jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Tariq Mahmood
Source: Diamond and Related Materials (article, 2025-05-21)
Field: Materials Science — Materials Chemistry

Permeation selectivity of defected hexagonal boron nitrides sheets towards H2 Hexagonal boron nitride ; Selectivity ; Permeation ; Hexagonal crystal system ; Boron ; Materials science ; Boron nitride ; Nanotechnology ; Chemistry ; Chemical engineering ; Crystallography ; Graphene ; Catalysis ; Organic chemistry Graphene research and applications ; Hydrogen Storage and Materials ; Boron and Carbon Nanomaterials Research
Authors: Lingyi Meng | Yiyan Wu | Jing Xi | Binling Yao | Yi‐Fei Deng | Yuan‐Zhu Zhang
Source: Inorganic Chemistry Communications (article, 2025-10-15)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Tuning electron transfer properties in cyanide-bridged [Fe2Co2] capsules with hexafluoropnicogen(V) anions Intermolecular force ; Electron transfer ; Alkyl ; Electron ; Transition metal ; Electron paramagnetic resonance ; Cobalt ; Ion Magnetism in coordination complexes ; Porphyrin and Phthalocyanine Chemistry ; Metal-Catalyzed Oxygenation Mechanisms
Authors: Abhiroop Bhattacharya | Sylvain G. Cloutier
Source: Machine Learning Science and Technology (article, 2025-10-29)
Field: Materials Science — Materials Chemistry

MatMMFuse: Multimodal fusion model for material property prediction Abstract The recent progress of using graph based encoding of crystal structures for high throughput material property prediction has been quite successful. However, using a single modality model prevents us from exploiting the advantages of an enhanced features space by combining different representations. Specifically, pre-trained Large language models can encode a large amount of knowledge which is beneficial for training of models. Moreover, the graph encoder is able to learn the local features while the text encoder is able to learn global information such as space group and crystal symmetry. In this work, we propose Material MultiModal fusion, a fusion based model which uses a multi-head attention mechanism for the combination of structure aware embedding from the crystal graph convolution network (CGCNN) and text embeddings from the SciBERT model. We train our model in an end-to-end framework using data from the materials project dataset. We show that our proposed model shows an improvement compared to the vanilla CGCNN and SciBERT model for all four key properties-formation energy, band gap, energy above hull and Fermi energy. Specifically, we observe an improvement of 40% compared to the vanilla CGCNN model and 68% compared to the SciBERT model for predicting the formation energy per atom. Importantly, we demonstrate the zero shot performance of the trained model on small curated datasets of Perovskites, Chalcogenides and the joint automated repository for various integrated simulation dataset. The results show that the proposed model exhibits better zero shot performance than the individual plain vanilla CGCNN and SciBERT model. This enables researchers to deploy the model for specialized industrial applications where collection of training data is prohibitively expensive. Embedding ; Graph ; Convolution (computer science) ; Encoder ; Encoding (memory) ; Property (philosophy) ; ENCODE ; Data modeling Machine Learning in Materials Science ; Inorganic Chemistry and Materials ; Advanced Graph Neural Networks
Authors: Usman Ali (6586886) | Rafael Meinusch (8086574) | Kevin Turke (5642177) | Peter R. Schreiner (770003) | Bernd M. Smarsly (1573363)
Source: Figshare (article, 2025-05-28)
Field: Materials Science — Materials Chemistry

Tuning theMesopore Network in Meso-Macroporous SilicaMonoliths by Hydrothermal Treatment – A Physisorption Study Macro-mesoporous silica monolith columns, prepared by a sol–gel procedure developed by K. Nakanishi, show beneficial flow and separation properties due to their 3D-interconnected macropores in combination with mesopores, providing a high surface area. Building on this, they are routinely used in analytical liquid chromatography. Within the synthetic process, fine-tuning of the mesopore dimension and interconnection is achieved by an etching step involving hydrothermal treatment under basic conditions, typically in the range of 80 °C–100 °C. The present study aims to unravel details of this harsh procedure by a comprehensive analysis of the resulting mesoporous network. Thus, a series of silica monoliths was prepared across a range of hydrothermal treatment temperatures (HTT) between 80 and 110 °C, thereby tuning the mesoporosity. Mercury intrusion porosimetry confirmed that enhanced HTT does not alter the macropore dimension and only affects the mesopore space. The study employed state-of-the-art physisorption analysis applying two adsorptives, Ar (87 K) and N<sub>2</sub> (77 K), to identify changes in the mesopore size and network connectivity as a function of HTT. Also, advanced hysteresis scanning was performed on the same materials, providing independent insights into pore network effects. These analyses indicate that increasing HTT systematically enhances the average mesopore size from 8 nm (80 °C) to approximately 25 nm (110 °C) and widens the pore size distribution, pointing to pronounced dissolution of SiO<sub>2</sub> at higher HTT. Surprisingly, the total mesopore volume remains constant upon increasing the HTT, implying a dissolution-reprecipitation mechanism for SiO<sub>2</sub>, rather than mere etching. Importantly, the in-depth porosity analysis reveals an increase in the size of necks, which reduces restrictions in the mesopore network connectivity. Furthermore, the data are in line with a recently proposed spatial mesopore size gradient in monoliths, which we find to be relevant at lower HTT and to systematically diminish toward higher HTT. Mesoporous material ; Macropore ; Physisorption ; Porosimetry ; Monolith ; Hydrothermal circulation ; Porosity Mesoporous Materials and Catalysis ; Analytical Chemistry and Chromatography ; Adsorption, diffusion, and thermodynamic properties of materials
Authors: Fengai Zhao | Hongyan Wang | Fang Wang | Ye Tian
Source: ACS Omega (article, 2025-12-05)
Field: Materials Science — Ceramics and Composites

Effects of Carbon Doping on the Structural, Energetic, and Electronic Properties of Intrinsic Defects in α-Al <sub>2</sub> O <sub>3</sub> The atomic-scale mechanisms governing the carbon doping effects in α-Al<sub>2</sub>O<sub>3</sub> remain incompletely understood. Using density functional theory (DFT), we systematically investigate how carbon substitution modulates the structural, energetic, and electronic properties of α-Al<sub>2</sub>O<sub>3</sub> with intrinsic defects. The findings reveal that structural distortion is strongly site-dependent. C substitution at oxygen sites (C<sub>O</sub>) induces significantly greater lattice deformation than aluminum-site substitution (C<sub>Al</sub>). Average distances from defect positions to neighboring atom analyses show that C doping preferentially perturbs the O atom distances in V<sub>Al</sub>/Al<sub>i</sub> systems, while dominantly affecting Al-atom distances in V<sub>O</sub>/O<sub>i</sub> systems. Furthermore, formation energies of intrinsic defects decrease by up to 35% with C doping under both O-rich and O-poor conditions. C<sub>O</sub> doping most effectively stabilizes vacancies/interstitials, while C<sub>Al</sub> preferentially lowers Frenkel pair energies. Aluminum vacancies (V<sub>Al</sub>) exhibit the lowest formation energy. Additionally, C doping introduces midgap states through C 2p and O 2p hybridization, narrowing band gaps from 6.16 eV (pristine) to 1.60-4.45 eV. This work provides crucial insights into how carbon doping modulates defect formation and local structure in α-Al<sub>2</sub>O<sub>3</sub>, offering valuable guidance for the strategic design of alumina-based materials with tailored properties through fine-tuning defect engineering. Doping ; Density functional theory ; Carbon fibers ; Electronic structure ; Atom (system on chip) ; Crystallographic defect ; Oxygen ; Lattice (music) Advanced ceramic materials synthesis ; Silicon and Solar Cell Technologies ; Semiconductor materials and devices
Authors: Antonio Vinci | Matteo Mor | Simone Failla | Pietro Galizia | Luca Zoli | Diletta Sciti
Source: Journal of the European Ceramic Society (article, 2025-03-06)
Field: Materials Science — Materials Chemistry

Novel CMCs based on B4C-TiB2 blends and carbon fibres Lightweight carbon fibre reinforced B 4 C/TiB 2 composites with varying B 4 C contents and fibre gradients were produced by slurry infiltration and sintering for the first time. Tuning the B 4 C content allowed to adjust the fibre/matrix interfacial strength, leading to different fracture behaviours: for a B 4 C content of 75%, the fibre/matrix interface was relatively weak and the composite had significantly high fracture toughness, whereas higher TiB 2 contents led to a denser matrix, stronger interface and low degree of delamination. Even though the fracture toughness was considerably high, the addition of Carbon fibres lowered the hardness down to ≈ 4 GPa. Therefore, an additional composite with a fibre gradient was fabricated to demonstrate the feasibility of combining a high fibre content in the inner layer with a harder layer on the outside, reaching hardness values of ≈ 27 GPa. • Carbon fibre-reinforced TiB 2 /B 4 C composites with fibre contents > 40 vol% were successfully manufactured for the first time via slurry infiltration and sintering techniques, incorporating both long and short fibres. • Adjusting the B4C/TiB2/fibre ratio allows fine-tuning of the fibre/matrix interfacial strength, promoting extensive fibre pull-out and enhancing fracture toughness. • Long fibre-reinforced composites exhibited a flexural strength of approximately 300 MPa, comparable to ZrB 2 -based materials of similar composition. • Short fibre-reinforced composites displayed a flexural strength of around 150 MPa and a fracture toughness of 5.5 MPa·m⁰·⁵, due to the random distribution of fibres. • A graded composite (density of 2.5 g/cm³) with a fibre content gradient—approximately 50% short fibres in the core and a fibre-free outer shell—achieved an excellent combination of properties, including high hardness (~27 GPa) in the outer layer and improved fracture toughness (5.5 MPa·m⁰·⁵) in the core. Materials science ; Composite material ; Carbon fibers ; Chemical engineering ; Composite number Boron and Carbon Nanomaterials Research ; Advanced ceramic materials synthesis ; MXene and MAX Phase Materials
Authors: Anna Niemczyk | Stanisław Jagielski | Ryszard Kluczowski | Jakub Kupecki | Magdalena Kosiorek | Leszek Ajdys | P. Ostrowski
Source: ECS Meeting Abstracts (article, 2025-11-24)
Field: Materials Science — Materials Chemistry

Durable Air Electrodes for Solid Oxide Cells - a Gradual LSC-GDC Composite Approach The primary limitation of Solid Oxide Cell (SOC) technology lies in the rapid degradation of cells during electrolysis mode. To address these issues while maintaining the core design of the cells, incorporation of the a composite electrode with fine-tuned microstructure proposed to reduce the risk of air electrode delamination. This composite consists of a buffer layer material (Gd0.1Ce0.9O2, GDC) and a state-of-the-art electrode (La0.6Sr0.4CoO3-δ, LSC), which minimizes the thermal expansion mismatch between the electrode, buffer layer, and electrolyte. Additionally, altering the electrode microstructure, for instance by adding a pore-forming agent, can improve the collection of oxygen during operation in SOE mode, thus preventing the development of potential differences and stress at the electrode-electrolyte interface. This study evaluated the electrochemical performance of solid oxide cells (SOCs) featuring a modified composite LSC-GDC air electrode with a gradual composition gradient. Batch of four different types of air electrode configurations were proposed and tested at the single-cell level (50 mm x 50 mm), and the most promising designs were implemented in SOC stacks (3–5 cells of 110 mm x 110 mm). Electrochemical analysis included j-V curve measurements and EIS spectra under SOE mode, including lon-term testing. Microstructural, morphological, and compositional analyses of the fabricated and tested cells were conducted using SEM, and SEM-EDS. The results demonstrated that a gradual reduction in GDC content across the electrode layers effectively produced a stable air electrode (tests &gt; 300 h) with high catalytic activity, minimizing the risk of delamination. Acknowledgments The presented research was financially supported by: the National Centre for Research and Development, Poland, within project no. LIDER/1/0003/L-12/20/NCBR/2021. Anna Niemczyk acknowledges support from the Foundation for Polish Science through a scholarship from the START program. Electrode ; Composite number ; Electrolysis ; Oxide ; Electrochemistry ; Clark electrode ; Palladium-hydrogen electrode ; Buffer (optical fiber) Advancements in Solid Oxide Fuel Cells ; Chemical Looping and Thermochemical Processes ; High-Temperature Coating Behaviors
Authors: Mingxuan Li
Source:  (article, 2025-07-25)
Field: Materials Science — Materials Chemistry

AMFR: Attentive Multi-Frequency Regressor for High-Precision Numerical Prediction in Large Language Models Across diverse benchmarks, large language models set new performance standards., but they are still struggling with high-precision numerical prediction tasks, because large language models are not sensitive to high-precision numbers and often have accuracy problems when faced with thousands of digits. To solve this problem, we propose a novel output layer structure, the Attentional Multi-Frequency Regressor (AMFR). Unlike the traditional linear regression head that simply maps hidden states, AMFR first projects the hidden representation into a multi-frequency space and constructs multi-scale frequency features using learnable sine-cosine basis functions; it then adaptively fuses the frequency components through an integrated attention mechanism to generate more refined high-precision numerical predictions. Experimental results show that AMFR significantly reduces the prediction error in high-precision numerical regression tasks such as molecular property prediction and time series prediction, effectively capturing important information at different frequencies. Our work provides an effective way to improve the performance of large language models in numerical reasoning tasks. Property (philosophy) ; Language model ; Representation (politics) ; Set (abstract data type) ; Series (stratigraphy) ; Basis (linear algebra) ; Regression ; Mean squared prediction error ; Time series Machine Learning in Materials Science ; Topic Modeling ; Machine Learning in Healthcare
Authors: Jithendaraa Subramanian | Linda Hung | Daniel Schweigert | Santosh K. Suram | Weike Ye
Source: ArXiv.org (preprint, 2025-06-27)
Field: Materials Science — Materials Chemistry

XxaCT-NN: Structure Agnostic Multimodal Learning for Materials Science Recent advances in materials discovery have been driven by structure-based models, particularly those using crystal graphs. While effective for computational datasets, these models are impractical for real-world applications where atomic structures are often unknown or difficult to obtain. We propose a scalable multimodal framework that learns directly from elemental composition and X-ray diffraction (XRD) -- two of the more available modalities in experimental workflows without requiring crystal structure input. Our architecture integrates modality-specific encoders with a cross-attention fusion module and is trained on the 5-million-sample Alexandria dataset. We present masked XRD modeling (MXM), and apply MXM and contrastive alignment as self-supervised pretraining strategies. Pretraining yields faster convergence (up to 4.2x speedup) and improves both accuracy and representation quality. We further demonstrate that multimodal performance scales more favorably with dataset size than unimodal baselines, with gains compounding at larger data regimes. Our results establish a path toward structure-free, experimentally grounded foundation models for materials science. Encoder ; Representation (politics) ; Modalities ; Workflow ; Convergence (economics) ; Multimodal learning ; Scalability ; Path (computing) ; Architecture Machine Learning in Materials Science ; Advanced Electron Microscopy Techniques and Applications ; X-ray Diffraction in Crystallography
Authors: Dianwei Zhang | Feng Li | Xuecheng Zhu | Hongfei Li | Huilin Liu | Baoguo Sun
Source: Trends in Food Science & Technology (article, 2025-10-04)
Field: Materials Science — Biomaterials

Aromatic short peptides: from self-assembled architectures to fluorescent sensing applications in food safety Stacking ; Fluorescence ; Food safety ; Food supply ; Heavy metals ; Metal ions in aqueous solution Supramolecular Self-Assembly in Materials ; Advanced biosensing and bioanalysis techniques ; Polydiacetylene-based materials and applications
Authors: Mohamed Wahib
Source:  (article, 2025-03-10)
Field: Materials Science — Materials Chemistry

The Use of LLMs and Other AI Models in Science The rapid advancement of large language models (LLMs) and other AI models has opened new frontiers in scientific research. In this talk, we will explore how these cutting-edge technologies are transforming various scientific disciplines. Across a wide range of science domains, LLMs and AI are enabling researchers to tackle complex problems with unprecedented speed and precision. We will first give an introduction to LLMs, then we discuss practical applications in fields such as chemistry, biology, and physics, and examine the potential challenges and ethical considerations in integrating these AI models into scientific workflows. Scientific modelling ; Range (aeronautics) ; Field (mathematics) ; Sociology of scientific knowledge Machine Learning in Materials Science ; Artificial Intelligence in Healthcare and Education ; Scientific Computing and Data Management
Authors: Vuylsteke, Bert | Beerten, Pieter | Detand, Jan | Ostuzzi, Francesca
Source: Ghent University Academic Bibliography (Ghent University) (article, 2025-01-01)
Field: Materials Science — Polymers and Plastics

From nature to innovation : a design perspective on biobinders, biofibers, bio-additives and biocomposites Industrial designers rely predominantly on fossil- and mineral based materials, despite the pressing need for sustainable alternatives and the existence of high-performing, fully biobased options. This study addresses the persistent knowledge gap surrounding fully biobased materials, particularly biocomposites composed of biobinders, biofibers, and bio-additives. It offers a comprehensive, design-oriented overview tailored to industrial design. The objective of this study is to support the process of biobased material selection and foster broader adoption of biocomposites in design practice. To this end, a thematic analysis was conducted using online Large Language Models (LLMs) to generate an initial material inventory. The outcome was completed and refined through expert review. All information was cross-verified with academic sources. Properties of each material were categorized into four domains following Michael Ashby’s materials selection framework: experiential, mechanical and chemical, processing, and circularity. Main result is an open-access database (spreadsheet format) that converges fragmented data into a practical material composition database for designers. Key findings highlight substantial gaps in available data and significant variability in reported material properties, often due to both natural variation and limited empirical research. These insights challenge prevailing industrial norms around uniformity and underscore the potential of biodesign to embrace open-ended, locally sourced, adaptive, and regenerative design practices. Future work includes expert validation of property categories, development of selection guidelines for composing constituents, and exploration of fabrication techniques. This study lays the groundwork for more sustainable material choices in design and contributes to advancing circular and regenerative economies. Selection (genetic algorithm) ; Perspective (graphical) ; Process (computing) ; Work (physics) ; Property (philosophy) ; Material selection ; Natural materials ; Sustainable development Natural Fiber Reinforced Composites ; Material Selection and Properties ; Plant and Biological Electrophysiology Studies
Authors: Beatriz A. Riga–Rocha | José Carlos Netto‐Ferreira | Carla C. Schmitt | Antônio E.H. Machado | Thiago F. Silva
Source: Journal of Photochemistry and Photobiology A Chemistry (article, 2025-12-19)
Field: Materials Science — Materials Chemistry

Solvatochromic and electronic effects in the photochemical behavior of chalcones and iminochalcones Photoisomerization ; Solvatochromism ; Ultrafast laser spectroscopy ; Flash photolysis ; Solvent effects ; Photochromism ; Absorption spectroscopy ; Intramolecular force ; Absorption (acoustics) Photochromic and Fluorescence Chemistry ; Photochemistry and Electron Transfer Studies ; Synthesis and biological activity
Authors: Zihan Zhang | Jia Mao | Y.-H. Pao | Linghao Zou | Hsin-Han Peng | Hsiang‐Chen Chui
Source: Results in Optics (article, 2025-07-03)
Field: Materials Science — Surfaces, Coatings and Films

Design and fabrication of a SiC chiral lens using spiral microstructures • The study demonstrated precise fabrication of chiral microstructures on SiC plates, showcasing how laser power and etching depth can be finely controlled to manipulate the morphology and depth of spiral patterns, which is crucial for optimizing SiC-based optical devices. • CD spectroscopy revealed that the chirality of etched patterns significantly influences the interaction with polarized light, allowing for fine-tuning of the CD response. The fabricated SiC chiral lenses achieved a CD value of 300 mdeg, indicating their strong potential for polarization control in photonic applications. • The study provides a reliable method for creating chiral SiC structures and insights into the relationship between microstructural parameters and optical performance, opening avenues for further research with different laser wavelengths and pattern designs to refine optical characteristics for innovative photonic applications. Polarization devices are essential components in optical systems, enabling the control and tuning of light polarization. Silicon carbide (SiC) stands out in this context due to its high refractive index, low absorption, and resistance to environmental conditions. In this work, we present the design, fabrication, and optical evaluation of SiC chiral lenses. The spiral patterns on the SiC wafer surface were fabricated using ultraviolet picosecond laser etching with both clockwise and counterclockwise orientations. The etching process achieved nanometer-scale depths with micrometer-scale pattern pitches. Optical simulations were performed and demonstrated good agreement with the experimental results. To assess the chiral performance of the fabricated lenses, circular dichroism (CD) spectroscopy was employed. CD measures the differential absorption of left- and right-handed circularly polarized light in chiral structures. Here, SiC as a material for chiral lenses, leveraging its exceptional thermal stability, high breakdown field strength, and strong radiation resistance for enhanced durability and performance in high-power and high-temperature applications. Additionally, by optimizing etching patterns or applying surface coatings, SiC chiral lenses can be customized for specific wavelength applications. The proposed SiC chiral lenses achieved a CD value of 300 mdeg, demonstrating their potential for polarization control. Fabrication ; Spiral (railway) ; Lens (geology) ; Microstructure ; Materials science ; Optics ; Composite material ; Mechanical engineering ; Engineering ; Physics ; Medicine Optical Coatings and Gratings ; Metamaterials and Metasurfaces Applications ; Advanced optical system design