quality, molecular and transformation representations, and machine learning architectures used in both predictive and generative models. A special focus is given on current challenges and on possible paths forward toward efficient and accurate machine learning models for on-the-fly prediction of reaction energetics. Field (mathematics) ; Property (philosophy) ; Generative grammar ; Term (time) ; Perspective (graphical) ; Key (lock) Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Catalysis and Oxidation Reactions