jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Dongxu Zhao | Shicheng Jin | Lu Feng | Wanchong Li | Zaiqing Yang | Zhe Wang | Xiaoyong Wu | Haozhen Yan | Yuhao Shi | Qiulin Fan | Yan Wang | Yu Mao | Jinsong Zhang
Source: Advanced Composites and Hybrid Materials (article, 2026-01-26)
Field: Materials Science — Electronic, Optical and Magnetic Materials

From waste to wideband: achieving ultra-broadband absorption with bio-inspired, machine learning-optimized metamaterials The development of high-performance electromagnetic wave-absorbing materials that combine cost efficiency and environmental sustainability remains a critical challenge in the fields of functional materials and stealth technology. Although the magnetoelectric coupling strategy has emerged as an important approach for designing high-performance absorbers by introducing magnetic loss and improving impedance matching, its ability to enhance absorption bandwidth remains limited, thereby restricting its application in scenarios requiring broadband absorption. To address this issue, this study proposes an innovative strategy for fabricating a hierarchical composite absorber derived from waste-based activated carbon spheres (ACSs). Inspired by natural biological structures, a sweet potato leaf-inspired electromagnetic wave absorber (SPL) has been designed. Leveraging electromagnetic wave localization theory and machine learning-assisted structural optimization, the resulting SPL successfully maximizes the localization of electromagnetic waves within the material distribution region. This leads to exceptional electromagnetic wave absorption performance, achieving a reflectivity below -10 dB across the entire 3–50 GHz frequency range at a matching thickness of only 10 mm. Simultaneously, owing to the inherent absorption characteristics of the activated carbon base, the composite retains its excellent capacity for adsorbing harmful gases. This work not only provides a high-performance, low-cost, and multifunctional electromagnetic wave absorber but also offers valuable insights into the value-added reuse of waste resources and the rational design of bio-inspired structures for advanced electromagnetic protection applications in both military and civilian architectural domains. Electromagnetic radiation ; Broadband ; Metamaterial ; Absorption (acoustics) ; Reuse ; Metamaterial absorber ; Bandwidth (computing) ; Composite number ; Reflection loss Electromagnetic wave absorption materials ; Metamaterials and Metasurfaces Applications ; Advanced Antenna and Metasurface Technologies
Authors: Roberta Fabbri | Karima Jeneh Perry | Emanuela Saracino | Chiara Lazzarini | Anna Mariano | Diletta Spennato | Maria Grazia Mola | Maria Grazia Raucci | A. A. Kovtun | Marianna Barbalinardo | Giorgia Conte | Denis Gentili | Marco Caprini | Vincenzo Palermo | Roberto Aricó Zamboni | Luigi Ambrosio | Wolfgang Losert | Andrea Candini | Grazia Paola Nicchia | Raj K. Gupta | Valentina Benfenati | Shashi P. Karna
Source: Advanced NanoBiomed Research (article, 2026-02-26)
Field: Materials Science — Materials Chemistry

Protein‐Template Gold Nanoclusters Induce Differentiation and Modify the Functional Properties of Primary Astrocytes Astrocyte ion channels, water channels, and calcium signaling play vital roles in maintaining brain homeostasis, coordinating neural network activity, supporting cognitive functions, and mediating neurovascular coupling. Disruptions in astrocyte dynamics are causally linked to gliosis occurring in pathologies, such as brain injuries and neurological diseases. Despite their importance, the potential to probe and modulate astrocyte molecular, functional, and morphological properties using intracellular light adsorbing nanoscale interfaces has been limited. In this study, we investigate the effects of fluorescent gold nanoclusters (fAuNCs) bound to bovine serum albumin (BSA) on the morphological, molecular, and functional properties of primary rat cortical astrocytes over an extended period. Treatment with fAuNCs‐BSA is not toxic over long term and induces notable morphological changes alongside alterations in whole‐cell chloride currents, cell volume regulation, and calcium signaling magnitude. To leverage the light‐absorbing properties of fAuNCs‐BSA, we expose acutely fAuNCs‐BSA‐treated astrocytes to LED blue light (λ_ex = 450 nm), which modulates potassium currents. Nanodiamond thermometry suggests that this effect results from local heating caused by photoexcitation of the nanoclusters. The potential of nanoclusters as a transformative approach for studying astrocyte function and their role in the biophysical mechanisms underlying neural communication is discussed. Astrocyte ; Nanoclusters ; Calcium in biology ; Intracellular ; Synaptogenesis ; Calcium ; Gliosis ; Calcium signaling ; Photoexcitation Nanocluster Synthesis and Applications ; Advanced Nanomaterials in Catalysis ; Alzheimer's disease research and treatments
Authors: Zidi Wang | Tao Zhang | Muyao Yu | Chuyi Zhou | Zezhao Xu | Huiyu Liu | Yuzhen Wen | Chen, Linjiang | Jie Zheng | Shan Jiang
Source: ArXiv.org (article, 2026-01-18)
Field: Chemistry — Organic Chemistry

Learning to Dock: Geometric Deep Learning for Predicting Supramolecular Host-Guest Complexes Predicting non-covalent host-guest recognition remains challenging due to the complex interplay of electrostatics, dispersion, and steric effects, and the limited transferability of existing docking approaches to synthetic supramolecular systems. Here we present DeepHostGuest, a geometric deep-learning framework that learns generalizable recognition principles directly from experimentally resolved host-guest structures. Hosts are encoded as electrostatic surfaces and guests as molecular graphs, enabling transferable learning across diverse supramolecular systems. DeepHostGuest achieves high-accuracy predictions (RMSD $\leq 2$ Angstrom for 80.8% of test cases), substantially outperforming classical docking without case-specific tuning. Notably, the method generalizes beyond its training domain to crystalline sponge systems, accurately capturing the binding of large amphiphilic molecules within metal-organic cages. Beyond predicting binding conformations, the structures generated by DeepHostGuest serve as a reliable basis for accurate binding free-energy calculations. Density Functional Theory (DFT)-calculated affinities correlate well with experiment, enabling structure-property relationships across 876 host-guest complexes spanning 34 host families. Interpretable feature analysis reveals that binding strength arises from a cooperative interplay of host polarity, guest hydrophobicity, and geometric complementarity, with distinct design regimes across supramolecular classes. Together, these results establish data-driven molecular recognition as a practical route to predictive supramolecular design, enabling high-throughput virtual screening and rational optimization of functional host-guest systems. Supramolecular chemistry ; Molecular recognition ; Docking (animal) ; Binding affinities ; Virtual screening ; Supramolecular assembly ; Deep learning ; Transferability Supramolecular Chemistry and Complexes ; Supramolecular Self-Assembly in Materials ; Crystallography and molecular interactions
Authors: Zidi Wang | Tao Zhang | Muyao Yu | Chuyi Zhou | Zezhao Xu | Huiyu Liu | Yuzhen Wen | Chen, Linjiang | Jie Zheng | Shan Jiang
Source: arXiv (Cornell University) (preprint, 2026-01-18)
Field: Chemistry — Organic Chemistry

Learning to Dock: Geometric Deep Learning for Predicting Supramolecular Host-Guest Complexes Predicting non-covalent host-guest recognition remains challenging due to the complex interplay of electrostatics, dispersion, and steric effects, and the limited transferability of existing docking approaches to synthetic supramolecular systems. Here we present DeepHostGuest, a geometric deep-learning framework that learns generalizable recognition principles directly from experimentally resolved host-guest structures. Hosts are encoded as electrostatic surfaces and guests as molecular graphs, enabling transferable learning across diverse supramolecular systems. DeepHostGuest achieves high-accuracy predictions (RMSD $\leq 2$ Angstrom for 80.8% of test cases), substantially outperforming classical docking without case-specific tuning. Notably, the method generalizes beyond its training domain to crystalline sponge systems, accurately capturing the binding of large amphiphilic molecules within metal-organic cages. Beyond predicting binding conformations, the structures generated by DeepHostGuest serve as a reliable basis for accurate binding free-energy calculations. Density Functional Theory (DFT)-calculated affinities correlate well with experiment, enabling structure-property relationships across 876 host-guest complexes spanning 34 host families. Interpretable feature analysis reveals that binding strength arises from a cooperative interplay of host polarity, guest hydrophobicity, and geometric complementarity, with distinct design regimes across supramolecular classes. Together, these results establish data-driven molecular recognition as a practical route to predictive supramolecular design, enabling high-throughput virtual screening and rational optimization of functional host-guest systems. Supramolecular chemistry ; Molecular recognition ; Docking (animal) ; Binding affinities ; Virtual screening ; Supramolecular assembly ; Deep learning ; Transferability Supramolecular Chemistry and Complexes ; Supramolecular Self-Assembly in Materials ; Crystallography and molecular interactions
Authors: Umair Haider | Gul Rahman | Imran Shakir | M.S. Al-Buriahi | Norah Alomayrah | Imen Kebaïli
Source: Materials Today Physics (article, 2026-01-15)
Field: Materials Science — Materials Chemistry

Accelerated discovery of MM <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si9.svg" display="inline" id="d1e160"> <mml:msup> <mml:mrow/> <mml:mrow> <mml:mo>′</mml:mo> </mml:mrow> </mml:msup> </mml:math> XT <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si10.svg" display="inline" id="d1e168"> <mml:msub> <mml:mrow/> <mml:mrow> <mml:mn>2</mml:mn> </mml:mrow> </mml:msub> </mml:math> MXenes for catalysis, electronics, and energy storage using supervised machine learning MXenes ; Carbide ; Analytical Chemistry (journal) ; MAX phases MXene and MAX Phase Materials ; Inorganic Chemistry and Materials ; Machine Learning in Materials Science
Authors: Nikhil Chaurasia | S. Sangal | Shikhar Krishn Jha
Source: Data in Brief (article, 2026-01-08)
Field: Materials Science — Materials Chemistry

Benchmarking geometric lamellar orientation: A large-scale synthetic dataset for quantification of ferrite-pearlite steels Quantitative metallography of ferrite-pearlite steels is essential for establishing structure-property correlations, yet manual characterization is labour-intensive and prone to bias. This article presents a large-scale synthetic dataset designed to train and benchmark deep learning models for the automated segmentation of pearlite colonies and ferrite grains. The dataset was generated using a computational pipeline that superimposes experimentally obtained ferrite and pearlite morphological textures onto simulated polycrystalline templates generated via nucleation and growth phenomena. The primary parameter investigated was the geometric lamellar orientation of pearlite colonies, which was categorized into 10 distinct classes (20° angular bins and a background ferrite class) relative to the image frame. The resulting dataset comprises 10,499 synthetic micrographs (512 × 512 pixels) paired with pixel-perfect ground truth segmentation masks. This data provides a robust resource for developing computer vision algorithms capable of discerning pearlite colonies based on the geometric orientation of their lamellae, thereby facilitating high-throughput quantitative analysis in materials science. Pearlite ; Segmentation ; Benchmarking ; Lamellar structure ; Ferrite (magnet) ; Orientation (vector space) ; Benchmark (surveying) ; Pattern recognition (psychology) Machine Learning in Materials Science ; Microstructure and Mechanical Properties of Steels ; Iron and Steelmaking Processes
Authors: Taha Y. Alqahtani | Sultan H. Alharbi | Stalin Arulsamy | Swati Mayur Keny | Malarkodi Velraj | Kumarappan Chidambaram | Panneerselvam Theivendren
Source: Nanomedicine Nanotechnology Biology and Medicine (article, 2026-01-24)
Field: Chemistry — Organic Chemistry

Computational prediction and SLN formulation of Narcissin for reverse transcriptase inhibition and controlled drug delivery applications Cytotoxicity ; Drug delivery ; Reverse transcriptase ; Hydrogen bond ; Zeta potential ; Molecular dynamics ; MTT assay ; Nanoparticle Chemical synthesis and alkaloids ; Cholinesterase and Neurodegenerative Diseases ; Berberine and alkaloids research
Authors: Muhammad Mujahid Ali | L. Edwin Gonzalez | Eric T. Dziekonski | J. Mitchell Wells | Dalton T. Snyder | William Allen LeFever | R.Graham Cooks
Source: TrAC Trends in Analytical Chemistry (article, 2026-03-27)
Field: Chemistry — Spectroscopy

Advancing biothreat detection technologies employing emerging mass spectrometry approaches Mass spectrometry ; Electrospray ionization ; Desorption electrospray ionization ; Ambient ionization ; Tandem mass spectrometry ; Photoionization ; Sample preparation ; Sample (material) Mass Spectrometry Techniques and Applications ; Metabolomics and Mass Spectrometry Studies ; Advanced Chemical Sensor Technologies
Authors: Yan Li | Yu Zheng | Wenjin Yao | J.P. Chen | Chuanqi Yu | Chuanyun Tao | Guixiang Yin | Hong Tang | Wei Ge | Ziyun Guo
Source: Latin American Journal of Solids and Structures (article, 2026-01-01)
Field: Materials Science — Materials Chemistry

A Hybrid YOLO–Mamba Deep Learning Framework for Real-time Ballistic Limit Velocity Prediction with Multiphysics-coupled Feature Fusion Abstract To support dynamic penetration decision-making, the millisecond-level real-time response requirement of missile attitude control systems requires efficient ballistic limit velocity (BLV) prediction models. This study proposes a deep learning model based on a YOLO–Mamba hybrid architecture, which achieves the adaptive modeling of multiphysical field coupling effects through feature cross-modules and polynomial expansion. The global feature extraction capability of YOLO and the local temporal modeling of Mamba synergistically enhance multiscale feature capture. In experiments, the model’s inference speed is 1.3 times greater than that of traditional methods, and its prediction error on ballistic datasets is reduced by 32.5–47.8% compared to those of SVM/random forests while maintaining a generalization accuracy of over 92% in data-scarce scenarios. The proposed model serves as a high-precision tool for the optimization of protective materials, and the YOLO–Mamba hybrid architecture offers a novel approach to data-driven modeling of complex impact dynamics problems. Deep learning ; Feature extraction ; Ballistic missile ; Feature (linguistics) ; Artificial neural network ; Limit (mathematics) ; Field (mathematics) ; Ballistic limit High-Velocity Impact and Material Behavior ; Guidance and Control Systems ; Military Defense Systems Analysis
Authors: Samuel Del Fré | Gilberto A. Alou Angulo | Maurice Monnerville | Alejandro Rivero Santamaría
Source: SPIRE - Sciences Po Institutional REpository (preprint, 2026-03-19)
Field: Materials Science — Materials Chemistry

Data-driven construction of machine-learning-based interatomic potentials for gas-surface scattering dynamics: the case of NO on graphite Accurate atomistic simulations of gas-surface scattering require potential energy surfaces that remain reliable over broad configurational and energetic ranges while retaining the efficiency needed for extensive trajectory sampling. Here, we develop a data-driven workflow for constructing a machine-learning interatomic potential (MLIP) tailored to gas-surface scattering dynamics, using nitric oxide (NO) scattering from highly oriented pyrolytic graphite (HOPG) as a benchmark system. Starting from an initial ab initio molecular dynamics (AIMD) dataset, local atomic environments are described by SOAP descriptors and analyzed in a reduced feature space obtained through principal component analysis. Farthest point sampling is then used to build a compact training set, and the resulting Deep Potential model is refined through a query-by-committee active-learning strategy using additional configurations extracted from molecular dynamics simulations over extended ranges of incident energies and surface temperatures. The final MLIP reproduces reference energies and forces with high fidelity and enables large-scale molecular dynamics simulations of NO scattering from graphite at a computational cost far below that of AIMD. The simulations provide detailed insight into adsorption energetics, trapping versus direct scattering probabilities, translational energy loss, angular distributions, and rotational excitation. Overall, the results reproduce the main experimental trends and demonstrate that descriptor-guided sampling combined with active learning offers an efficient and transferable strategy for constructing MLIPs for gas-surface interactions. Molecular dynamics ; Scattering ; Interatomic potential ; Highly oriented pyrolytic graphite ; Ab initio ; Radius of gyration ; Potential energy ; Graphite ; Sampling (signal processing) Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Protein Structure and Dynamics
Authors: Imed‐Eddine Benrabah | Geandier Guillaume | Olha Nakonechna | Benoit Denand | H.P. Van Landeghem | A. Deschamps | Sébastien Y. P. Allain
Source: Advanced Engineering Materials (article, 2026-03-04)
Field: Materials Science — Materials Chemistry

Deep Learning for Real‐Time Phase Quantification from X‐ray Diffraction: Toward High‐Throughput Steel Microstructure Mapping High‐throughput X‐ray diffraction (XRD) techniques are revolutionizing materials science by enabling the rapid collection of hundreds or thousands of diffraction patterns in a single experiment. However, traditional analytical methods like Rietveld refinement become a significant bottleneck for quantifying phase fractions from such large datasets, due to their time‐consuming, expert‐driven nature. In this work, we develop a convolutional neural network (CNN) approach to directly quantify phase fractions in two‐phase steels using only experimental XRD data. The model is trained on over 40 000 real XRD patterns obtained from a high‐throughput synchrotron campaign spanning a wide range of steel compositions, heat treatments, and measurement configurations. The CNN learns to map 1D diffraction patterns to phase fraction outputs, effectively bypassing the need for iterative profile fitting. On test data, the model achieves excellent agreement with ground‐truth phase fractions (obtained via conventional analysis), with mean absolute errors on the order of 2%. The inference speed (milliseconds per pattern) is almost two orders of magnitude faster than Rietveld refinement, enabling real‐time phase monitoring during high‐volume experiments. This CNN‐based method demonstrates a scalable, accurate alternative for phase quantification in high‐throughput settings, addressing the critical need for rapid data analysis in modern materials research. Rietveld refinement ; Diffraction ; Convolutional neural network ; Phase (matter) ; Bottleneck ; Deep learning ; Synchrotron Machine Learning in Materials Science ; Microstructure and Mechanical Properties of Steels ; X-ray Diffraction in Crystallography
Authors: Wenjing Huang | Ji Ding | Cheng Cheng | Feng Xue
Source: Bioorganic Chemistry (article, 2026-04-06)
Field: Materials Science — Materials Chemistry

Discovery, engineering and application of a maleate isomerase for efficient L-malic acid biosynthesis via dual-enzyme cascade with fumarase Fumarase ; Biosynthesis ; Cascade ; Isomerase ; Enzyme ; Metabolic engineering Enzyme Structure and Function ; Diet, Metabolism, and Disease ; Enzyme Catalysis and Immobilization
Authors: Ke Xu | yichao jiang | yuyu nan | Ming Fang | Jian Feng | Zhixiang Huang
Source: Optics Letters (article, 2026-03-02)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Broadband visible-light anti-reflection enabled by a bilayer dielectric metamaterial with deep-learning-driven inverse design Achieving ultra-low reflection across the visible spectrum and under wide incident angles is of considerable importance for broadband antireflection coatings and optical interface engineering. Here, we introduce a bilayer subwavelength dielectric metamaterial together with a rapid and generalizable inverse-design framework based on a tandem residual deep neural network architecture. The metamaterial is fabricated entirely from the same dielectric material as the substrate, ensuring excellent compatibility and straightforward integration. Owing to its all-dielectric composition, the design principle can also be readily extended to other spectral bands as required. Furthermore, the framework can directly incorporate different substrate material parameters and output the corresponding optimized geometry, demonstrating strong adaptability and scalability. The inverse-design strategy integrates a forward network (FNet) and an inverse network (INet), both constructed using lightweight residual blocks to ensure solution uniqueness and high prediction fidelity. Simulations show that the optimized bilayer metamaterial achieves a maximum reflectivity of less than 3% throughout the visible band (400-700 nm) and maintains stable ultra-low-reflection performance for incident angles up to 80°. Beyond providing a compact and fabrication-friendly route to broadband wide-angle antireflection, this work establishes a fast, accurate, and broadly applicable inverse-design paradigm for next-generation nanophotonic structures. Broadband ; Metamaterial ; Bilayer ; Inverse ; Refractive index ; Reflection (computer programming) ; Beam splitter Metamaterials and Metasurfaces Applications ; Plasmonic and Surface Plasmon Research ; Advanced Antenna and Metasurface Technologies
Authors: Dongdong Li | Tianyi Wang | Heng Chen | Qing  An Li | Rui Su | Bin Xu | Rulong Zhou | Jing Wang | Yuwei You | Pengfei Guan | C.S. Liu
Source: SSRN Electronic Journal (preprint, 2026-01-01)
Field: Materials Science — Materials Chemistry

A high-accuracy machine-learning interatomic potential for modeling rhenium in tungsten Void (composites) ; Tungsten ; Rhenium ; Nuclear transmutation ; Interatomic potential ; Impurity ; Fusion ; Monte Carlo method ; Bridging (networking) Fusion materials and technologies ; Nuclear Materials and Properties ; Machine Learning in Materials Science