jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Manuel Bärtschi | Stephan Waldner | Silvia Schwyn Thöny | Xavier Maeder | Fabian Steger | Thomas Frei | Vivek Devulapalli
Source: Journal of the European Optical Society Rapid Publications (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

UV coatings using Ta<sub>2</sub>O<sub>5</sub>-SiO<sub>2</sub> quantized nanolaminates The use of Quantized Nanolaminates (QNL) in optical interference coatings has gained significant attention in recent years. By using a magnetron sputtering tool from Evatec with rotating substrate table and multiple sputtering sources operating simultaneously, we are able to deposit these metamaterials consistently at exceptionally high rates, comparable to and sometimes even exceeding magnetron sputter rates for standard materials. This combination of high deposition rate and precision enables us to treat QNL as a stand-alone material, effectively replacing high-refractive index materials in complex interference filter designs. In our experiments, we used a combination of Ta 2 O 5 -SiO 2 QNL to produce filters like anti-reflective and shortpass coatings within the UV range of 266–350 nm, which would be not possible with Ta 2 O 5 as bulk material in similar designs. Materials science ; Optoelectronics ; Optics ; Physics Silicone and Siloxane Chemistry
Authors: Jie Zhao | Minhua Zhu | Ling Xia | Yifeng Fan | Feng Liu
Source: Scientific Reports (article, 2025-08-04)
Field: Materials Science — Electronic, Optical and Magnetic Materials

A novel passive shimming optimization method of MRI magnet based on a PSA-SQP hybrid algorithm In Magnetic Resonance Imaging (MRI), achieving a highly uniform main magnetic field (B<sub>0</sub>) is essential for producing detailed images of human anatomy. Passive Shimming (PS) is a technique used to enhance B<sub>0</sub> uniformity by strategically arranging shimming iron pieces inside the magnet bore. Traditionally, PS optimization has been implemented using Linear Programming (LP), posing challenges in balancing field quality with the quantity of iron used for shimming. This work aims to improve the efficacy of passive shimming by optimally balancing field quality, iron usage, and harmonics, leading to a smoother field profile. This study introduces a hybrid algorithm that combines a Pattern Search Algorithm with Sequential Quadratic Programming (PSA-SQP) to enhance shimming performance. Additionally, a regularization method is employed to effectively reduce the use of iron pieces. The magnetic field improved from 462 ppm to 6.7 ppm, utilizing merely 0.8 kg of iron in a 400 mm Diameter of Spherical Volume (DSV) of a 7T MRI magnet. Compared to traditional LP optimization techniques, this method notably enhanced magnetic field uniformity by 98.5% and reduced the iron weight requirement by 91.7%, showcasing impressive performance. The proposed new passive shimming algorithm is more effective in improving magnetic field uniformity for MRI applications. Sequential quadratic programming ; Magnet ; Computer science ; Optimization algorithm ; Algorithm ; Mathematical optimization ; Mathematics ; Quadratic programming ; Engineering ; Mechanical engineering Magnetic Properties and Applications ; Advanced MRI Techniques and Applications ; Magnetic properties of thin films
Authors: J.G. Rogers | A. Egly | Y. Roh | R. Terry | F. J. Wessel
Source: Plasma (article, 2025-06-30)
Field: Materials Science — Materials Chemistry

A Quasi-Spherical Fusion Reactor Burning Boron-11 Fuel In this study, particle-in-cell (PIC) simulation was used to validate a conceptual design for a quasi-spherical, net power, hydrogen-plus-boron-11-fueled fusion reactor incorporating high-temperature superconducting (HTS) magnets. By burning a fully thermalized plasma, our proposed MET6 reactor uses the principles of the 1980 magneto-electrostatic trap design of Yushmanov to improve the classic Polywell design. Because the input power consumed by the reactor will barely balance the waste bremsstrahlung radiation, future research must focus on reducing the bremsstrahlung losses to reach practical net power levels. The first step to reducing bremsstrahlung, explored in this paper, is to tune the reactor parameters to reduce the energies of trapped electrons. We assume the quality factor Q can be approximated as the ratio of fusion power output divided by bremsstrahlung power loss. Thus, assuming the particles’ power loss is negligible compared to bremsstrahlung power loss, the resulting quality factor is estimated to be Q ≈ 1.3. Boron ; Fusion ; Materials science ; Nuclear engineering ; Fusion power ; Engineering ; Physics ; Nuclear physics ; Plasma Nuclear Materials and Properties ; Nuclear reactor physics and engineering ; Cold Fusion and Nuclear Reactions
Authors: M S Pallavi | Praveen Kumar | B. R. Shivakumar
Source: Scientific Reports (article, 2025-02-06)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Design and modeling of a highly compact negative index floral shape metamaterial for flight navigation applications The collision avoidance system (CAS) is a mandatory monitoring apparatus equipped in all aircraft to safeguard flight safety. The CAS scans the predefined regions in a systematic manner for a certain length of time to detect any approaching aircraft that could potentially pose a threat. Thus, CAS requires a focused multi-element radiator which can encompass the complete azimuth region. Recent years have seen a growing emphasis on enhancing the efficiency of CAS antennas because of several constraints, such as low gain (3.6 dB), larger dimensions, substantial side-lobe amplitude (- 7 dB), and challenges with beam adaptation. The current research strives to enhance the gain of a CAS antenna by incorporating the basic idea of metamaterials (MTMs). Therefore, a compact floral-shaped double negative (DNG) MTM design is proposed. The CAS antenna routes the signal throughout the complete azimuth region, so the designed MTM must be proficient to withstand its DNG characteristics for different incident angles. Hence, the proposed design is tested at various incident angles spanning between to along the azimuth region, at a deviation. The results indicate that the proposed structure retains its DNG behavior in the desired frequency range, regardless of the incident angles. The computed effective medium ratio of the structure is 13.47 at the CAS central frequency (1.06 GHz), highlighting its compactness and efficacy. Furthermore, to analyze the function of the structure on the antenna, the unit-element (UE) is expanded to a 5 × 4 array and deployed as an additional layer on the radiator at a predetermined distance. The addition of MTM to the radiator outperformed the conventional radiator by enhancing the antenna gain, by 2.6 dB, respectively. Additionally, to confirm the experimental findings, the UE and array designs are fabricated, and the fabrication results align closely with the simulation results. Azimuth ; Antenna (radio) ; Computer science ; Metamaterial ; Acoustics ; Radiator (engine cooling) ; Optics ; Telecommunications ; Physics Metamaterials and Metasurfaces Applications ; Antenna Design and Analysis ; Advanced Antenna and Metasurface Technologies
Authors: Uğur Kafkas
Source: International Journal Of Engineering & Applied Sciences (article, 2025-05-01)
Field: Materials Science — Materials Chemistry

Size-Dependent Bending Response of Perforated Nanobeams on Winkler-Pasternak Foundation This study investigates the bending response of perforated nanobeams resting on Winkler-Pasternak elastic foundation (WPEF), using Eringen's theory of nonlocal elasticity (ENET). The analysis examines how various parameters affect the mechanical response of the nanobeam, including the nonlocal parameter, foundation parameters, filling ratio, and number of holes. Results indicate that an increase in the nonlocal parameter produces larger transverse displacements compared to classical beam theory, while the stiffness decreases due to nanoscale effects. The elastic foundation parameters significantly influence beam behavior, with the Pasternak model proving more effective than the Winkler model (WEF) in reducing displacement. Analysis of hole properties reveals that higher filling ratios increase beam stiffness, while an increase in the number of holes decreases nanobeam stiffness. These findings are crucial for optimizing the design of nanoelectromechanical systems and other nanostructured devices where bending behavior affects performance. Foundation (evidence) ; Bending ; Structural engineering ; Materials science ; Composite material ; Engineering Nonlocal and gradient elasticity in micro/nano structures ; Force Microscopy Techniques and Applications ; Thermoelastic and Magnetoelastic Phenomena
Authors: Giorgio Previati | Pietro Stabile | Federico Ballo
Source: Sensors (article, 2025-05-12)
Field: Materials Science — Materials Chemistry

A Variable Reduction Approach for Microbeams on Elastic Foundation In this paper, the bending behavior of microbeams resting on elastic foundations is analyzed. Due to the widespread use of Micro-Electro-Mechanical Systems (MEMSs) in sensing and actuation applications, various approaches have been developed for modeling such beams. Numerous specialized analytical and numerical models exist for specific configurations of beams and elastic foundations. This work proposes a novel approach. Separate models for the beam and the elastic foundation are developed using the finite element method. These models are then coupled using a variable reduction technique, in which only the degrees of freedom of the beam are retained in the solving system. This approach enables the coupling of any beam and foundation model and allows for independent refinement of the foundation mesh without increasing the size of the solving system. This method is particularly effective for analyzing configurations where the substrate exhibits nonlinear or non-homogeneous characteristics, or where gaps are present between the beam and the substrate. The nonlocal effects due to the small scale of the beam are also considered. This paper focuses on both the static deformation and frequency response of the microbeam. The proposed approach is validated against previously published models. Compared with existing models, the method presented here offers a simpler and more flexible formulation, while allowing the inclusion of nonlinearities in both the beam and foundation, as well as the modeling of gaps. Microbeam ; Beam (structure) ; Reduction (mathematics) ; Nonlinear system ; Finite element method ; Foundation (evidence) ; Computer science ; Bending ; Structural engineering ; Engineering ; Physics ; Mathematics ; Optics ; Geometry Nonlocal and gradient elasticity in micro/nano structures ; Numerical methods in engineering ; Mechanical and Optical Resonators
Authors: Malik Ahmed Mubeen | Fuyi Chen
Source: The Journal of Chemical Physics (article, 2025-11-03)
Field: Materials Science — Materials Chemistry

Deep reinforcement learning for AgPd-based multimetallic nanoclusters: Accelerating global minimum discovery in high-entropy alloy systems The combinatorial complexity and rugged potential energy surfaces of multimetallic nanoclusters, particularly high-entropy alloys, challenge traditional optimization methods, such as genetic algorithms and basin hopping. This work utilized a deep reinforcement learning framework to predict stable atomic arrangements for AgPd-based multimetallic systems, including high-entropy alloys, such as Ag3Pd3Au3Pt3Cu3 and Ag5Pd5Au5Pt5Cu5 nanoclusters. The deep reinforcement learning agent employs atom-centered symmetry functions with element-specific descriptors to encode nanocluster geometries and strategically navigate the configuration space via a hybrid action space, balancing atom selection and displacement. Predicted configurations are validated through density functional theory calculations of formation energies, confirming the thermodynamic stability of DRL-generated structures. Ab initio molecular dynamics simulations confirmed the thermal stability of high entropy alloy nanoclusters at 300 K. This approach demonstrates the potential of deep reinforcement learning to accelerate the discovery of stable multimetallic nanoclusters, offering a pathway for rational design of advanced nanomaterials. Reinforcement learning ; Stability (learning theory) ; Nanoclusters ; Ab initio ; Molecular dynamics ; Entropy (arrow of time) ; Deep learning ; Alloy Machine Learning in Materials Science ; High Entropy Alloys Studies ; Quasicrystal Structures and Properties
Authors: Johann L. Rapp | Dylan M. Anstine | Filipp Gusev | Filipp Nikitin | Kyeong‐Yeol Yun | Meredith A. Borden | Vittal Bhat | Olexandr Isayev | Frank A. Leibfarth
Source: Angewandte Chemie International Edition (article, 2025-07-14)
Field: Materials Science — Materials Chemistry

Design of Tough 3D Printable Elastomers with Human‐in‐the‐Loop Reinforcement Learning The development of high-performance elastomers for additive manufacturing requires overcoming complex property trade-offs that challenge conventional material discovery pipelines. Here, a human-in-the-loop reinforcement learning (RL) approach is used to discover polyurethane elastomers that overcome pervasive stress-strain property tradeoffs. Starting with a diverse training set of 92 formulations, a coupled multi-component reward system was identified that guides RL agents toward materials with both high strength and extensibility. Through three rounds of iterative optimization combining RL predictions with human chemical intuition, we identified elastomers with more than double the average toughness compared to the initial training set. The final exploitation round, aided by solubility prescreening, predicted twelve materials exhibiting both high strength (>10 MPa) and high strain at break (>200%). Analysis of the high-performing materials revealed structure-property insights, including the benefits of high molar mass urethane oligomers, a high density of urethane functional groups, and incorporation of rigid low molecular weight diols and unsymmetric diisocyanates. These findings demonstrate that machine-guided, human-augmented design is a powerful strategy for accelerating polymer discovery in applications where data is scarce and expensive to acquire, with broad applicability to multi-objective materials optimization. Elastomer ; Reinforcement ; Loop (graph theory) ; Reinforcement learning ; Materials science ; Human-in-the-loop ; Composite material ; Polymer science ; Computer science ; Human–computer interaction ; Artificial intelligence ; Mathematics Machine Learning in Materials Science ; Additive Manufacturing and 3D Printing Technologies ; Advanced Memory and Neural Computing
Authors: Yifan Zhao | Hongfei Zhu | Limiao Deng | Zhongzhi Han
Source: Journal of Food Composition and Analysis (article, 2025-02-11)
Field: Chemistry — Analytical Chemistry

A cumulative learning method for pixel-level hyperspectral detection of aflatoxins on peanuts using convolutional neural network Hyperspectral imaging ; Convolutional neural network ; Pattern recognition (psychology) ; Aflatoxin ; Artificial intelligence ; Pixel ; Artificial neural network ; Environmental science ; Computer science ; Biology ; Food science Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: Rongguang Zhang | Xun Chen | Han Wang | Jianfeng Sun | Shize Huang | Xuanzhi Zhang | Jiewu Leng
Source: Polymers (article, 2025-02-01)
Field: Materials Science — Biomaterials

Study on Deposition of Coaxial Electrospinning Fibers by Coaxial Auxiliary Flow Field Gas-assisted coaxial electrospinning (GACES) is a simple and general method for the mass preparation of coaxial nanofiber membranes, which has great industrial potential. However, in the manufacturing process, due to the bending instability of the jet in the electric field and the pulling effect of the gas flow field, the deposition uniformity of the fiber is still a big problem. Through finite element simulation analysis of the flow field in the manufacturing process and the construction of the jet mechanics model after adding the flow field, the influence mechanism of coaxial auxiliary flow on the fiber deposition area and its uniformity was successfully revealed in this research. Finally, the deposition area and thickness uniformity of coaxial fibers are increased by 3 times (the deposition area: 19.63 cm<sup>2</sup> → 78.50 cm<sup>2</sup>) and 2.34 times (the standard variance: 3 μm<sup>2</sup> → 10 μm<sup>2</sup>) by gas-assisted coaxial electrospinning. At the same time, the coaxial auxiliary gas flow also reduces the coaxial fiber diameter by 36.9% (the average fiber diameter: 241 nm ± 5 nm → 152 nm ± 23 nm) and the distribution range by 66% (the standard variance: 1.5 × 10<sup>2</sup> nm<sup>2</sup> → 51 nm<sup>2</sup>). This research provides a reliable idea and experimental basis for homogeneous preparation of coaxial nanofiber membranes. Coaxial ; Materials science ; Fiber ; Electrospinning ; Jet (fluid) ; Composite material ; Nanofiber ; Deposition (geology) ; Flow (mathematics) ; Bending ; Mechanics ; Mechanical engineering ; Polymer ; Physics ; Engineering Electrospun Nanofibers in Biomedical Applications ; Advanced Sensor and Energy Harvesting Materials ; Surface Modification and Superhydrophobicity
Authors: Yingjie Zhao | Wei Chen | Haoxuan Tang | Y. Zhao | Hongjinyi Liu | Zhongcheng Min | Yang Luo | Junjie Lu | Jianing Qu | Ruixue Yang | Chuanzhi Li | Qing Zhu | Wan‐Ting He | Jia Chen | Yuting Mao | Jie Niu | Zhiping Xu
Source: Journal of the American Ceramic Society (article, 2025-11-23)
Field: Materials Science — Ceramics and Composites

Exploring glass science and technology through enhanced SciGlass database ( <i>SciGlass+</i> ) Abstract Glass development is driven by scientific insights into their material genes as well as engineering practices in response to industry needs. Incubating domain knowledge into well‐formatted datasets and databases, and making them open and visible can accelerate the process, facilitating automated material discovery, and design in the data‐driven era. In this work, we implement a machine‐learning framework to collect information from published research articles. The enriched product, SciGlass+ , transcends the publicly available glass database SciGlass by including both scientific data and research metadata. With these enriched contents, research patterns of glasses are analyzed to identify milestone events in technological development and evaluate their sustainability impact. The integration of experimental data from literature and those from computer simulations provides refined insights into the composition–property relationship, thereby supporting a theory‐driven and artificial intelligence‐assisted approach to glass development. The practical applications of SciGlass+ are demonstrated in screening and designing glasses for electronic packaging and low‐carbon emission materials. Milestone ; Domain (mathematical analysis) ; Glass industry ; Sustainability ; Scientific literature ; Open source ; Electronics Glass properties and applications ; Structural Analysis of Composite Materials ; Catalysis and Oxidation Reactions
Authors: Min Zhu | Qiujie Xie | Yixuan Weng | Jian Wu | Zhen Lin | Linyi Yang | Yue Zhang
Source: ArXiv.org (preprint, 2025-06-02)
Field: Materials Science — Materials Chemistry

AI Scientists Fail Without Strong Implementation Capability The emergence of Artificial Intelligence (AI) Scientist represents a paradigm shift in scientific discovery, with large language models (LLMs) taking the lead as the primary executor in the entire scientific workflow from idea generation to experiment implementation. Recent AI Scientist studies demonstrate sufficient capabilities for independent scientific discovery, with the generated research reports gaining acceptance at the ICLR 2025 workshop and ACL 2025, arguing that a human-level AI Scientist, capable of uncovering phenomena previously unknown to humans, may be imminent. Despite this substantial progress, AI Scientist has yet to produce a groundbreaking achievement in the domain of computer science on par with automated scientific tools. Based on extensive quantitative evidence from existing benchmarks in complex engineering tasks and a systematic evaluation assess 28 research papers generated by five advanced AI Scientist systems, we argue that \textbf{the fundamental bottleneck for AI Scientists lies in their capability to execute the requisite verification procedures.} Current AI Scientist systems lack the execution capabilities needed to execute rigorous experiments and produce high-quality scientific papers. To better illustrate the root cause of this \textbf{implementation gap}, we provide an in-depth discussion on the fundamental limitations of AI Scientist. This position paper aims to call for the participants in the community to bridge the implementation gap. Executor ; Bottleneck ; Workflow ; Bridge (graph theory) ; Position paper ; Domain (mathematical analysis) ; Applications of artificial intelligence Machine Learning in Materials Science ; Scientific Computing and Data Management ; Artificial Intelligence in Healthcare and Education
Authors: Liyan Yuwen | Jieting Tang | Yayu Qi | Tianyi Zou | Shaotong Zhang | Yaqian Zhang | Qing‐Wei Zhang
Source: Chemical Science (article, 2025-12-12)
Field: Chemistry — Organic Chemistry

Diastereoselective synthesis of cyclopropyl sulfoxides <i>via</i> hydrosulfenation Cyclopropyl sulfoxides, merging two privileged motifs in medicinal chemistry, remain synthetically challenging despite their pharmaceutical potential. Herein, we report a mild, metal-free hydrosulfenation strategy that enables their direct synthesis, achieving exceptional diastereoselectivity (dr up to > 20 : 1) in systems with up to eight possible diastereomers. Chiral sulfoxides with 4 to 7 stereogenic centers were also synthesized in high dr. The methodology provides direct access to medicinally relevant architectures, including cyclopropyl sulfones, sulfoximines, and drug-conjugated hybrids. Mechanistic studies reveal stereochemical control <i>via</i> collective spatial factors including <i>endo</i>/<i>exo</i>, facial and side selectivities during the cycloaddition step. Stereocenter ; Cycloaddition ; Stereoselectivity ; Enantioselective synthesis ; Stereoisomerism Sulfur-Based Synthesis Techniques ; Synthesis and Catalytic Reactions ; Cyclopropane Reaction Mechanisms