jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Sheng Zhang | Jinbao Zhang | Lu Ren | Yaohua Shao | Weiyi Yan | Meng Tian | Kunling Peng | Ping Lin | Yunzhen Du
Source: Physical Chemistry Chemical Physics (article, 2025-12-04)
Field: Materials Science — Materials Chemistry

Vacancy-induced phonon localization and lattice softening for reduced thermal conductivity in Mg <sub>3</sub> Sb <sub>2</sub> With the natural abundance, low cost, and compatibility with sustainable technologies, Mg<sub>3</sub>Sb<sub>2</sub> has emerged as a promising mid-temperature thermoelectric material. Intrinsic point defects, particularly vacancies, are common in Mg<sub>3</sub>Sb<sub>2</sub> and play a crucial role in shaping its thermoelectric properties, guiding experimental design and performance optimization. However, their impact on lattice thermal conductivity (<i>κ</i><sub>L</sub>) remains insufficiently understood. This work investigates the effects of Mg and Sb vacancies on the <i>κ</i><sub>L</sub> of Mg<sub>3</sub>Sb<sub>2</sub> using a neural network potential (NNP). Our results show that both types of vacancies significantly reduce <i>κ</i><sub>L</sub>, primarily due to enhanced phonon-defect scattering. Comprehensive analyses of phonon dispersion, group velocities, mean square displacement (MSD), the phonon participation ratio (PPR), and elastic properties demonstrate that vacancies trigger pronounced phonon softening, slow down phonon transport, and promote strong localization, while simultaneously amplifying atomic vibrations and weakening interatomic bonding. This work clarifies the microscopic mechanisms by which point defects affect phonon transport and identifies defect engineering as an effective strategy for controlling thermal properties in thermoelectric materials. Phonon ; Thermoelectric effect ; Thermal conductivity ; Thermoelectric materials ; Crystallographic defect ; Softening ; Lattice (music) Advanced Thermoelectric Materials and Devices ; Thermal properties of materials ; Thermal Expansion and Ionic Conductivity
Authors: Zhiyi Ji | Honggao Liu | Jieqing Li | Yuanzhong Wang
Source: Microchemical Journal (article, 2025-04-08)
Field: Chemistry — Analytical Chemistry

Spectra and image features combined with machine learning for drying temperature identification of Phlebopus portentosus Identification (biology) ; Artificial intelligence ; Image (mathematics) ; Pattern recognition (psychology) ; Computer science ; Chemistry ; Materials science ; Analytical Chemistry (journal) ; Chromatography ; Botany ; Biology Spectroscopy and Chemometric Analyses ; Meat and Animal Product Quality ; Botanical Research and Applications
Authors: Juan Liao | Zhenxiang Shi | Daolong Dou | Haiou Lu | Kai Ni | Qian Zhou | Xiaohao Wang
Source: Materials (article, 2025-03-29)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Deep Learning-Assisted Design for High-Q-Value Dielectric Metasurface Structures Optical sensing technologies play a crucial role in various fields such as biology, medicine, and food safety by measuring changes in material properties, such as the refractive index, light absorption, and scattering. Dielectric metasurfaces, with their subwavelength-scale geometric features and the ability to achieve high-quality-factor (Q-value) resonances through specific meta-atom designs, offer a new avenue for achieving faster and more sensitive material detection. The resonant wavelength, as one of the key indicators in meta-atom design, is usually determined using traditional solving methods such as electromagnetic simulations, which, although capable of providing high-precision prediction results, suffer from slow computational speed and long processing times. To address this issue, this paper proposes a forward prediction network for the amplitude spectrum of dielectric metasurfaces. Test results demonstrated that the mean square error of this network was consistently less than 10-3, and the neural network required less than 1 s, indicating its high-precision prediction capability. Furthermore, we employed transfer learning to apply this network to predict the near-infrared transmission spectra of high-Q-value resonant dielectric metasurfaces, achieving significant effectiveness. This method greatly enhanced the efficiency of metasurface design, and the designed network could serve as a universal backbone model for the forward prediction of spectral responses for other types of dielectric metasurfaces. Dielectric ; Computer science ; Absorption (acoustics) ; Artificial neural network ; Materials science ; Refractive index ; Wavelength ; Representation (politics) ; Reflection (computer programming) ; Optics ; Optoelectronics ; Electronic engineering ; Artificial intelligence ; Physics ; Engineering Metamaterials and Metasurfaces Applications ; Animal Vocal Communication and Behavior ; Advanced Antenna and Metasurface Technologies
Authors: Bruna Hornink | Daigard Ricardo Ortega Rodríguez | Deoclécio Jardim Amorim | Peter Groenendijk | Kathelyn Paredes-Villanueva | José Guilherme Roquette | Ana Carolina Maioli Campos Barbosa | Edson Vidal | Alexandre Bahia Gontijo | Monique Silva Costa | Norma Rodrigues Nunes de Senna | Davi Neves de Lemos | Alejandro Venegas-Gonzalez | Cátia Henriques Callado | Nelson Jaén‐Barrios | Cláudia Fontana | Daniela Granato‐Souza | Gabriel Assis-Pereira | Edilson J. Requena‐Rojas | Leif Armando Portal‐Cahuana | Lucas G Pereira | Fábio Ribas Chaddad | Renata Cristina Bovi | Hudson Wallace Pereira de Carvalho | Mário Tomazello Filho
Source: The Science of The Total Environment (article, 2025-10-17)
Field: Chemistry — Organic Chemistry

Combining wood traits as a promising timber origin verification and its application in the Brazilian trade chain Identification (biology) ; Tracing ; Illegal logging ; Scale (ratio) ; Field (mathematics) ; Solid wood ; Provenance Wood and Agarwood Research ; Agricultural and Food Sciences ; Wood Treatment and Properties
Authors: Cunhao Lu | Fanjie Meng | Jiajie Zhang | Zeyuan Zhang
Source: Magnetochemistry (article, 2025-10-25)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Core Loss Prediction Model of High-Frequency Sinusoidal Excitation Based on Artificial Neural Network The magnitude of core loss is a crucial factor affecting the efficiency of power converters. Due to the complex mechanism of core loss, diverse influencing factors, and the strong coupling characteristics between materials and operating conditions, traditional core loss prediction models struggle to achieve high-precision prediction of core loss. Based on the Artificial Neural Network (ANN), this paper investigates core loss under high-frequency sinusoidal excitation. The core loss training data is processed using a logarithmic transformation method, and an ANN core loss prediction model is established with temperature, frequency, and magnetic flux density as features. The results show that, compared with non-logarithmic processing, logarithmic transformation of the data can effectively improve the prediction accuracy (PA) of the ANN model. Within the ±10% error range, the maximum PA of the ANN prediction model reaches 98.48%, and the minimum Mean Absolute Percentage Error (MAPE) can be as low as 2.58%. In addition, a comparison with the Steinmetz Equation (SE) and K-nearest neighbor (KNN) prediction models reveals that, for four materials, within the ±10% error range of the true core loss values, the minimum PA of the ANN model is 93.33% with an average of 95.38%; the minimum PA of the KNN model is 43.94% with an average of 62.07%; and the minimum PA of the SE model is 14.91% with an average of 19.83%. Furthermore, the MAPE of the ANN model is within 5%. Artificial neural network ; Core (optical fiber) ; Logarithm ; Mean absolute percentage error ; Transformation (genetics) ; Mean squared error ; Approximation error ; Range (aeronautics) Magnetic Properties and Applications ; HVDC Systems and Fault Protection ; Advanced DC-DC Converters
Authors: Jeong-Wan Lee | G.S. Choi | Sungjun Yang
Source: IEEE Access (article, 2025-01-01)
Field: Chemistry — Organic Chemistry

POD-Based Machine Learning Approach for Coupled EM-Thermal Analysis in Microwave Heating Microwave heating processes require accurate simulation of time-varying electromagnetic (EM) properties to ensure reliable temperature predictions. Conventional methods which require conducting full-wave EM simulations at every thermal step, impose substantial computational burdens. In this paper, we propose a machine learning-based approach for reduced-order modeling that efficiently predicts the specific absorption rate (SAR) distributions in coupled EM and thermal analyses. The method employs proper orthogonal decomposition (POD) to extract dominant spatial and temporal modes from simulation data, thereby reducing the parametric complexity even when training data are limited. A linear regression model is then established between time-variant material properties and temporal POD modes, which enables the accurate reconstruction of dynamic SAR distributions during the heating processes. Numerical validations using 3-D FDTD simulations of microwave heating with temperature-dependent Debye characteristics of the material properties show that the proposed method achieves a maximum temperature deviation of only 0.0095°C for a 32.65°C temperature rise in a 20-minute heating simulation. This corresponds to a 0.029% error while reducing the required number of EM simulations to 1/6 compared to conventional methods. This significant reduction in computational cost and high prediction accuracy highlights the potential of the proposed method as an effective alternative for coupled EM-thermal simulations in microwave heating applications. Point of delivery ; Electromagnetic heating ; Microwave ; Computer science ; Thermal ; Microwave heating ; Materials science ; Machine learning ; Electronic engineering ; Electrical engineering ; Engineering ; Physics ; Telecommunications ; Thermodynamics ; Electromagnetic coil Microwave-Assisted Synthesis and Applications
Authors: Tianshu Xu | Ran Wei | Yichen Deng | Ke Cao | Luheng Tang | Subhash C. Singh | Chunlei Guo
Source: Advanced Functional Materials (article, 2025-11-10)
Field: Materials Science — Surfaces, Coatings and Films

Substrate‐Independent Microdroplet Transport via Simple Superhydrophobic Tweezers Abstract Liquid droplet transfer is important for a wide range of applications, such as medical diagnostics, drug delivery, and microchemical reactors. Superhydrophobic surfaces have been used for droplet transfer. However, existing droplet‐transfer techniques remain substrate‐dependent or rely on special materials and complex external controls, greatly limiting their usability in various applications. In this study, a substrate‐independent droplet transfer technique based on simple materials and facile actuation mechanisms is developed by transforming common tweezer tips superhydrophobic using femtosecond laser processing. The tweezer tip superhydrophobicity and adhesion strength are controlled by varying the laser processing parameters and optimized through a machine learning‐assisted approach. In addition to the altered tweezer tip adhesion strength, the adhesion force can be controlled in real‐time by varying the tip separation. The superhydrophobic tweezers can transfer various types of liquids, including water and various biomedical solutions, over a wide range of volume (1–35 µL), independent of the substrate. The tweezers can make volume‐selective transfer or multiple‐droplet simultaneous transfer with no content loss or cross‐contamination (&lt;0.01 parts per billion). The superhydrophobic tweezers provide a simple, cost‐effective, and highly versatile solution for droplet transfer, with significant potential applications in pharmaceutical manufacturing, biomedical diagnostics, and biological research systems. Tweezers ; Optical tweezers ; Femtosecond ; Adhesion ; Laser ; Drug delivery ; Transfer printing Surface Modification and Superhydrophobicity ; Electrowetting and Microfluidic Technologies ; Nanofabrication and Lithography Techniques
Authors: Kapil Dev Sharma | Hrushikesh M. Gade | Neetu Kumari
Source: Physical Chemistry Chemical Physics (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

A theoretical study of oxygen anion diffusion through La and Ca doped SrTiO<sub>3</sub> lattice structures: molecular dynamics and ML approaches The diffusion of oxygen ions in doped perovskite materials is of crucial importance for applications such as solid oxide fuel cells and electrolyzers. This study employs a synergistic approach combining MD simulations and machine learning techniques to investigate the influence of Ca and La dopants on oxygen anion diffusion in A-site deficient SrTiO<sub>3</sub>. MD simulations were performed on SrTiO<sub>3</sub> super-cells with varying concentrations (5%, 15%, and 25%) of Ca and La dopants, both individually and in combination. Bulk oxygen anion diffusivity values were calculated from the mean squared displacement data obtained from the MD trajectories. <i>k</i>-Means clustering analysis was employed to identify localized clusters of oxygen anions and analyze their displacement patterns, shedding light on the impact of dopants on the surrounding oxygen anion migration. The results reveal that Ca doping enhances oxygen anion diffusivity, while La doping exhibits a more localized effect, restricting oxygen anion mobility in its vicinity due to increased cation-anion interactions. The co-doping of Ca and La leads to an intermediate behavior, with enhanced diffusion compared to La-doped systems but lower than Ca-doped systems. The study provides insights into the mechanisms governing oxygen anion diffusion in doped perovskites and highlights the potential of integrating computational approaches for optimizing materials design for energy-related applications. Doping ; Ion ; Oxygen ; Oxide ; Chemical physics ; Diffusion ; Perovskite (structure) ; Molecular dynamics ; Materials science ; Lattice (music) ; Fuel cells ; Chemistry ; Physical chemistry ; Nanotechnology ; Chemical engineering ; Crystallography ; Computational chemistry ; Thermodynamics ; Physics ; Optoelectronics ; Organic chemistry Advancements in Solid Oxide Fuel Cells
Authors: Weinan Zheng | Xinyang Li | Qiuyun Wang | Anmin Chen
Source: Physical Chemistry Chemical Physics (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Boosting thermoelectric performance of ferroelectric monolayer α-In<sub>2</sub>Se<sub>3</sub><i>via</i> strongly enhanced phonon scattering induced by site-specific Te doping The thermoelectric properties of ferroelectric monolayer α-In<sub>2</sub>Se<sub>3</sub> with site-specific Te doping are systematically investigated using first-principles calculations and on-the-fly machine-learning-assisted phonon transport simulations. Te substitution at different atomic layers leads to a substantial reduction in lattice thermal conductivity, primarily due to enhanced phonon scattering induced by mass contrast and local structural asymmetry. The electronic transport characteristics, including band dispersion, carrier effective mass, and Seebeck coefficient, remain largely unaffected, ensuring preserved power factor. As a combined result, the thermoelectric figure of merit (<i>zT</i>) increases from approximately 0.4 in the pristine structure to nearly 2.5 at 600 K in the optimally doped configuration. These findings demonstrate the impact of doping-site selectivity and data-driven anharmonic modeling on the thermoelectric performance of two-dimensional ferroelectric materials. Moreover, the top and bottom doping configurations are related by ferroelectric switching, enabling potential modulation of thermal transport and thermoelectric properties <i>via</i> polarization reversal. Thermoelectric effect ; Materials science ; Condensed matter physics ; Ferroelectricity ; Doping ; Thermoelectric materials ; Phonon scattering ; Seebeck coefficient ; Phonon ; Figure of merit ; Effective mass (spring–mass system) ; Monolayer ; Thermal conductivity ; Optoelectronics ; Nanotechnology ; Physics Advanced Thermoelectric Materials and Devices ; Perovskite Materials and Applications ; 2D Materials and Applications
Authors: Victor Ponce | Shaik Merkatur Hakim Marjuban | Ulises Martin | Heather Eich | Homero Castaneda
Source:  (article, 2025-04-06)
Field: Materials Science — Materials Chemistry

Predictive Modeling of Corrosion Evolution in Automotive Multifunctional Coatings Using Electrochemical Testing and Artificial Neural Networks Abstract The natural and induced harsh conditions such as temperature, humidity, rain, and de-icing salt, influence the lifetime performance of different parts of a vehicle. The coating/metallic substrate interface experiences various degradation stages during exposure to corrosion conditions. The corrosion performance of three different coating systems (Physical barrier (polymer-based), sacrificial (Nickel zinc), and the combination of both (hybrid)) used for automotive applications was studied. In this work, we used electrochemical and surface experimental data with mechanistic analysis to develop a mathematical framework. The understanding of the degradation mechanisms under steady-state conditions served to train a mathematical model that was later used as a predictive tool. The electrochemical impedance spectroscopy (EIS) technique was utilized to characterize the coating and substrate performance during steady-state, and to train an Artificial Neural Network (ANN) as an arrangement of Time Series Prediction (TSP). After a few days of testing, the ANN successfully predicted the coating performance over multiple years, where experimental results validated these estimates. Moving forward, accelerated methods and field data will be utilized to further test and validate the mathematical framework developed under laboratory conditions, allowing this approach as an effective predictive tool. Corrosion ; Artificial neural network ; Materials science ; Automotive industry ; Electrochemistry ; Computer science ; Metallurgy ; Engineering ; Artificial intelligence ; Electrode ; Chemistry ; Aerospace engineering Corrosion Behavior and Inhibition ; Non-Destructive Testing Techniques ; Smart Materials for Construction
Authors: Masahiro Oya | Yosuke Okamoto | Shinichi Nakazawa | Kotaro Maruyama | Yuichiro Yamazaki | Shinji Murakami | Yoshihiro Midoh | Noriyuki Miura
Source:  (article, 2025-02-21)
Field: Materials Science — Surfaces, Coatings and Films

Advanced pattern contour extraction function for see-through BSE images of high voltage SEM As semiconductor device structures become smaller and more complex, the need to measure not only single-layer patterns but also overlay across multiple layers has become increasingly critical. Traditionally, overlay has been managed by measurement on dedicated patterns. However, due to the growing complexity of manufacturing processes, it is now essential to measure overlay directly on actual device patterns. To address this challenge, TASMIT has developed a high-voltage scanning electron microscope (HV-SEM) system. Achieving accurate contour extraction of lower layer patterns from low contrast and low sharpness see-through BSE images, advanced processing technique is required. To this end, we have developed an advanced contour extraction method that integrates Active Contour Model with deep learning. In this study, we propose a method to generate a synthetic training dataset by leveraging the layout data of the sample, which captures the pattern variations and fluctuations observed in actual sample images. Training the contour extraction model with this synthetic dataset, we successfully obtained accurate overlay measurement results for a sample wafer containing multiple stacked layers in the back-end-of-line (BEOL) pattern, provided by imec. This paper presents the details of our method, evaluation results, and the unique advantages of SEM based die-to-database (D2DB) see-through overlay metrology. Extraction (chemistry) ; Function (biology) ; Voltage ; Computer science ; Feature extraction ; Artificial intelligence ; Computer vision ; Pattern recognition (psychology) ; Engineering ; Electrical engineering ; Chemistry ; Chromatography ; Biology ; Cell biology Electron and X-Ray Spectroscopy Techniques ; Advancements in Photolithography Techniques ; Non-Destructive Testing Techniques
Authors: Ganghui Dong | Tian Cui | Zihao Huo | Zhengtao Liu | Wenxuan Chen | Pugeng Hou | Yue‐Wen Fang | Defang Duan
Source: Materials Today Physics (article, 2025-11-17)
Field: Materials Science — Materials Chemistry

Machine-learning potentials for quantum and anharmonic effects in superconducting <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si1.svg"> <mml:mrow> <mml:mi mathvariant="bold-italic">F</mml:mi> <mml:mi mathvariant="bold-italic">m</mml:mi> <mml:mover accent="true"> <mml:mn mathvariant="bold">3</mml:mn> <mml:mo>‾</mml:mo> </mml:mover> <mml:mi mathvariant="bold-italic">m</mml:mi> </mml:mrow> </mml:math> LaBeH8 Anharmonicity ; Superconductivity ; Harmonic ; Ternary operation ; Quantum ; Tensor (intrinsic definition) ; Ab initio quantum chemistry methods Hydrogen Storage and Materials ; Inorganic Chemistry and Materials ; Rare-earth and actinide compounds
Authors: Menglin Gao | Xing Hu | Shengjun Xia | Yingjie Zhan | Li Xu | Shuai Li | Ziqiang Wang | Wei Liu | Qiulin Li
Source: Journal of Materials Research and Technology (article, 2025-05-01)
Field: Materials Science — Materials Chemistry

Quantitative research of multiscale microstructural evolution during creep of 800H alloy assisted by high-throughput characterization and deep learning The 800H alloy is widely used in critical components in high-temperature gas-cooled reactors (HTGR), where creep acts as one of the primary degradation mechanisms. To ensure the safe operation of HTGR, it is essential to understand the microstructural evolution during creep. Here, we introduced high-throughput scanning electron microscopy and deep learning techniques, integrated with various cross-scale characterization methods, to acquire quantitative microstructural information over large areas of the 800H alloy at different creep stages. By leveraging these quantitative data, we proposed multiscale microstructural evolution patterns and mechanisms during creep. Notably, regarding the intragranular precipitates, we uncovered a novel mechanism in which nano-Ti(C,N) precipitation and dissolution are closely associated with the evolution of dislocation loop debris. This mechanism underscores the critical role of nano-Ti(C,N) in sustaining creep resistance, a phenomenon challenging to capture using conventional characterization methods. This work provides an efficient and accurate approach for analyzing microstructures, which not only facilitates a profound understanding of the microstructural evolution during creep of 800H alloy but also establishes a robust foundation for developing microstructure-based performance prediction models and assessing creep damage. Furthermore, this approach is extendable to other types of damage and materials, demonstrating broad application potential. Materials science ; Creep ; Alloy ; Characterization (materials science) ; Throughput ; Metallurgy ; Nanotechnology ; Computer science Nuclear Materials and Properties ; High Temperature Alloys and Creep ; Microstructure and Mechanical Properties of Steels