jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Shuangqi Gan | Kai Fan | Shanshan Liang | Tingting Lei | Junwei Zhou | Yongqin Ju | Qiang Xiang
Source: High Energy Chemistry (article, 2025-11-11)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Investigation on the Performance of Carbon Nanotube-Doped Polypyrrole Flexible Supercapacitor Electrodes via Gamma-Ray Irradiation Modification Flexible supercapacitors have surged in popularity across various sectors, including artificial intelligence (AI), wearable technology, and renewable energy. In this work, carbon nanotubes (CNTs) submerged in an organic solution of pyrrole (Py) monomer were exposed to gamma-ray irradiation to create carbon nanotubes doped polypyrrole (CNTs/PPy) flexible supercapacitor electrode materials. Scanning electron microscopy (SEM) was used to characterize the surface microstructure of electrode materials before and after irradiation, and the formation and adhesion of new substances on the material surface and CNTs was found, which was hypothesized that gamma-ray irradiation initiated the polymerization reaction of Py monomers and the production of polypyrrole (PPy). Additional examination of the electrode materials using Fourier transform infrared absorption spectroscopy (FTIR) and X-ray photoelectron spectroscopy (XPS) verified the successful advancement of the polymerization reaction and the formation of PPy conductive polymer. The prepared CNTs (5 mg)/PPy electrode materials exhibited excellent electrochemical properties, with a specific capacitance of 201.6 F g–1. The constant current charge-discharge (GCD) and alternating current impedance spectroscopy (EIS) experiments of electrode materials confirmed that the loading of CNTs effectively improved the conductivity and electrochemical stability of the material, reduced the resistivity, and demonstrated a positive effect on improving the electrochemical performance of the material. However, it was also observed that an overabundance of CNTs compromised the mechanical properties and flexibility of the material. Overall, our findings provide valuable insights and a benchmark for developing flexible supercapacitor electrode materials with enhanced electrochemical performance. Polypyrrole ; Supercapacitor ; Carbon nanotube ; Electrode ; Dielectric spectroscopy ; Conductive polymer ; Polymerization ; Electrochemistry Supercapacitor Materials and Fabrication ; Conducting polymers and applications ; Fiber-reinforced polymer composites
Authors: Daichi Egami | Hiroyuki Yoshida
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Materials Science — Materials Chemistry

Automated Determination of Ionization Energy and Electron Affinity from UPS and LEIPS Spectra Based on Piecewise Polynomial Fitting Polynomial ; Spectral line ; Piecewise ; Outlier ; Ionization energy ; Intersection (aeronautics) ; Ionization ; Electron affinity (data page) ; Energy (signal processing) Machine Learning in Materials Science ; Organic Electronics and Photovoltaics ; Electron and X-Ray Spectroscopy Techniques
Authors: Mikael Kuwahara (21009882) | Yoshiki Hasukawa (20456262) | Fernando Garcia-Escobar (14835006) | Satoshi Maeda (1247163) | Lauren Takahashi (3107373) | Keisuke Takahashi (1409992)
Source: Figshare (article, 2025-04-07)
Field: Materials Science — Materials Chemistry

Development of anOpen-Source 3D-Printed MaterialSynthesis Robot FLUID: Hardware and Software Blueprints for AccessibleAutomation in Materials Science The automation of materials design has emerged as a key focus in the field of materials science. The advent of materials informatics marked an important step toward this goal, utilizing data-driven approaches to accelerate the discovery and optimization of materials. However, the adoption of robotic systems for automated synthesis has been relatively gradual, influenced by the technical challenges and complexity of implementation. Here, the development of FLUID, an open-source, 3D-printed robotic platform for material synthesis, is presented. FLUID provides a comprehensive blueprint for both hardware and software, enabling accessible and customizable automation for a broad range of researchers. The system’s performance is experimentally validated through the materials synthesis, demonstrating its capability and flexibility. This approach aims to democratize automation in material synthesis, providing researchers with a practical, cost-effective solution to accelerate innovation in materials science. Automation ; Blueprint ; Robot ; Field (mathematics) ; Software ; Laboratory automation ; Focus (optics) Machine Learning in Materials Science ; Additive Manufacturing and 3D Printing Technologies ; Modular Robots and Swarm Intelligence
Authors: Hooman Pourbala | Fateme Peyvand | Sepehr Izadi | Shahin Jafari Ashtiani | Dorsan Rabbanian | Niki Pouyanfar | Kamran Banan | Mohammad‐Ali Shahbazi | Mehrdad Faizi | Fatemeh Ghorbani‐Bidkorbeh
Source: Journal of Drug Delivery Science and Technology (article, 2025-10-30)
Field: Chemistry — Analytical Chemistry

Exploring the applications of molecularly imprinted polymers in drug delivery to the nervous system Drug delivery ; Biocompatibility ; Molecularly imprinted polymer ; Bridge (graph theory) ; Targeted drug delivery ; Rational design ; Clinical trial Analytical chemistry methods development ; Hydrogels: synthesis, properties, applications ; Innovative Microfluidic and Catalytic Techniques Innovation
Authors: Joseph W. Abbott | Carlos Mera Acosta | Alaa Akkoush | Alberto Ambrosetti | Viktor Atalla | A. Bagrets | Jörg Behler | Daniel Berger | Björn Bieniek | Jonas Björk | Volker Blum | Saeed Bohloul | Connor L. Box | Nicholas J. Boyer | Danilo S. Brambila | Gabriel A. Bramley | Kyle R. Bryenton | María Camarasa-Gómez | Christian Carbogno | Fabio Caruso | Sucismita Chutia | Michele Ceriotti | Gábor Csányi | William Dawson | Francisco A. Delesma | Fabio Della Sala | B. Delley | Robert A. DiStasio | Maria Dragoumi | Sander Driessen | Marc Dvorak | Simon Erker | Ferdinand Evers | Eduardo Fabiano | Matthew R. Farrow | Florian Fiebig | Jakob Filser | Lucas Foppa | Lukas Gallandi | Alberto Garcia | Ralf Gehrke | Simiam Ghan | Luca M. Ghiringhelli | Mark Glass | Stefan Goedecker | Dorothea Golze | Matthias Gramzow | James A. Green | Andrea Grisafi | Andreas Grüneis | Jan Günzl | Stefan Gutzeit | S.J. Hall | Felix Hanke | Ville Havu | Xing He | Joscha Hekele | Olle Hellman | Uthpala Herath | Jan Hermann | Daniel Hernangómez‐Pérez | Oliver T. Hofmann | Johannes Hoja | Simon B. Hollweger | Lukas Hörmann | Ben Hourahine | Wei Bin How | William Huhn | Marcel Hülsberg | Timo Jacob | Sara Panahian Jand | Hong Jiang | Erin R. Johnson | Werner Jürgens | J. Matthias Kahk | Yosuke Kanai | Kisung Kang | Petr Karpov | Elisabeth Keller | Roman Kempt | Danish Khan | Matthias Kick | Benedikt Klein | Jan Kloppenburg | A. Knoll | Florian Knoop | Franz Knuth | S. Kocher | Jannis Kockläuner | Sebastian Kokott | Thomas Körzdörfer | Hagen-Henrik Kowalski | Peter Kratzer | Pavel Kůs | Raul Laasner | Bruno Lang | Björn Lange | Marcel F. Langer | Ask Hjorth Larsen | Hermann Lederer
Source: JuSER Publikationsportal (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Roadmap on Advancements of the FHI-aims Software Package Electronic-structure theory is the foundation of the description of materials including multiscale modeling of their properties and functions. Obviously, without sufficient accuracy at the base, reliable predictions are unlikely at any level that follows. The software package FHI-aims has proven to be a game changer for accurate free-energy calculations because of its scalability, numerical precision, and its efficient handling of density functional theory (DFT) with hybrid functionals and van der Waals interactions. It treats molecules, clusters, and extended systems (solids and liquids) on an equal footing. Besides DFT, FHI-aims also includes quantum-chemistry methods, descriptions for excited states and vibrations, and calculations of various types of transport. Recent advancements address the integration of FHI-aims into an increasing number of workflows and various artificial intelligence (AI) methods. This Roadmap describes the state-of-the-art of FHI-aims and advancements that are currently ongoing or planned. Software ; Workflow ; Software package ; Foundation (evidence) ; System integration ; R package Machine Learning in Materials Science ; Advanced Physical and Chemical Molecular Interactions ; Advanced Chemical Physics Studies
Authors: Li Z. | Wang X | Guo Z | Zeng K | Sundarrajan S | Li X | Ramakrishna S
Source: Newcastle University ePrints (Newcastle Univesity) (review, 2025-01-01)
Field: Materials Science — Biomaterials

Process engineering and manufacturing technologies for structural control and multifunctionality in electrospun nanofibres © 2025 The Author(s). Published by Informa UK Limited, trading as Taylor &amp; Francis Group.Electrospinning has evolved into a mature yet continually expanding technique for assembling fibrous architectures with dimensions spanning the micro – to nanometre range. Over the past two decades, process engineering and manufacturing technologies, ranging from solution formulation and field control to collector design, novel spinning modes, and integration with 4D printing have greatly extended its scope, enabling high precision in regulating fibre diameter, alignment, architecture, and ultimately multifunctionality. This work provides a coherent synthesis of these developments, tracing the interplay between materials chemistry, process physics, and structural outcomes. We examine established and emerging strategies for integrating electrospinning with additive manufacturing and data-driven process optimisation, thereby opening new avenues for multifunctional materials. Applications are considered across a spectrum that includes biomedical scaffolds, energy devices, environmental protection systems, and responsive textiles, with emphasis on how processing decisions shape structural and functional performance. Finally, we identify persistent bottlenecks in scalability, reproducibility, and sustainability, and outline prospective directions in which green chemistry, intelligent control systems, and hybrid manufacturing are expected to converge, positioning electrospun nanofibres–with enhanced structural control and multifunctionality–as a cornerstone of next-generation engineered materials. Process (computing) ; Spinning ; 3D printing ; Process control ; Electrospinning ; Tracing ; Manufacturing process ; Field (mathematics) ; Work in process Electrospun Nanofibers in Biomedical Applications ; Advanced Sensor and Energy Harvesting Materials ; Electrohydrodynamics and Fluid Dynamics
Authors: Alexis Komarov
Source: Digital Library NAES of Ukraine (National Academy of Educational Sciences of Ukraine) (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Розділ VIІ. Прискорення перевірки наукових гіпотез за допомогою багатомодельного мислення на основі штучного інтелекту: тематичні дослідження в оптиці This study explores the transformative role of artificial intelligence (AI) tools in accelerating scientific hypothesis validation, focusing on the iterative use of reasoning and deep research large language models (LLMs). We classify LLMs into single-step, multi-step reasoning, and web-integrated deep research variants, demonstrating that ensembles of independent models enhance accuracy probabilistically from 8-27% for individual reasoning models to 48-62% for collective inference, when addressing complex physical queries. To illustrate the approach of collective AI inference, we examine case studies of diamond Bragg mirror reflectivity for keV-scale X-rays and metal vapor lasing for high-boiling-temperature metals. By iteratively prompting six LLMs with tailored queries and peer-reviewed data, we derive optimized theoretical results and experimental setups. The results underscore the ability of LLMs to accelerate scientific hypothesis testing, identify theoretical limits, and design experimental configurations while also highlighting the importance of verifying AI outputs, confronting AI with facts and follow-up questions, and accounting for AI model correlations. This framework pioneers a paradigm shift in interdisciplinary research, merging AI-driven reasoning with domain-specific expertise to resolve ambiguities in cutting-edge material science and photonics. Transformative learning ; Visual reasoning ; Skepticism ; Deep learning ; Reflectivity ; Scientific reasoning ; Artificial neural network Machine Learning in Materials Science ; Explainable Artificial Intelligence (XAI) ; Advanced Graph Neural Networks
Authors: Volker Haag | Kilian Dremel | Tim Lukas Lewandrowski | Anne Reisenbach | Jannik Stebani | Simon Zabler | Valentina Theresia Zemke | Gerald Koch
Source: OpenAgrar (article, 2025-01-01)
Field: Chemistry — Organic Chemistry

Identification of wood and charcoal with 3D-microscopy, volumetric imaging of µCT-measurements and combined machine learning: a review In an age of increasing surveillance and control of markets and trade flows, reliable methods for species and origin identification of commercial timbers and other woody plant products are required. In highly processed and mixed materials such as charcoal as well as wood-based materials or paper, DNA and chemical composition is degraded or altered, whereas the anatomical wood structure is often still completely or partially preserved. Hence, identification based on structural characteristics is the sole alternative. This paper reviews and compares the main methods of wood and charcoal identification based on wood anatomy. It summarizes the strengths and shortcomings of established traditional methods and highlights the progress made by new digital and AI-driven techniques, with a focus on developments of the past decade. Charcoal ; Identification (biology) ; Species identification ; Focus (optics) ; Wood industry Wood and Agarwood Research ; Wood Treatment and Properties ; Industrial Vision Systems and Defect Detection
Authors: Weida Wang | Benteng Chen | Di Zhang | Wei Liu | Shouzhi Pu | Ben Gao | Jin Zeng | Xiao-Yong Wei | Tianshu Yu | Shuzhou Sun | Tianfan Fu | Wanli Ouyang | Lei Bai | Jiatong Li | Zifu Wang | Yuqiang Li | Shufei Zhang
Source: ArXiv.org (preprint, 2025-10-19)
Field: Materials Science — Materials Chemistry

Chem-R: Learning to Reason as a Chemist Although large language models (LLMs) have significant potential to advance chemical discovery, current LLMs lack core chemical knowledge, produce unreliable reasoning trajectories, and exhibit suboptimal performance across diverse chemical tasks. To address these challenges, we propose Chem-R, a generalizable Chemical Reasoning model designed to emulate the deliberative processes of chemists. Chem-R is trained through a three-phase framework that progressively builds advanced reasoning capabilities, including: 1) Chemical Foundation Training, which establishes core chemical knowledge. 2) Chemical Reasoning Protocol Distillation, incorporating structured, expert-like reasoning traces to guide systematic and reliable problem solving. 3) Multi-task Group Relative Policy Optimization that optimizes the model for balanced performance across diverse molecular- and reaction-level tasks. This structured pipeline enables Chem-R to achieve state-of-the-art performance on comprehensive benchmarks, surpassing leading large language models, including Gemini-2.5-Pro and DeepSeek-R1, by up to 32% on molecular tasks and 48% on reaction tasks. Meanwhile, Chem-R also consistently outperforms the existing chemical foundation models across both molecular and reaction level tasks. These results highlight Chem-R's robust generalization, interpretability, and potential as a foundation for next-generation AI-driven chemical discovery. The code and model are available at https://github.com/davidweidawang/Chem-R. Foundation (evidence) ; Chemist ; Scientific reasoning ; Pipeline (software) Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Chemistry and Chemical Engineering
Authors: Takahiro Matsumoto | Shigeru Yokota | Takuma Kaneko | Mayeesha Marium | Jeheon Kim | Yasuhiro Watanabe | Hiroyuki Iwamoto | Keiji Umetani | Tomoya Uruga | Albert Mufundirwa | Yuki Mizuno | Daiki Fujioka | Tetsuya Miyazawa | Hirokazu Tsuji | Yoshiharu Uchimoto | Masashi Matsumoto | Hideto Imai | Y. Sakurai
Source: Applied Sciences (article, 2025-04-03)
Field: Materials Science — Surfaces, Coatings and Films

FC-BENTEN: Synchrotron X-Ray Experimental Database for Polymer-Electrolyte Fuel-Cell Material Analysis This review is focused on FC-BENTEN, an advanced synchrotron X-ray experimental database developed at SPring-8 with support from Japan’s New Energy and Industrial Technology Development Organization (NEDO). Designed to advance polymer electrolyte fuel cells (PEFCs) research, FC-BENTEN addresses challenges in improving efficiency, durability, and cost-effectiveness through data-driven approaches informed by materials informatics (MI). Through standardization of protocols for sample preparation, data acquisition, analysis, and formatting, the database ensures high-quality, reproducible data essential for reliable scientific outcomes. FC-BENTEN streamlines metadata creation using automated processes and template-based tools, enhancing data management, accessibility, and interoperability. Security measures include two-factor authentication, safeguarding sensitive information and maintaining controlled user access. Planned integration with MI platforms will broaden data cross-referencing capabilities, facilitate PEFC applications expansion, and guide future research. This review discusses FC-BENTEN’s architectural framework, metadata standardization efforts, and role in advancing PEFC research through a high-throughput experimental workflow. It illustrates how data-driven methods and standardized practices contribute to innovation, underscoring databases’ potential to accelerate next-generation PEFC technologies development. Materials science ; Synchrotron ; Electrolyte ; Fuel cells ; Chemical engineering ; Chemistry ; Engineering ; Physics ; Physical chemistry ; Nuclear physics Electron and X-Ray Spectroscopy Techniques ; X-ray Diffraction in Crystallography ; Machine Learning in Materials Science
Authors: Hiroaki Mamiya | Noriki Terada | Simon Rosenqvist Larsen | Naohito Tsujii | Kosuke Hiroi | T. Shinohara | Hossein Sepehri-Amin
Source: Institutional Repositories DataBase (IRDB) (article, 2025-03-03)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Evaluation of Ho100−x−yErxCey magnetic refrigerants using advanced multisample neutron transmission spectroscopy This article presents a novel multisample neutron transmission spectroscopy method, specifically developed to analyze the spin configurations of Ho100-x-yErxCey across 25 different compositions. Conventional neutron diffractometry is precise but inefficient for evaluating the extensive range of these alloys required for advanced hydrogen liquefaction technologies, prompting the development of our novel technique. We demonstrate that this new spectroscopic technique is not only crucial for high throughput evaluation of the magnetic refrigerants but also serves as a valuable tool for big data-driven research into new magnetic materials. Spectroscopy ; Transmission (telecommunications) ; Neutron ; Refrigerant ; Calibration Magnetic and transport properties of perovskites and related materials ; Magnetic confinement fusion research ; Physics of Superconductivity and Magnetism
Authors: Rowan Walrath
Source: C&EN Global Enterprise (article, 2025-03-24)
Field: Materials Science — Materials Chemistry

AI doesn’t know how to depict chemistry AI on caffeine Generative artificial intelligence (genAI) software isn’t coming for chemists’ jobs just yet—at least not when it comes to visualizing molecules. As an experiment, this Newscriptster directed three genAI chatbots to draw the chemical structure for caffeine. When Grok, the genAI tool made by X, was asked to “draw a caffeine molecule,” the program provided images of grayish, amorphous collections of spheres linked by what might be chemical bonds. It had the characteristically glossy, dreamlike sheen of all genAI “art.” Aside from looking “menacing,” as one C&EN colleague described it, it wasn’t evocative of a chemical structure—caffeine or any other—so Grok got more-specific instructions: “Draw the molecular structure for caffeine.” This time, Grok supplied one image that was more like a spiderweb than anything resembling caffeine. “Caffeine, or MOF? You decide,” said another C&EN colleague. (Notably, caffeine is not a metal-organic framework.) Its final generated faux structure involved Chemistry ; Polymer science Machine Learning in Materials Science
Authors: Richard C Yarnell | Paul Amoruso | Ronald F. DeMara
Source:  (article, 2025-12-14)
Field: Materials Science — Materials Chemistry

Prompt Engineering for Fpga Design: Llm-Driven Workflows Beyond Hls The relentless growth of hardware complexity, fueled by advances in semiconductor technology, is pushing traditional design methodologies to their limits. To meet these escalating demands, engineers are beginning to leverage the rapid advancements in artificial intelligence, particularly large language models (LLMs), to accelerate and optimize workflows. Within this work, we reveal proactive approaches to improve how widely used LLMs, namely ChatGPT, DeepSeek, Google Gemini, and Meta AI, generate Verilog Hardware Description Language code. Using these models, we create synthesizable implementations of select circuits from the MachSuite benchmark set: a molecular dynamics calculator, a sparse matrix multiplier, a sorting engine, and a Viterbi decoder. Furthermore, we utilize the LLMs to drive functional verification of the generated designs and characterize the code defects introduced by each model. We then invoke an iterative LLM prompting strategy to identify and correct these problems. Our AI-based code repair strategy achieves high success rates, up to 100 % with DeepSeek, 93 % with Meta AI, and 88 % with Gemini for the selected use cases. Finally, we synthesize our generated circuits for a Xilinx Virtex UltraScale+ FPGA and obtain hardware implementations that consistently require fewer logic resources, and frequently feature higher operating frequencies, than functionally equivalent circuits generated by standard runs of Xilinx Vitis High-Level Synthesis. Field-programmable gate array ; Implementation ; Hardware description language ; Benchmark (surveying) ; VHDL ; Reconfigurable computing ; Netlist ; Verilog ; Place and route ; Code (set theory) Machine Learning in Materials Science ; Embedded Systems Design Techniques ; Parallel Computing and Optimization Techniques