jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Vadimas Ivinskij | Eugenijus Mačerauskas | Laisvidas Striška | Darius Plonis | Vijitashwa Pandey | Sonata Tolvaišienė | Inga Morkvėnaitė
Source: Applied Sciences (article, 2026-04-06)
Field: Chemistry — Electrochemistry

Exponential Function-Based Neural Tangent Kernels for SECM Signal Reconstruction Scanning electrochemical microscopy (SECM) provides spatially resolved electrochemical information but is constrained by long acquisition times arising from dense spatial sampling requirements. This work investigates whether physics-informed signal representations can improve neural reconstruction of SECM approach curve signals from sparse measurements. We propose an exponential function-based Neural Tangent Kernel (NTK) framework in which SECM signals are encoded using deterministic exponential feature mappings aligned with diffusion-controlled electrochemical dynamics. A layer-wise NTK checkpointing mechanism is employed to filter covariantly insignificant components during training, reducing redundancy while preserving dominant signal modes. The method is evaluated on synthetically generated SECM signals designed to replicate characteristic approach curve behavior. Quantitative performance is assessed using root mean square error (RMSE), mean absolute error (MAE), relative error (%), and the coefficient of determination (R2). Compared to a random Gaussian (Fourier feature) baseline (RMSE = 0.0952, MAE = 0.0547, Rel.Err = 17.68%), the proposed exponential mappings achieve consistently lower reconstruction error, with the best configuration yielding RMSE = 0.0858, MAE = 0.0375, and relative error = 11.10% under identical training conditions. Results demonstrate that incorporating physically motivated exponential feature representations into NTK-aware learning improves reconstruction fidelity and stability for low-dimensional electrochemical signals, highlighting the potential of physics-informed kernel methods for accelerated SECM data acquisition. Exponential function ; Scanning electrochemical microscopy ; Mean squared error ; Kernel (algebra) ; Tangent ; Gaussian function ; SIGNAL (programming language) ; Gaussian ; Artificial neural network Electrochemical Analysis and Applications ; Conducting polymers and applications ; Electrodeposition and Electroless Coatings
Authors: Sebastiaan P. Huber | Michail Minotakis | Marnik Bercx | Timo Reents | Kristjan Eimre | Nataliya Paulish | Nicolas G. Hörmann | Martin Uhrin | Nicola Marzari | Giovanni Pizzi
Source: Digital Discovery (article, 2026-01-01)
Field: Materials Science — Materials Chemistry

MC3D: the Materials Cloud computational database of experimentally known stoichiometric inorganics Density-functional theory (DFT) is a widely used method to compute properties of materials, which are often collected in databases and serve as valuable starting points for further studies. In this article, we present the Materials Cloud Three-Dimensional Structure Database (MC3D), an online database of computed three-dimensional (3D) inorganic crystal structures. Close to a million experimentally reported structures were imported from the COD, ICSD and MPDS databases; these were parsed and filtered to yield a collection of 72 589 unique and stoichiometric structures, of which 95% are, to date, classified as experimentally known. The geometries of structures with up to 64 atoms were then optimized using DFT with automated workflows and curated input protocols. The procedure was repeated for different functionals and computational protocols, generating three methodology-based MC3D subdatabases: PBE-v1, PBEsol-v1, and PBEsol-v2, with the latest containing 32 013 unique structures. All subdatabases of the MC3D are made available on the Materials Cloud portal, which provides a graphical interface to explore and download the data. The database includes the full provenance graph of all the calculations driven by the automated workflows, thus establishing full reproducibility of the results and more-than-FAIR procedures. Workflow ; Cloud computing ; Stoichiometry ; Graphical user interface ; Graph database ; Interface (matter) ; Graph Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Inorganic Chemistry and Materials
Authors: Yu-Cheng Zhu | Jia-Xi Zeng | Xin-Zheng Li
Source: ArXiv.org (article, 2026-02-03)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Unconventional superconductivity from lattice quantum disorder Unconventional superconductivity presents a defining and enduring challenge in condensed matter physics. Prevailing theoretical frameworks have predominantly emphasized electronic degrees of freedom, largely neglecting the rich physics inherent in the lattice. Although conventional phonon theory offers an elegant description of structural phase diagrams and lattice dynamics, its omission of nuclear quantum many-body effects results in misleading phase diagram interpretations and, consequently, an unsound foundation for superconducting theory. Here, by incorporating nuclear quantum many-body effects within first-principles calculations, we discover a lattice quantum disordered phase in superconductors H3S and La3Ni2O7. This phase occupies a triangular region in the pressure-temperature phase diagram, whose left boundary aligns precisely with Tc of the left flank of the superconducting dome. The Tcmax of this quantum disordered phase coincides with the maximum of superconducting Tc, indicating this phase as both the origin of superconductivity on the dome's left flank and a key ingredient of its pairing mechanism. Our findings advance the understanding of high-temperature superconductivity and establish the lattice quantum disordered phase as a unifying framework, both for predicting new superconductors and for elucidating phenomena in a broader context of condensed matter physics. Superconductivity ; Pairing ; Quantum phases ; Phase diagram ; Quantum ; Lattice (music) ; Quantum phase transition ; Hexagonal lattice Organic and Molecular Conductors Research ; Physics of Superconductivity and Magnetism ; Iron-based superconductors research
Authors: Lin Zhang
Source: Springer Link (Chiba Institute of Technology) (article, 2026-01-19)
Field: Materials Science — Polymers and Plastics

A multi-mode driven high-performance color-changing window for building environmental protection In this study, an innovative smart window color-changing is assembled by cathode (Mo^WO3) and anode (TiO2). The color change performance of the window is improved by Mo doping (reversibility increased from 79.1% to 93.6%), and the color change effect of the window can be automatically adjusted by external illumination by TiO2 on the anode. The microstructure and electrochromic behavior were characterized of the cathode. The microstructure was characterized of the anode. At the same time, the feasibility of discoloration of the device under light control is verified. Window (computing) ; Microstructure ; Anode ; Electrochromism ; Cathode Transition Metal Oxide Nanomaterials ; Pigment Synthesis and Properties ; Urban Heat Island Mitigation
Authors: Gulnoz Temirova | Fakhriddin Khabibov | Marifat Esanova
Source: Research Square (preprint, 2026-03-16)
Field: Materials Science — Polymers and Plastics

An AI-Based Framework for Automated 3D Pattern Generation and Virtual Fitting of National Garments Point cloud ; Convolutional neural network ; Point (geometry) ; Clothing ; Solid modeling ; Deep learning ; Pattern recognition (psychology) ; Machine vision Textile materials and evaluations ; 3D Shape Modeling and Analysis ; Fashion and Cultural Textiles
Authors: Lucía De Rosa | Luca Domenico D'Andrea | Alessandra Romanelli
Source: Preprints.org (preprint, 2026-03-27)
Field: Materials Science — Biomaterials

Chirality Effect on Physical and Biological Properties of Peptide-Based Hydrogels The self-assembly of peptide-based building blocks into ordered structures is widely exploited for the development of novel biomaterials, including hydrogels. In this review, we analyze the effect of chirality on the ability of peptides to form hydrogels. We describe systems composed of peptides of opposite chirality i.e. peptides composed of all L- or D-amino acids and peptides composed of amino acids with alternate chirality, i.e. one L- and D-amino acid or one block containing all L-amino acid followed by one block composed of all D-amino acids. Finally, we illustrate systems composed of mixtures of L- and D-peptides. The structural features of these compounds are discussed. We further compare the mechanical properties of hydrogels formed by homochiral and heterochiral peptides. Finally, we discuss the potential biological applications of these systems, focusing on the differences between hydrogels formed from peptides of opposite chirality or mixed chirality. Chirality (physics) ; Self-healing hydrogels ; Peptide ; Amino acid ; Block (permutation group theory) ; Well-defined Supramolecular Self-Assembly in Materials ; Hydrogels: synthesis, properties, applications ; Polydiacetylene-based materials and applications
Authors: Daniel Cozzolino
Source: Methods and Protocols (article, 2026-04-02)
Field: Chemistry — Analytical Chemistry

The Ability of Vibrational Spectroscopy to Analyze Holistically the Food Matrix-Moving Away from the Concept of Individual Compounds The concepts of food matrix and holistic analysis have been used in a wide range of scientific disciplines to describe the sum of the parts of a whole that provide a specific property or functionality to the sample. Traditional chemical and physical analysis needs to destroy the sample (e.g., dilution, extraction, drying) before analysis. The utilization of vibrational spectroscopy techniques, like near (NIR), mid infrared (MIR) and Raman spectroscopy, allows for the non-destructive analysis of food ingredients and products. The resulting output of this analysis is based on the information provided by the vibrational modes of atoms present in the different molecules, allowing the measurement of different chemical and physical characteristics of the food. The objective of this paper is to discuss the ability of vibrational spectroscopy methods to provide robust tools to analyze the food matrix holistically, moving away from the traditional analysis of individual compounds or chemical parameters. Studies discussed and presented in this review demonstrated the ability of vibrational spectroscopy (e.g., NIR, MIR and Raman spectroscopy, hyperspectral imaging) to assess the whole food matrix beyond the traditional notion of developing a calibration model. Raman spectroscopy ; Matrix (chemical analysis) ; Infrared spectroscopy ; Spectroscopy ; Calibration ; Infrared ; Food products ; Range (aeronautics) Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Edible Oils Quality and Analysis
Authors: Xuhong Li | Mo Zhou | Yongteng Wei | Tengfei Cao | Xiaoli Fan
Source: npj Computational Materials (article, 2026-03-03)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Hybrid magnetic skyrmions with near-zero Hall angle and electrical switchability in a 2D multiferroic Magnetic skyrmions hold great promise as information carriers in spintronics, yet their practical implementation is impeded by the skyrmion Hall effect (SkHE). Combining symmetry analysis, first-principles calculations, and atomic spin simulations, we demonstrate hybrid skyrmions in the multiferroic monolayer TcIrGe2Se6. It hosts a mixed Dzyaloshinskii-Moriya interaction (DMI) containing both parallel and perpendicular components. This unique DMI stabilizes hybrid skyrmions exhibiting a nearly vanishing skyrmion Hall angle, thereby suppressing the SkHE. Benefiting from strong DMI and high Curie temperature (330 K), these hybrid skyrmions maintain stable across a wide temperature and magnetic field range. The intrinsic magnetoelectric coupling enables electrical control of skyrmion chirality and current-driven motion through ferroelectric switching, while strain engineering permits continuous helicity modulation and induces a topological transition to bimerons. Our work establishes TcIrGe2Se6 as a promising platform for hybrid skyrmions and provides a multimodal control scheme, integrating electrical switching for chirality and strain engineering for helicity. Skyrmion ; Multiferroics ; Helicity ; Hall effect ; Coupling (piping) ; Curie temperature ; Chirality (physics) ; Spintronics ; Ferroelectricity Multiferroics and related materials ; Magnetic Field Sensors Techniques ; Magnetic and transport properties of perovskites and related materials
Authors: Lei Zhang
Source: E3S Web of Conferences (article, 2026-01-01)
Field: Materials Science — Polymers and Plastics

A multi-mode driven high-performance color-changing window for building environmental protection In this study, an innovative smart window color-changing is assembled by cathode (Mo^WO3) and anode (TiO2). The color change performance of the window is improved by Mo doping (reversibility increased from 79.1% to 93.6%), and the color change effect of the window can be automatically adjusted by external illumination by TiO2 on the anode. The microstructure and electrochromic behavior were characterized of the cathode. The microstructure was characterized of the anode. At the same time, the feasibility of discoloration of the device under light control is verified. Window (computing) ; Microstructure ; Anode ; Electrochromism ; Cathode Transition Metal Oxide Nanomaterials ; Pigment Synthesis and Properties ; Conducting polymers and applications
Authors: Mahmoud A. Essam | Amal Nassar | Eman Nassar | Mona Younis
Source: Scientific Reports (article, 2026-03-06)
Field: Materials Science — Polymers and Plastics

Correlation between carbon percentage and nanocomposite performance in commodity and engineering thermoplastics (ABS, HIPS, PP, and PC) In this study, the effect of graphene nanoplates (GNP) on the mechanical behavior of four engineering thermoplastics Acrylonitrile Butadiene Styrene (ABS), High-Impact Polystyrene (HIPS), Polycarbonate (PC), and Polypropylene (PP) was systematically investigated. Although graphene nanoplates (GNP) have been extensively studied as reinforcing fillers for thermoplastic polymers, the performance of these materials varies greatly depending on the polymer matrix. Uncertainty about how the fundamental chemical composition, especially carbon percentage (C%), affects GNP dispersion and the ensuing mechanical performance is a major unsolved issue. By methodically linking the mechanical response of GNP-reinforced thermoplastics with the polymer carbon content, this study seeks to close this gap. A wide variety of carbon percentages and molecular structures were represented by the selection of four economically significant polymers: ABS, HIPS, PC, and PP. Melt compounding and injection molding were used to create composites with 0.7 weight% GNP. To assess the combined impacts of C% and GNP addition, the suggested approach combines elemental analysis, scanning electron microscopy, X-ray diffraction, and mechanical testing (hardness and impact strength), all of which are backed by a 2 × 4 factorial experimental design. The findings demonstrate that whereas HIPS experiences agglomeration and performance degradation, polymers with more advantageous chemical interactions, such ABS and PP, have better GNP dispersion and improved mechanical characteristics. The derived models' capacity to reliably relate mechanical behavior, GNP incorporation, and carbon percentage were validated by statistical analysis. These results offer a useful foundation for choosing thermoplastics for nanocomposites based on graphene. Polypropylene ; Compounding ; Molding (decorative) ; Polymer ; Nanocomposite ; Polycarbonate ; Dispersion (optics) ; Thermoplastic Polymer Nanocomposites and Properties ; Graphene research and applications ; Polymer crystallization and properties
Authors: Hyun-Deok Jang | Seok-Kyu Pyo | Dong-Hee Lee | Sang-Jin Lee | Hyeon-Seok Jeong | Jeong-Eun Lee
Source: Journal of Intelligent Manufacturing (article, 2026-03-24)
Field: Materials Science — Materials Chemistry

Rule-Based neural surrogate for real-time FEM prediction in H-Beam manufacturing This paper presents a surrogate modeling framework for predicting point-level geometric and thermal characteristics, providing a computationally efficient alternative to traditional Finite Element Method (FEM) simulations. To demonstrate the effectiveness of the proposed framework, we adapt three neural network approaches: (1) a conventional residual multilayer perceptron (Residual MLP); (2) a feature-engineered Residual MLP enhanced with geometric domain knowledge, such as distances from points to cast boundaries; and (3) a novel model termed Deep Controllable Rule Learning with Joint Feasibility and Selectivity (DeepCTRL-JFS). DeepCTRL-JFS extends the original DeepCTRL framework by interpreting implicit process rules as joint feasible regions over multiple response variables and softly guiding predictions toward rule-compliant outputs through selective penalty application. Comparative evaluations confirm that explicitly embedding geometric knowledge–either through enriched input features or architectural constraints–leads to substantial improvements in overall model performance. The primary contribution of this study lies in demonstrating that incorporating domain-specific process knowledge into surrogate models, through either feature engineering or rule-based neural network architectures, significantly improves predictive accuracy and generalization capability. As a result, the proposed methodology can accelerate numerical simulations by providing fast and accurate surrogate predictions and can also support manufacturing process optimization by effectively integrating domain-specific knowledge into the learning framework. Surrogate model ; Artificial neural network ; Generalization ; Process (computing) ; Embedding ; Residual ; Perceptron ; Feature (linguistics) Machine Learning in Materials Science ; Model Reduction and Neural Networks ; Manufacturing Process and Optimization
Authors: Víctor Tuninetti | Carlos Beecher | Emanuele Vincenzo Arcieri | Angelo Oñate | Claudio García‐Herrera | Sergio Baragetti
Source: Scientific Reports (article, 2026-01-09)
Field: Materials Science — Materials Chemistry

Fracture behavior of Ti-6Al-4V in the extreme thermo-mechanical environment of fan blade-out This study presents a comprehensive numerical analysis of stress triaxiality and damage evolution in Ti-6Al-4V aeroengine containment case under realistic Fan Blade-Out (FBO) loading conditions. Using a finite element model with a validated Johnson-Cook (JC) damage model, we investigate the fan case’s response across a range of rotational speeds, culminating in an extreme-speed fracture event. The simulations accurately capture the complex interplay of high strain rates (~ 104 s−1), significant adiabatic heating (> 900 °C), and evolving stress states. A key finding is a speed-dependent shift in the failure mechanism, from tensile-driven damage at lower speeds to shear-dominated failure at higher speeds. The fracture analysis of failure predicted under coupled, high-temperature, high-strain-rate conditions shows the model operating in a slight extrapolated regime. This highlights considerable uncertainty in using models calibrated with standard, decoupled tests for predicting failure in complex, termo-mechanical events. The findings underscore the necessity of new experimental data for FBO analysis that captures coupled thermo-mechanical effects to improve the predictive accuracy of computational fracture mechanics and ensure the robust design of damage-tolerant aeroengine components. Fracture (geology) ; Finite element method ; Stress (linguistics) ; Fracture mechanics ; Damage mechanics ; Range (aeronautics) ; Deformation (meteorology) ; Adiabatic process High-Velocity Impact and Material Behavior ; Cellular and Composite Structures ; Energetic Materials and Combustion
Authors: Abdulmumin Usman | Abdulrahman Muhammad Haris | Z. Sulaiman | Otaru Oyiza | Sulaiman Tijjani Nasiru | Suleiman AliDaddy
Source: American Journal of Quantum Chemistry and Molecular Spectroscopy (article, 2026-01-23)
Field: Materials Science — Materials Chemistry

Designing Novel Quinoline Derivative as Corrosion Inhibitorfor Aluminium in Hydrochloric Acid Solutions In this research, novel quinoline derivative as aluminium corrosion inhibitor was designed by utilizing twenty three (23) molecules of quinoline derivatives tested each as corrosion inhibitors for the aluminium in HCl solution; experimentally through weight loss method, and theoretical investigations using quantitative structure activity relationship (QSAR). The inhibition efficiencies of the quinoline derrivatives obtained from the weight lossshows that some quinoline derivatives such as 5-MeQ, 5-ClQ, 8-TMeQ, 6-ACQ and 7-OHQ inhibits the corrosion better than others as indicated by percentage inhibition efficiency (IE). Quantum chemical calculation indicated that the most popular parameters which play a prominent role are the eigenvalues of the highest occupied molecular orbital (HOMO) and lowest unoccupied molecular orbital (LUMO), the HOMO-LUMO gap (ΔE), chemical hardness and softness and the number of electrons transferred from inhibitor molecule to the metal surface. Base on the several physicochemical descriptors and investigation of the adsorption of these molecules on the aluminium surface by the QSAR study of twenty three quiniline derivatives with the aid of material studio, a model was developed and validated. On the basis of the physicochemical parameters, predicted inhibition efficiency of 97.7% obtained using experimental inhibition efficiencies at 303K in 0.4MHCl and 0.2g/mol inhibitor concentration, and the correlation matrix from the QSAR study; 5-chloro,7-hydroxy-quinoline (5-Cl,7-OH-C&amp;lt;sub&amp;gt;9&amp;lt;/sub&amp;gt;H&amp;lt;sub&amp;gt;5&amp;lt;/sub&amp;gt;N) was designed and accepted as new efficient and effective quinoline derivative inhibitor for aluminium corrosion in HCl acid solution. Quinoline ; Corrosion ; Aluminium ; Hydrochloric acid ; Molecule ; Corrosion inhibitor ; Derivative (finance) Corrosion Behavior and Inhibition ; Synthesis of Tetrazole Derivatives ; Concrete Corrosion and Durability
Authors: Na Liu | Marcia Ahn | Subrata Ghosh | Dipika Mandal | Bed Poudel | Wenjie Li
Source: Crystals (article, 2026-03-20)
Field: Materials Science — Materials Chemistry

Microstructural Evolution and Precipitate Control in Boron-Doped Ni-Mn-Ti Shape Memory Alloys via Thermal Processing Elastocaloric cooling, which leverages stress-induced phase transformation in shape memory materials, represents a sustainable and energy-efficient alternative to conventional vapor-compression cooling systems. Central to optimizing these materials is understanding how thermal processing history dictates phase formation, microstructure, and thermal properties. In this study, we investigated the (Ni50Mn31.5Ti18)99.8B0.2 compound synthesized via vacuum induction melting and arc melting, followed by water quenching. Induction melting results in needle-like, boron-rich precipitates within the martensite lattice. In contrast, vacuum arc melting promoted precipitate growth at the grain boundaries. The vacuum arc melting sample exhibits ~82% martensite phase fraction, a near-ambient transformation temperature of ~277 K, a large transition entropy change of ~75 J·kg−1·K−1, and moderate thermal hysteresis of ~24 K. These results underscore the pivotal role of thermal history in tailoring phase stability and transformation thermodynamics, providing essential design guidelines for subsequent mechanical performance optimization in elastocaloric shape memory alloys for energy-efficient and sustainable thermal management applications. Shape-memory alloy ; Vacuum induction melting ; Thermal ; Temperature cycling ; Diffusionless transformation ; Hysteresis ; Thermal hysteresis ; Martensite Shape Memory Alloy Transformations ; High Entropy Alloys Studies ; Metallic Glasses and Amorphous Alloys