jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Gongxizi Ren
Source:  (article, 2025-11-28)
Field: Materials Science — Materials Chemistry

Simulating Spectroscopy Measuring Processes and Results Using Large Language Models Large language models (LLMs) have recently demonstrated remarkable potential in participating scientific research; however, their reliance on statistical learning limits the ability to perform physically consistent reasoning. To address this challenge, an LLM-Spec framework is presented in this research as an end-to-end tool for experimental planning and result prediction in materials characterization using spectroscopy techniques. LLM-Spec is an LLM-based framework that integrates GPT-4 with established physics, chemistry, and spectroscopy databases and first-principles density functional theory (DFT) simulations. The framework decomposes spectroscopy measurements into four embedded modules, including technique selection, sample preparation, experimental workflow design, and DFT-based spectral prediction. Each module is grounded in structured, physics-informed reasoning logic. By coupling semantic reasoning with physically validated databases and computational models, LLM-Spec transforms qualitative scientific intent into quantitatively verifiable experimental plans and simulation outputs. This integration ensures logical coherence across the spectroscopy measuring process and establishes a scalable paradigm for applying LLMs to data-driven, physically interpretable research in materials science. Workflow ; Coherence (philosophical gambling strategy) ; Process (computing) ; Spectroscopy ; Characterization (materials science) ; Qualitative reasoning ; Equivalence (formal languages) ; Verifiable secret sharing Machine Learning in Materials Science ; Scientific Computing and Data Management ; Software System Performance and Reliability
Authors: Frederic Felsen | Christian Künkel | Karsten Reuter | Christoph Scheurer
Source: Chemical Engineering Journal Advances (article, 2025-04-15)
Field: Materials Science — Materials Chemistry

Adaptive designs for the exploration of reaction kinetic phase transitions In heterogeneous catalysis, changes of the reaction mechanism or of the catalyst’s surface structure and composition often lead to drastic changes in the effective kinetics over a small range of reaction conditions. The topology of kinetic phase diagrams that summarize this effective kinetics is therefore typically characterized by extended regimes of smooth kinetic behavior separated by narrow such phase transitions. This discontinuous topology prevents the efficient exploration of kinetic phase diagrams with traditional design-of-experiment approaches that assume a smoothly varying measurement function over the entire investigated domain. Bridging towards modern statistical learning and optimization, we here propose an adaptive design algorithm that tackles this issue in a data-efficient way. A support vector machine classification learns and iteratively refines the a priori unknown positions of the phase transitions by optimally designing new data points, i.e. the reaction conditions at which the next kinetic measurements are to be taken. Using a variety of analytic toy systems, we illustrate the potential of this approach in analyzing experimental design spaces composed of multiple distinct regimes. Finally, we reconstruct the kinetic phase diagram for the CO oxidation over a RuO 2 (110) model catalyst from a minimum number of simulated kinetic data from a microkinetic model. • Data driven approach for robust kinetic modeling. • Algorithm automatically determines functional form of model to avoid bias. • Effective kinetic models do not require mechanistic understanding. • Automatic discovery of kinetic regimes. Kinetic energy ; Phase (matter) ; Computer science ; Statistical physics ; Physics ; Classical mechanics ; Quantum mechanics Crystallization and Solubility Studies ; Machine Learning in Materials Science ; Optimal Experimental Design Methods
Authors: Jong Hyeok Han | Boseong Heo | Myeong Jin Ju | Youngjin Kim | Joon Ha Chang | Hee‐Jae Jeon
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Materials Science — Surfaces, Coatings and Films

Quantitative Image-Analysis Framework for Precise Discrimination of Cation Mixing in High-Nickel Ncm Cathodes Nickel ; Mixing (physics) ; Cathode ; Image (mathematics) ; Computer science ; Pattern recognition (psychology) ; Materials science ; Chemistry ; Artificial intelligence ; Physics ; Metallurgy ; Physical chemistry Electron and X-Ray Spectroscopy Techniques ; Non-Destructive Testing Techniques ; Welding Techniques and Residual Stresses
Authors: Mariah Batool (21636061) | Sara Pedram (21636064) | Jasna Jankovic (6899759)
Source: Figshare (article, 2025-07-01)
Field: Materials Science — Materials Chemistry

AdvancingMicrostructural Analysis: Practical andHigh-Throughput AI-Based Image Processing and Automated Data AnalysisTechniques for Quantification of Structural Parameters for MulticomponentCatalyst Systems With microstructural characterization tools now achieving nanometric and even subatomic resolution, scientists can visualize structures once thought impossible. However, mere visualization underutilizes the vast potential of imaging data. To fully harness this information, researchers are advancing image processing and data analysis techniques that enable precise quantification and automated postcharacterization analysis, unlocking deeper insights into complex material systems. As a rapidly evolving field of research, this Perspective highlights developed practical image and data analysis techniques powered by computer vision (CV) and artificial intelligence (AI) algorithms. These advanced methods offer the capability of unraveling complex material interactions in multicomponent systems, revealing intriguing structural features, and enabling precise quantification. Utilizing data from scanning electron microscopy (SEM), transmission electron microscopy (TEM), and scanning transmission electron microscopy with energy dispersive spectroscopy (STEM/EDS), these methods extract key microstructural features that are critical parameters for optimizing material performance. Tested on diverse electrochemical systems, these techniques demonstrate flexibility in their applications. Their strengths and limitations are discussed, showcasing their potential to drive automated and comprehensive materials characterization and advance the field of material science. Characterization (materials science) ; Image processing ; Flexibility (engineering) ; Field (mathematics) ; Visualization ; Automation ; Data processing Machine Learning in Materials Science ; Advanced Electron Microscopy Techniques and Applications ; Electron and X-Ray Spectroscopy Techniques
Authors: Lovepreet Singh | Aparajita Sharma | Jagdish Verma
Source: World Journal of Microbiology and Biotechnology (review, 2025-11-01)
Field: Materials Science — Materials Chemistry

Emerging green-synthesized nanozymes for microbial applications: toward sustainable antimicrobial technologies Sustainability ; Transformative learning ; CLARITY ; Environmental remediation ; Human health Advanced Nanomaterials in Catalysis ; Nanoplatforms for cancer theranostics ; Nanoparticles: synthesis and applications
Authors: Qili Shen | G. M. Liu | B. W. Hou | Na Qiang | Yang Xiaolong | Ruijian Xu | Zhu Liu | Yanxiong Fang | Xin Huang | Zhe Lü
Source:  (article, 2025-03-28)
Field: Chemistry — Organic Chemistry

Development of organic pressure-sensitive adhesive under the background of artificial intelligence Silicone pressure-sensitive adhesives (SiPSAs) have demonstrated broad application potential in electronics and other fields due to their exceptional properties, such as temperature resistance, weather resistance, and chemical corrosion resistance. However, traditional SiPSAs are predominantly thermally cured solvent-based products, which not only exhibit slow curing rates but also release harmful solvents like toluene during application, causing environmental pollution. To address this issue, this study Under the background of artificial intelligence. synthesized thiol-functionalized MTQ silicone resins (MTQ-SH) with various M/(T+Q) ratios using γ-mercaptopropyltrimethoxysilane, tetraethyl orthosilicate, and hexamethyldisiloxane as raw materials. These resins were then photo-crosslinked with vinyl-terminated silicone oil to prepare UV-curable silicone PSAs (UV-SiPSAs). The results revealed that the optimal precursor ratio for UV-SiPSA preparation was M/T/Q = 1.00/0.21/1.58. When the photoinitiator TPO-L was added at 1 wt.% and the molar ratio of thiol groups to vinyl end groups was 1.2:1, the resulting UV-SiPSA exhibited an initial adhesion strength capable of holding a 22# steel ball and a holding time of 230 minutes at 25℃. This UV-curable PSA shows promising potential for replacing traditional thermally cured silicone PSAs in the protective film industry. Pressure sensitive ; Adhesive ; Artificial intelligence ; Computer science ; Materials science ; Nanotechnology Advanced Polymer Synthesis and Characterization ; Photopolymerization techniques and applications ; Synthesis and properties of polymers
Authors: Nasim Kharazminezhad | Ramez Pourahmad
Source:  (article, 2025-11-19)
Field: Materials Science — Polymers and Plastics

Electrochemical Biosensors Based on Polyaniline Nanostructures: Advances, Challenges, and Outlook for Smart Systems Polyaniline (PANI), as one of the most promising conductive polymers, has attracted significant interest in electrochemical biosensors due to its electroactivity, stability, and biocompatibility. Recent advances in nanotechnology, especially the development of nanofibers via electrospinning, have enabled the design of substrates with high surface-to-volume ratios, improving sensitivity and lowering detection limits (Zhang et al., 2017; Popov et al., 2021). Despite these advantages, long-term electrochemical stability and reproducible fabrication remain critical challenges (Gungordu Er et al., 2022). This review critically analyzes the recent progress of PANI-based nanostructured biosensors, focusing on their morphology-performance relationship. Comparative tables and updated references (2019-2024) highlight analytical performance. Emerging solutions, such as AI-assisted self-calibration, are also discussed to provide an outlook toward next-generation smart biosensing systems. Polyaniline ; Biosensor ; Conductive polymer ; Smart system ; Nanofiber Conducting polymers and applications ; Electrochemical sensors and biosensors ; Supercapacitor Materials and Fabrication
Authors: Junaid Jami | Nitish Bhagat | Amrita Bhattacharya
Source: ArXiv.org (preprint, 2025-07-02)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Data-driven high-throughput search for the accelerated discovery of rare-earth-free permanent magnets An integrated data-driven approach combined with a high-throughput framework based on first-principles calculations was used to discover novel rare-earth-free permanent magnets, focusing on binary alloys. Compounds were screened systematically based on their elemental composition, structure, stability, and magnetization. Density functional theory (DFT) calculations were performed on the selected candidates to evaluate their magnetocrystalline anisotropy energy (MAE) and Curie temperature (Tc), resulting in the identification of ten promising materials. A thorough literature review was done to assess reports of prior existence, which confirmed the novelty of ZnFe and Fe8N. Their ferromagnetic ground state was re-established through DFT, and structural stability was confirmed via negative formation enthalpies, phonon spectra, and elastic criteria. Tetragonal ZnFe and Fe8N exhibit high saturation magnetization (&gt;1 T), large anisotropy constants (&gt;0.5 MJ/m^3), and high Tc (&gt;1200 K). Their magnetic hardness parameters (kappa = 0.85 for ZnFe and 0.70 for Fe8N) further support their potential as gap magnets. These findings highlight the efficacy of our high-throughput screening, which may serve as a theoretical blueprint for the experimental realization of these materials. Magnetocrystalline anisotropy ; Density functional theory ; Curie temperature ; Ferromagnetism ; Magnet ; Tetragonal crystal system ; Ground state ; Magnetization Magnetic Properties of Alloys ; Heusler alloys: electronic and magnetic properties ; Boron and Carbon Nanomaterials Research
Authors: Michel Lehmann | Andreas Möckel
Source: IET conference proceedings. (article, 2025-08-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

AI-supported method for determining the magnetisation of small electrical steel samples Electrical steel forms the basis of technical magnetic circuits by conducting and amplifying magnetic flux. Depending on the specific application, isotropic or anisotropic behaviour may be desired. The material properties are influenced by processing methods such as punching or laser cutting. Measuring equipment for quantifying the influence of machining has not yet been standardised. This leads to difficulties when analysing measurement data obtained during the processing of sheet metal strips with a width of between 0.3 and 5 mm. Magnetic flux leakage can distort the magnetisation curve (BH curve) determined by measurement. This paper presents a possibility for a novel method that enables the analysis of small, machined electrical steel samples in an equivalent magnetic circuit with pre-wound coils using artificial intelligence (AI). Magnetization ; Materials science ; Electrical steel ; Metallurgy ; Physics ; Magnetic field Magnetic Properties and Applications ; Non-Destructive Testing Techniques ; Microstructure and Mechanical Properties of Steels
Authors: Yi-Heng Zhang (12010203) | Shi-Hui Ding (21243900) | Yi-Ming Wang (18749) | Wen Chen (30046) | Dong Zhu (538074) | Shi-Jun Ge (718127) | Peng Chen (6514) | Yan-Qing Lu (3981119)
Source: Figshare (article, 2025-05-05)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Optical BinaryOperator Based on Thermally ControllableChiral Superstructures By harnessing multiple dimensions of light to implement mathematical functions, structured optical materials introduce a twist in the paradigm of optical informatics, shifting from “displaying with light” to “computing with light”. One vital subset of mathematical operators is binary operators, whose output depends on two inputs, such as Boolean logic. Herein, we propose and demonstrate an optical binary operator based on thermally controllable chiral nanostructures. By engineering the anisotropic and chiral materials, the output is determined by the interplay between two inputs; furthermore, the accompanying Pancharatnam–Berry phase unlocks additional computing functionalities. The customized modulation of the orbital angular momentum and intensity distribution of light enables binary operations on integers and images. The tunable operating spectrum spans an ultrawide range over 1600 nm from the visible to the near-infrared region. This study reveals new opportunities for soft matter and may facilitate diverse applications in machine vision and optical artificial intelligence. Binary number ; Operator (biology) ; Modulation (music) ; Optical anisotropy ; Twist ; Bitwise operation ; Range (aeronautics) ; Phase (matter) ; Spectrum (functional analysis) Metamaterials and Metasurfaces Applications ; Neural Networks and Reservoir Computing ; Photonic Crystals and Applications
Authors: Zhuobin Ma | Yeda Lian | Hao Li | Leike Yang | Lanjie Niu | Hang Shen
Source: Research Square (preprint, 2025-12-11)
Field: Materials Science — Materials Chemistry

Analysis of Dynamic Mechanical Effects of Single-Crystal Silicon Anisotropy on MEMS Fuze Safety and Arming Systems under High Strain Rates Fuze ; Microelectromechanical systems ; Stress (linguistics) ; Anisotropy ; Split-Hopkinson pressure bar ; Finite element method ; Shock (circulatory) ; Reliability (semiconductor) ; Silicon High-Velocity Impact and Material Behavior ; Energetic Materials and Combustion ; Structural Response to Dynamic Loads
Authors: Victor V. Volkov | Carole C. Perry | Riccardo Chelli
Source: Preprints.org (preprint, 2025-03-03)
Field: Chemistry — Inorganic Chemistry

Zinc and Oxo-Vanadium Complexes upon Binding to Phosphatase Enzymes: Structure, Electronics and Implications Oxovanadium and zinc complexes are reported as insulin-mimetics. They inhibit several proteins including enzymes which belong to the same class of membrane sensitive phosphatases, similar in terms of general architecture and biochemistry of the active site. Borrowing from this summary, we explore the structural and electronic properties of representative oxovanadium and zinc complexes as computed in isolation and upon binding to PTEN and PTP1B phosphatases. Using crystallographic data and quantum chemistry calculations, we investigate how bonding nature and structural flexibility of the studied inhibitors affects efficiency of their binding to the active sites of the enzymes: albeit different, the two active sites represent evolutionary variant choices of the same type of biochemistry of phosphatases. As a result of our studies, we address optical responses which can be suitable for diagnostics and discuss engineering of AI assisted protein embedding to alter electronic states of metal centres which may be beneficial for biomedical and quantum information applications within the bio-spintronics of tomorrow. Vanadium ; Zinc ; Enzyme ; Chemistry ; Phosphatase ; Materials science ; Biochemistry ; Inorganic chemistry ; Organic chemistry Vanadium and Halogenation Chemistry ; Polyoxometalates: Synthesis and Applications ; Porphyrin and Phthalocyanine Chemistry
Authors: Yuhan Chen | Ying Wang | Liang Yuan | Ke Ma | Qiyuan Qian | Yao Zhang | Wenbin Jin | Hai Wan | Shuohan Huang | Yuwei Chen | Peng Wei | Yanping Wang | Yong He | Yumin Xia
Source: International Journal of Biological Macromolecules (article, 2025-11-15)
Field: Materials Science — Biomaterials

Robust and highly conductive silk fibroin fibers boosted by poly(ionic liquid)-mediated nanocarbon dispersion for multimodal sensors Fibroin ; Biocompatibility ; Electrical conductor ; SILK ; Conductivity ; Scanning electron microscope ; Bending ; Dispersion (optics) Silk-based biomaterials and applications ; Advanced Sensor and Energy Harvesting Materials ; Electrospun Nanofibers in Biomedical Applications
Authors: Diego J. Monterrubio-Chanca | Guido Falk von Rudorff
Source: ArXiv.org (preprint, 2025-08-28)
Field: Chemistry — Physical and Theoretical Chemistry

Representative Random Sampling of Chemical Space The overwhelming majority of molecules remains unexplored. This is mostly due to the sheer number of them, which prohibits any enumeration of chemical space, the set of all such molecules. In practice, only subsets of chemical space are considered, but those subsets exhibit substantial bias, prohibiting data-driven characterization of chemical space itself. In this work, we provide a method produce unbiased representative random samples of chemical space without enumeration of constituent molecules and to estimate the number of molecules in any custom chemical space. The approach is applicable to molecules which can be represented as graph and runs efficiently even for molecules of 30 atoms. We use it to estimate the representativeness of current databases with respect to their underlying chemical space and to establish a necessary criterion for a lower bound of database sizes to be representative of an underlying chemical space. Chemical space ; Representativeness heuristic ; Enumeration ; Space (punctuation) ; Graph ; Set (abstract data type) ; Sampling (signal processing) ; Chemical process History and advancements in chemistry ; Geochemistry and Geologic Mapping
Authors: Gajanan M. Hingangavkar
Source: Sensing and Imaging (article, 2025-11-24)
Field: Materials Science — Materials Chemistry

Advances in Two-Dimensional Nanomaterials for Intelligent Biosensing and Environmental Monitoring Biosensor ; Software deployment ; Environmental monitoring ; Wearable computer ; Key (lock) ; Internet of Things ; Intelligent sensor MXene and MAX Phase Materials ; 2D Materials and Applications ; Graphene research and applications
Authors: Jianwei Zhai | Zhou Yu | Jun Hu
Source: Journal of Manufacturing Processes (article, 2025-05-07)
Field: Materials Science — Materials Chemistry

The importance of patterning design in laser-induced graphene for advanced applications Materials science ; Graphene ; Nanotechnology ; Laser ; Systems engineering ; Engineering physics ; Optics ; Engineering Graphene research and applications ; Electrohydrodynamics and Fluid Dynamics ; Plasma Diagnostics and Applications
Authors: Kang-Il Choi | HyunJong Kim | YoHan Choi | SuYoung Chi
Source:  (article, 2025-10-14)
Field: Materials Science — Materials Chemistry

A Deep Dive into KoMaP for Accelerating AI-Based Material Development Existing material platforms mostly focus on collecting material data to accelerate material development. In this paper, we present Korea Material AI Platform (KoMaP), a material data platform for accelerating data-driven material development. This platform harmonizes material data and tools for generating trained models used by researchers based on the material data. Finally, it integrates material data into the material AI model, allowing us to accelerate new material development. Focus (optics) ; Material handling ; Material properties ; Material efficiency ; Materials processing ; Natural materials Machine Learning in Materials Science ; Material Selection and Properties ; Modular Robots and Swarm Intelligence
Authors: Kanishka Chhibber | Ritika Puri | Divya Jain
Source: Drug Delivery Letters (article, 2025-12-30)
Field: Materials Science — Materials Chemistry

Synthesis and Characterization of Zinc Oxide Nanoparticles for Biomedical Applications: Techniques and Future Prospects Abstract: Zinc oxide nanoparticles (ZnO NPs) have attracted significant interest for biomedical applications due to their exceptional physicochemical properties, biocompatibility, and multifunctional capabilities. The review delves into the synthesis, characterization, and biomedical applicability of ZnO NPs, with emphasis on drug delivery, bioimaging, wound healing, antibacterial, antidiabetic, anti-inflammatory, and anticancer applications. Different synthesis methods, such as co-precipitation, sol-gel, hydrothermal, microemulsion, and microwave-assisted methods, are reviewed with emphasis on their impact on the morphology and functionality of NPs. The selective toxicity of ZnO NPs toward cancer cells, drug bioavailability enhancement, and antimicrobial activities make them valuable candidates for advanced therapeutic applications. Despite these benefits, toxicity issues, biocompatibility, and the limitations of mass production need to be overcome for clinical translation. The future research should emphasize the optimization of nanoparticle formulations, enhance safety profiles, and incorporate artificial intelligence for precision medicine applications. As nanotechnology continues to advance, ZnO NPs have enormous potential to transform contemporary medicine by providing novel solutions for targeted drug delivery and disease management. Drug delivery ; Nanoparticle ; Characterization (materials science) ; Drug ; Nanomedicine ; Targeted drug delivery ; Precision medicine ZnO doping and properties ; Nanoparticles: synthesis and applications ; Magnesium Oxide Properties and Applications