jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Mengfan Wu | Junfu Tan | Yu Zhu | Jie Ren
Source: arXiv (Cornell University) (preprint, 2026-04-04)
Field: Materials Science — Materials Chemistry

KappaFormer: Physics-aware Transformer for lattice thermal conductivity via cross-domain transfer learning Machine learning has been widely used for predicting material properties. However, efficient prediction of lattice thermal conductivity ($κ_\mathrm{L}$) remains a long-standing challenge, primarily due to the scarcity of high-quality training data. Here we introduce KappaFormer, a physics-aware Transformer architecture that embeds the harmonic-anharmonic decomposition of $κ_\mathrm{L}$ within the network. KappaFormer comprises a harmonic branch pre-trained on large-scale elastic property data and an anharmonic branch fine-tuned on limited experimental $κ_\mathrm{L}$ data, enabling effective knowledge transfer and enhanced generalization. High-throughput screening with KappaFormer identifies multiple candidates with ultralow $κ_\mathrm{L}$, which are further confirmed by first-principles calculations. Physics interpretability further elucidates the vibrational mechanisms governing thermal transport suppression, linking structural motifs to strong anharmonicity. This study provides a generalizable framework for physics-guided machine learning to accelerate the discovery of new materials. Interpretability ; Anharmonicity ; Thermal conductivity ; Lattice (music) ; Transfer of learning ; Thermal ; Thermal conduction Thermal properties of materials ; Machine Learning in Materials Science ; Advanced Thermoelectric Materials and Devices
Authors: Aya Benjira | D Andre | Guy Antou | Denis Rochais | Pierre Carles | T. Piquero | Alexandre Maitre
Source: Powder Technology (article, 2026-03-17)
Field: Materials Science — Ceramics and Composites

Discrete-element modeling of viscous flow sintering based on machine-learning-aided 3D microstructure reconstruction This study aims to develop a numerical model devoted to the simulation of the early stages of viscous flow sintering of glassy materials using the discrete element method (DEM). An amorphous and spherical silica powder is selected as model glassy material. The green compacts were shaped using cold uniaxial pressing. A key innovation is the creation of an accurate digital twin of the microstructure, uniquely reconstructed in 3D from real FIB/SEM images using a machine learning algorithm for particle recognition. New DEM contact laws, derived from finite element analysis, incorporate attractive forces, viscosity, and neck growth. The DEM simulation's macroscopic shrinkage trajectory deviates by less than 1% from experimental dilatometry data during the isothermal dwell, validating the model's ability to describe the initial and intermediate sintering stages. • Granular skeleton generation using FIB/SEM. • Skeleton digitalization using machine learning algorithm. • DEM sintering simulation on amorphous silica glass. Sintering ; Isothermal process ; Shrinkage ; Microstructure ; Discrete element method ; Flow (mathematics) ; Finite element method ; Amorphous solid Advanced ceramic materials synthesis ; Powder Metallurgy Techniques and Materials ; Composite Material Mechanics
Authors: H Al-Hadidi | I Abuzayed | C. Zhang | J.L. Curiel-Sosa
Source: Journal of Physics Conference Series (article, 2026-04-01)
Field: Materials Science — Materials Chemistry

A Surrogate Modelling Framework for Predicting the Effective Behaviour of Composite Materials Abstract Accurate prediction of the effective properties of composite materials is essential for their design and optimisation in high-performance engineering applications. This work presents an integrated approach combining micromechanical modelling with machine learning to establish an efficient predictive framework for composite behaviour. A dataset of effective elastic moduli was generated using Representative Volume Elements (RVEs) with periodic boundary conditions via finite element analysis. Various machine learning algorithms, including Random Forest, Gradient Boosting, and Support Vector Regression, were evaluated. Random Forest demonstrates the highest accuracy in modelling complex material responses. To further enhance efficiency, a surrogate model incorporating an active learning strategy was introduced, enabling iterative improvement by focusing computational resources on high-uncertainty regions. The framework was validated against both theoretical predictions and experimental results, showing notable accuracy, especially in the prediction of transverse and shear properties. This study offers a reliable and scalable methodology that leverages the strengths of micromechanics and data-driven modelling, facilitating the accelerated design of advanced composite materials with reduced computational demand. Micromechanics ; Surrogate model ; Random forest ; Representative elementary volume ; Finite element method ; Material properties ; Composite number ; Uncertainty quantification ; Support vector machine ; Scalability Machine Learning in Materials Science ; Composite Material Mechanics ; Model Reduction and Neural Networks
Authors: Umut Bakhbergen | Sherif Araby | Zhandos Tauanov | Nursultan Turdakyn | Reza Montazami | Fethi Abbassi | Gulnur Kalimuldina
Source: Advanced Materials Interfaces (article, 2026-01-09)
Field: Materials Science — Materials Chemistry

Ultra‐Improved Interfacial Strength Between Metallic Surface and Polyurethane via Cost‐Effective Anodizing Process ABSTRACT Interfacial strength at the metal‐adhesive joint is a pivotal parameter to define adhesive bond reliability and its overall mechanical performance. This study systematically investigates and optimizes sulfuric acid anodizing (SAA) parameters to promote the interfacial strength between aluminum Al2024 (Al) alloy and polyurethane (PU). Response surface methodology (RSM) was employed for the design of experiments by altering acid concentration, anodizing current, and time. Surface roughness and contact angle were measured to assess the effect of these parameters, while scanning electron and atomic force microscopies were employed to elucidate structure‐property relationships. SAA exceptionally enhanced the interfacial strength by increasing the contact area by creating a micro‐ and nano‐porous oxide layer, resulting in a 920% and 15 100% improvement in the single lap shear strength (SLSS) and fracture energy, respectively, after 45 min anodizing. By maximizing SLSS and minimizing cost through time, electric current, and concentration, RSM defined the optimum SAA conditions at 0.4144 M concentration, 1.0907A current, and 5.505 min; experimental measurements validated the optimized outcomes, showing respective increases of 600% and 6700% in SLSS and fracture energy. Also, the failure mode shifted from adhesive to predominantly cohesive mode. Additionally, different machine learning (ML) algorithms were trained using various combinations of anodizing and surface topography features; the most influential parameters were identified while mitigating overfitting despite the limited dataset. Among all tested ML models, support vector regression showed the highest accuracy, achieving a remarkable mean absolute percentage error of ≈11%. The study highlights the effectiveness of SAA to enhance Al/PU interface strength. Anodizing ; Response surface methodology ; Surface roughness ; Surface finish ; Adhesive ; Waviness ; Shear strength (soil) ; Oxide ; Universal testing machine ; Contact angle Anodic Oxide Films and Nanostructures ; Mechanical Behavior of Composites ; Corrosion Behavior and Inhibition
Authors: Ruan Bernardy | Patrícia Brugnerotto | Lázaro da Costa Corrêa Cañizares | Larissa Rodrigues | Betina Peres | Silvia Naiane Jappe | Brenda Dannenberg Kaster | Janete Monteiro | Sílvia Letícia Rivero Meza | M. L. R. S. de Oliveira
Source: SSRN Electronic Journal (preprint, 2026-01-01)
Field: Chemistry — Analytical Chemistry

Applications of Artificial Intelligence in Rice Quality Assessment and Adulteration Detection: A Literature Review Interoperability ; Quality assessment ; Robustness (evolution) ; Applications of artificial intelligence ; Quality (philosophy) ; Hyperspectral imaging Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Advanced Chemical Sensor Technologies
Authors: Po-Yen Chen | Teruyasu Mizoguchi
Source: arXiv (Cornell University) (article, 2026-01-19)
Field: Materials Science — Materials Chemistry

Effect of uniaxial compressive stress on polarization switching and domain wall formation in tetragonal phase BaTiO3 via machine learning potential Ferroelectric materials such as BaTiO3 exhibit spontaneous polarization that can be reoriented by an external electric field, forming the basis of various memory, actuator, and sensor applications. The polarization switching behavior, however, is strongly influenced by mechanical boundary conditions due to the intrinsic electromechanical coupling in ferroelectrics. In this study, we employ a machine learning interatomic potential to investigate the effect of uniaxial compressive stress on polarization switching and domain wall evolution in the tetragonal phase of BaTiO3. This study revealed a critical stress about 120 MPa which 90 degree polarization switching occurs. Beyond the critical stress, larger supercells exhibit lower activation energies for polarization switching with 180-degree domain wall formation and weaker constraints from periodic boundary conditions, thereby facilitating domain-wall formation. Besides, Increasing compressive stress reduces both the remnant polarization and the coercive field, while a double hysteresis loop emerges at a stress level of 80 MPa. These findings provide atomistic insights into stress-controlled ferroelectric switching and highlight the crucial role of mechanical loading in designing reliable ferroelectric devices. Ferroelectricity ; Tetragonal crystal system ; Polarization (electrochemistry) ; Phase boundary ; Hysteresis ; Compressive strength ; Stress (linguistics) Ferroelectric and Piezoelectric Materials ; Advanced Sensor and Energy Harvesting Materials ; Ferroelectric and Negative Capacitance Devices
Authors: Ruoxuan Guo | Liping Song | Wencheng Yao | Yingzhong Huo | Lu Wang | Yang Liu | Yuejie Ai | Xiangke Wang
Source: Journal of Colloid and Interface Science (article, 2026-01-29)
Field: Materials Science — Materials Chemistry

Guided electron flow in anthraquinone-methoxy donor-acceptor1-acceptor2 covalent triazine frameworks enabling superior selective uranium capture Triazine ; Ternary operation ; Photocatalysis ; Density functional theory ; Anthraquinone ; Covalent bond ; Catalysis ; Water splitting ; Seawater ; Electron Covalent Organic Framework Applications ; Radioactive element chemistry and processing ; Advanced Photocatalysis Techniques
Authors: Ziyang Yang | Yijie Zhu | Jiuyang Shi | Shuning Pan | Shaobo Yu | Yujian Pan | Zhixin Liang | Junjie Wang | Jian Sun
Source: The Journal of Chemical Physics (article, 2026-03-16)
Field: Materials Science — Materials Chemistry

Phase diagram and global structure search of bismuth using machine learning potential Bismuth's (Bi) unique high-pressure phase behavior has long attracted significant interest. Despite their significance in both technological applications and fundamental research, comprehensive and accurate modeling of these transitions remains challenging. To address this, we developed a neural equivariant potential machine learning potential for Bi with near first-principles accuracy. By integrating this potential with state-of-the-art computational techniques-including the MAGUS crystal structure search algorithm and GPUMD molecular dynamics simulations with enhanced sampling-we systematically explored the phase behavior of Bi under high-pressure and high-temperature conditions. The calculated solid-solid phase boundaries and solid-liquid coexistence line up to 4 GPa show good agreement with previous experimental results. Furthermore, we predict a new competitive phase of Bi with P42/mnm symmetry, which is dynamically stable around 2 GPa and competitive at free energy with the known phase C2/m near the melting line. Phase diagram ; Phase (matter) ; Crystal structure prediction ; Bismuth ; Artificial neural network ; Energy (signal processing) ; Line (geometry) Machine Learning in Materials Science ; Advanced Thermoelectric Materials and Devices ; Boron and Carbon Nanomaterials Research
Authors: Luana S. P. Maia | Darlan Almeida Barroso | Tiago B. Marinho | Matheus R. Araújo | João V. A. Pereira | Carlos Alexandre R. Fernandes | Renato Martins | Benoit Cluzel | Glendo Freitas Guimarães
Source: Journal of materials research/Pratt's guide to venture capital sources (article, 2026-02-03)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Resonance spectrum prediction and inverse design of plasmonic gold nanostructures based on regression Abstract Machine learning (ML) has become an important tool in nanophotonics for predicting the optical response of plasmonic nanostructures based on parameters, such as geometry and material. Traditional modeling approaches often involve high computational costs and long simulation times, which hinder rapid optimization and design. This work investigates the plasmonic resonance behavior of cylindrical gold nanostructures and evaluates several ML regression models for direct and inverse design. Among the tested methods, the Extra Trees Regressor (ET) achieved the best performance in predicting scattering spectra, with MAE of 0.097, MSE of 0.052, RMSE of 0.224, and R 2 of 0.999. For inverse design, which maps spectral inputs to physical parameters, the model demonstrated high accuracy and strong generalization, recovering nanocylinder radius with errors below 2.6%. These results highlight the potential of ML-based strategies to accelerate nanophotonic design, reduce dependence on high-cost simulations, and support efficient characterization workflows. Graphical abstract Nanophotonics ; Plasmon ; Inverse ; Inverse problem ; Nanostructure ; Resonance (particle physics) ; Mean squared error ; Inverse scattering problem ; Nanosensor Gold and Silver Nanoparticles Synthesis and Applications ; Plasmonic and Surface Plasmon Research ; Machine Learning in Materials Science
Authors: Rinsha Cholasseri | Susmita De
Source: Physical Chemistry Chemical Physics (article, 2026-01-01)
Field: Materials Science — Materials Chemistry

Photoswitches for ion channel regulation: expanding the scope of phototherapy through computational chemistry The development of ion channel phototherapy using azobenzene-based photoswitchable molecules represents a promising strategy for the precise modulation of ion channels in therapeutic applications, aiming to treat diseases such as epilepsy, long QT syndrome, Brugada syndrome, and cystic fibrosis. This article features a structure-activity analysis of such modulators, emphasizing the need to integrate chemical modifications, biological context, and computational modelling to enhance drug design. Despite advancements in photophysical tuning and scaffold optimization, critical challenges, including limited isomer selectivity and poor operating wavelengths, continue to hinder clinical translation. The functional performance of these compounds is closely linked to their electronic structure and dynamic interactions with protein environments. Advanced computational methods, including quantum mechanical (QM), molecular dynamics (MD), and QM/MM simulations, offer atomistic insights into photoisomerization mechanisms and protein-ligand dynamics. When combined with experimental validation and machine learning driven screening, these approaches may potentially accelerate the identification of next-generation light-controlled therapeutics and pave the way for personalized, reversible, and non-invasive interventions targeting ion channel dysfunction. Photoisomerization ; Ion channel ; Scope (computer science) ; Identification (biology) ; Computational model ; Drug discovery ; Quantum ; Channel (broadcasting) Photochromic and Fluorescence Chemistry ; Molecular Sensors and Ion Detection ; Supramolecular Chemistry and Complexes
Authors: Leandro Rodrigues Oviedo | Vinícius Rodrigues Oviedo | Gabriela Carvalho Collazzo | Camila A. Teles | William Leonardo da Silva
Source: Applied Materials Today (article, 2026-04-02)
Field: Materials Science — Materials Chemistry

Exploring the photocatalytic activity and acidity of nanocatalyst based on green copper oxide nanoparticles and nanozeolites: Exploratory Data Analysis, Computer Vision and Deep Learning This study employs a novel integration of Computer Vision (CV) and Deep Learning (DL) algorithms (CNN/RNN-LSTM algorithm) to investigate the acidity and photocatalytic performance of a green nanocatalyst: copper oxide nanoparticles supported on sodalite nanozeolite (nSOD@CuO-NPs). The nanocatalyst, synthesized using Camelia sinensis (green tea) extract, was characterized, revealing the sodalite, tenorite, and cuprite phases and a good compromise between surface area and stability. The NH 3 -TPD analysis indicated a presence of weak-to-moderate acidity of the nanocatalysts and a significant reduction in the density of acid sites upon CuO-NPs loading, decreasing from Ns = 135 µmol NH3 g -1 catalyst (nSOD) to Ns = 58 µmol NH 3 g -1 catalyst (nSOD@CuO-NPs), probably due to occupation/blockage of nanozeolite active sites by the nanoparticles. This modification suggests a plausible transition from BrØnsted-dominant to Lewis-dominant acidity, potentially enhancing oxidation reactions. Exploratory Data Analysis (EDA) of heterogeneous photocatalysis assays demonstrated that nSOD@CuO-NPs achieved 82% removal of a highly concentrated (70 L -1 ) binary mixture of Crystal Violet (CV) and Methylene Blue (MB) dyes after 240 min at an optimal concentration of 0.8 L -1 and neutral pH. The DL/CV model generated a 3D morphology with an acidity heat map, enabling the proposal of a comprehensive photocatalytic degradation mechanism under visible light, highlighting the critical role of Cu-based Lewis acid sites and the generated ROS in the degradation pathway. A subsequent kinetic study on the main byproduct, aniline, showed an 89.6% removal, confirming the promising photocatalytic activity of the nSOD@CuO-NPs for complete wastewater treatment. • A green copper oxide nanoparticles supported on sodalite nanozeolite was synthesized. • The photocatalytic activity of nSOD@CuO-NPs in a bicomponent system was investigated. • Up to 82% degradation was achieved for CV:MB dye mixture (70 L -1 ) after 240 min • nSOD@CuO-NPs’ acid sites distribution was explored by CNN/RNN-LSTM algorithms. • The acidity of nSOD@CuO-NPs nanocatalyst was explored by NH 3 -TPD and CV/DL analysis. Photocatalysis ; Deep learning ; Nanoparticle ; Copper ; Copper oxide ; Oxide Machine Learning in Materials Science ; Copper-based nanomaterials and applications ; Transition Metal Oxide Nanomaterials
Authors: Cheewawut Na Talang | Aniwat Kesorn | Chanaprom Cholsuk | Tobias Vogl | Rutchapon Hunkao | Asawin Sinsarp | Sujin Suwanna | Suraphong Yuma
Source: Journal of Chemical Information and Modeling (article, 2026-02-12)
Field: Materials Science — Materials Chemistry

Chemical Feature Engineering and Defect-Aware Structural Fingerprint Representations for Complex Defects in 2D Materials Designing descriptors for multiple defects in two-dimensional materials is challenging due to the diverse local atomic environments created by different defect types and arrangements. Existing physics-informed descriptors struggle to distinguish distinct defect configurations with identical composition, while deep learning models, though powerful, require large data sets and are less interpretable. In this work, we address this limitation by engineering chemical descriptors and constructing structural features from nearest-neighbor distributions provided by the classical force-field-inspired descriptors (CFID). We show that our engineering method, combined with defect-aware structural features derived from the Hellinger distance, even excluding the full distribution features, improves data point discrimination in high-dimensional feature space while reducing the number of features by 50%. In predicting formation energy per defect site, this extended feature set balances reliance on a few dominant features, enhancing model interpretation and generalization at the cost of a marginal 10% increase in prediction error compared to baseline descriptors. This generalization capability is empirically validated on an external out-of-distribution data set of bulk hBN defects, where our model exhibits lower uncertainty and superior stability within the applicable physical domain (- 1 < <i>E</i><sub><i>f</i></sub> < 5 eV). However, predicting a highly complex and nonlinear target, such as the HOMO-LUMO gap, remains challenging, as none of our extensions outperform the baseline. This physics-informed approach offers an interpretable and computationally efficient alternative to deep learning models, providing new insights into defect representations in 2D materials and serving as a tool for the high-throughput prescreening of stable defect candidates prior to expensive first-principles calculations. Generalization ; Feature (linguistics) ; Stability (learning theory) ; Pattern recognition (psychology) ; Feature vector ; Set (abstract data type) ; Domain (mathematical analysis) ; Feature engineering ; Point (geometry) Machine Learning in Materials Science ; Nanopore and Nanochannel Transport Studies ; 2D Materials and Applications
Authors: Junsu Lee | Aziz Ahmed | Tae-Soo You
Source: Journal of Materials Chemistry A (article, 2026-01-01)
Field: Materials Science — Materials Chemistry

AI-assisted enhancement of thermoelectric properties in the Yb <sub> 14− <i>x</i> </sub> Ca <sub> <i>x</i> </sub> Mn <sub> 1− <i>y</i> </sub> AlySb <sub>11</sub> Zintl system An AI-guided strategy accelerates thermoelectric materials discovery. In the quinary Yb 14− x Ca x Mn 1− y Al y Sb 11 system, machine learning predicted a high ZT composition, validating a streamlined pathway for complex material development. Thermoelectric effect ; Thermoelectric materials ; Thermoelectric cooling ; Thermoelectric generator ; Work (physics) Advanced Thermoelectric Materials and Devices ; Machine Learning in Materials Science ; Rare-earth and actinide compounds
Authors: Yu Su | Xia Xiao | JiaLe Chen | Yuqian Tian | Jieyi Chen | Kali Xie | Zhouyi Guo Zhouyi Guo | Bo Xu | Huiqing Zhong Huiqing Zhong | Haolin Chen | Z M Liu
Source: Sensors and Actuators B Chemical (article, 2026-02-13)
Field: Materials Science — Materials Chemistry

Supra-carbon dots as smart surface-enhanced Raman scattering material for tumor monitoring Raman scattering ; Raman spectroscopy ; Plasmon ; Nanocomposite ; Coupling (piping) ; Metal ; Nanoparticle ; Metal ions in aqueous solution Carbon and Quantum Dots Applications ; Gold and Silver Nanoparticles Synthesis and Applications ; Nanoplatforms for cancer theranostics