jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Hamid Reza Sezavar | Saeed Hasanzadeh | Navid Fahimi
Source: Scientific Reports (article, 2025-09-29)
Field: Materials Science — Materials Chemistry

A novel hybrid mathematical deep learning technique for early warning of flashover in composite insulators This paper presents a novel approach based on a hybrid mathematical deep-learning model for flashover prediction of the polluted composite insulators. The proposed prediction method has been designed according to critical pre-flashover conditions, including the dry-band arcing (DBA) and flashover (FO) stages. The feature achievement is developed based on weighted continuous wavelet transform (WCWT) analysis that can discharge counting as a performance index according to real sample test leakage current (LC) data. It is shown that the application of the presented WCWT is superior to that of the typical wavelet analysis. Then, flashover prediction is achieved using the combination of a novel graph attention network (GAT) based on extracted indexes of the discharge peak number and power fitted on a Gaussian function. The developed prediction model can be used as an early-warning tool to prevent outages due to insulator failures. Validation of the presented model for predicting dry-band arcing and flashover is investigated for several parameters, such as the geometry specification of the samples and pollution levels. In addition, a flowchart is represented for online monitoring applications based on the proposed approach. Arc flash ; Insulator (electricity) ; Wavelet ; Electric arc ; Flowchart ; Wavelet transform ; Pattern recognition (psychology) ; Feature extraction High voltage insulation and dielectric phenomena ; Power Transformer Diagnostics and Insulation ; Electrical Fault Detection and Protection
Authors: Jiangbo Wen | Yanmin Jia | Yong Lü | Haijun Luo | Zhenwei Huang | Caiyin You | Zhanwen He | Xiaoyu Xu
Source: Processes (article, 2025-04-08)
Field: Chemistry — Analytical Chemistry

Progress in Wax Deposition Characteristics and Prediction Methods for High Pour Point and Viscous Crude Oil Water System With the continuous growth of global energy demand, the exploitation of deepwater oil and gas resources has become an important part of national energy strategies. The high-viscosity crude oil in deepwater areas such as the South China Sea poses severe challenges to oil and gas pipeline transportation due to its high pour point and high viscosity characteristics. Wax deposition, particularly significant under low temperature and high viscosity conditions, can lead to reduced pipeline flow rates, decreased transportation efficiency, and even potential safety hazards. Therefore, in-depth research on the wax deposition characteristics and mechanisms in high-viscosity systems holds significant theoretical and engineering application value. Current research primarily focuses on the influencing factors of wax deposition, deposition mechanisms, and the establishment of prediction models. Studies have shown that external factors such as temperature, shear intensity, operating time, and water content have significant effects on the wax deposition process. Specifically, increased temperature differences accelerate the deposition of wax molecules, while the presence of the aqueous phase inhibits wax crystallization and deposition. Furthermore, the formation mechanisms of wax deposition mainly include molecular diffusion, shear stripping, and aging effects. Researchers have explored the dynamic changes and influencing laws of wax deposition by establishing mathematical models combined with experimental data. In summary, although some progress has been made in studying the wax deposition characteristics in high-viscosity systems, research on wax deposition characteristics in mixtures, especially under the combined action of pour point depressants and flow improvers, is still inadequate. Future research should strengthen the systematic exploration of wax deposition mechanisms, quantify the effects of different external factors, and develop wax deposition prediction models suitable for practical engineering to ensure the safe and stable operation of deepwater oil and gas pipelines. Through in depth theoretical and experimental research, robust technical support can be provided for the efficient development of deepwater oil and gas resources. Wax ; Crude oil ; Petroleum engineering ; Deposition (geology) ; Pour point ; Environmental science ; Materials science ; Chemical engineering ; Geology ; Engineering ; Composite material ; Geomorphology Petroleum Processing and Analysis ; Enhanced Oil Recovery Techniques ; Hydrocarbon exploration and reservoir analysis
Authors: Ignas Pakamorė | Ross S. Forgan
Source: Digital Discovery (article, 2025-01-01)
Field: Chemistry — Organic Chemistry

Computation-guided exploration of the reaction parameter space of <i>N</i> , <i>N</i> -dimethylformamide hydrolysis Navigating the reaction parameter space can pose challenges, especially considering the exponential growth in the number of parameters even in seemingly straightforward chemical reactions or formulations. Dimethylformamide ; Computation ; Hydrolysis ; Space (punctuation) ; Computer science ; Parameter space ; Algorithm ; Chemistry ; Mathematics ; Statistics ; Organic chemistry Chemical Reaction Mechanisms ; Advanced Data Processing Techniques ; Various Chemistry Research Topics
Authors: Milad Nasiri | Yan Wang
Source: Crystals (article, 2025-05-09)
Field: Materials Science — Materials Chemistry

Evolution of Phonon Spectral Energy Density in Superlattice Structures Superlattices are a distinctive class of artificial nanostructures formed by the periodic stacking of two or more materials. The high density of interfaces in these structures often gives rise to exotic physical properties. In the context of thermal transport, it is well established that such interfaces can significantly scatter particle-like phonons while also inducing constructive or destructive interference in wave-like phonons, depending on the relationship between the phonons’ coherence lengths and the superlattice’s period thickness. In this work, we systematically investigate the effect of temperature on the spectral energy density of phonon modes in superlattices. Additionally, we examine how variations in superlattice period thickness influence phonon lifetimes and energy density. Our findings provide critical insights into the spectral phonon properties of superlattices, particularly in terms of their coherence and lifetimes. Superlattice ; Phonon ; Condensed matter physics ; Physics ; Materials science Advanced Thermoelectric Materials and Devices ; Thermal properties of materials ; Thermal Radiation and Cooling Technologies
Authors: Laurent Peltier | Léo Thiercelin | Fodil Meraghni
Source: Shape Memory and Superelasticity (article, 2025-03-11)
Field: Materials Science — Materials Chemistry

Design of (TiHfZr)(NiCoCu) High-Entropy Shape Memory Alloys: From Firstov's Experiments to Data-Driven Approach Abstract This paper deals with the design of (TiHfZr)(NiCoCu) high-entropy and high-temperature shape memory alloys (HE-HT-SMAs). It explains the chronology and the progress of this design starting from the experimental work of Georgi Firstov initiated in the 2015s until the advent of data-driven alloy approaches. A state-of-the-art (TiHfZr)(NiCoCu) HE-HT-SMA family is presented and enriched by a database used as input for a data-driven approach. The paper then focuses on the comparison of martensitic transformation temperatures provided by: (i) the experimental work of Firstov et al. started in 2015, (ii) other recent experimental studies and, (iii) those predicted by two numerical approaches. The first approach consists of a linear regression model proposed by Peltier et al., while the second one is proposed and enriched by Thiercelin et al. using a data-driven technique (random forest regression). The results from the data-driven approach yield accurate predictions that align with the experimental data from both the literature and previous studies. Thus demonstrating the importance of physics-informed, inspired techniques to optimize the design of future alloys, in particular HE-HT-SMAs. High entropy alloys ; Statistical physics ; Computer science ; Materials science ; Metallurgy ; Physics ; Alloy Shape Memory Alloy Transformations ; High Entropy Alloys Studies ; Titanium Alloys Microstructure and Properties
Authors: C. Murphy | Shaina Buksa | John Day | Argelia Felix-Lopez | Subin Antony Jose | Pradeep L. Menezes
Source: Processes (article, 2025-05-19)
Field: Materials Science — Materials Chemistry

Disruptive Manufacturing of Oxide Dispersion-Strengthened Steels for Nuclear Applications: Advances, Challenges, and Future Prospects Oxide dispersion-strengthened (ODS) steels are emerging as leading candidate materials for next-generation nuclear reactor components due to their exceptional resistance to creep, fatigue, and irradiation, combined with high strength at elevated temperatures. This paper investigates the microstructural mechanisms underpinning these superior properties, with a particular focus on the critical role of nano-oxides in enhancing performance. Various manufacturing techniques, including powder metallurgy and additive manufacturing, are reviewed to assess their impact on the structural and mechanical properties of ODS steels. Recent case studies on their application in nuclear environments highlight the potential of ODS steels to significantly extend component longevity without necessitating major reactor redesigns. Nevertheless, further research is necessary to assess their reliability under extreme environmental conditions and to determine optimal, scalable manufacturing processes for large-scale production. Materials science ; Oxide ; Dispersion (optics) ; Nanotechnology ; Forensic engineering ; Environmental science ; Engineering ; Metallurgy ; Physics Nuclear Materials and Properties ; Nuclear materials and radiation effects ; Fusion materials and technologies
Authors: Amine Fourari | Mouad Chakkour | Mohamed Ould Moussa | Ismail Khay | Tarak Ben Zineb
Source: Polymer Composites (review, 2025-06-02)
Field: Materials Science — Polymers and Plastics

A Comprehensive Review on Thermal and Hygro/Hydrothermal Aging of Plant Fiber Composites: Properties, Degradation Mechanisms, and Service Life Prediction ABSTRACT Recently, the growth of plant fiber‐reinforced composites (PFRCs) has a great impact on composite research and innovation. As these materials have various applications in several engineering fields, it would be of great interest to understand the multi‐physical properties of plant fiber composites under real‐life conditions. In this context, several efforts have been made to shed light on the multi‐physical properties of bio‐composites under synergetic aging environments. For this reason, the current review examines the hygro/hydrothermal and thermal aging of plant fiber polymer composites. Interestingly, the mechanical and physical (i.e., morphology chemical composition, crystallinity, thermal stability, moisture absorption…) properties of bio‐composites and their constituents are scrutinized to better understand material response under harsh environments. Several analytical models (i.e., Arrhenius, time–temperature superposition principle …) used for predicting the lifespan of the aforementioned materials are critically reviewed. This review paper is intended to provide new insights into the durability of plant fiber composites with a view to widespread industrial integration. More importantly, future directions are also provided to trigger new research areas. Materials science ; Composite material ; Hydrothermal circulation ; Service life ; Crystallinity ; Durability ; Fiber ; Context (archaeology) ; Composite number ; Thermal ; Material properties ; Arrhenius equation ; Engineering Natural Fiber Reinforced Composites ; Bamboo properties and applications ; Advanced Cellulose Research Studies
Authors: Moritz Gubler | Jonas A. Finkler | Stig Rune Jensen | Stefan Goedecker | Luca Frediani
Source: The Journal of Physical Chemistry A (article, 2025-01-27)
Field: Materials Science — Materials Chemistry

Noise-Tolerant Force Calculations in Density Functional Theory: A Surface Integral Approach for Wavelet-Based Methods We introduce a method for computing quantum mechanical forces through surface integrals over the stress tensor within the framework of the density functional theory. This approach avoids the inaccuracies of traditional force calculations using the Hellmann-Feynman theorem when applied to multiresolution wavelet representations of orbitals. By integrating the quantum mechanical stress tensor over surfaces that enclose individual nuclei, we achieve highly accurate forces that exhibit superior consistency with the potential energy surface. Extensive benchmarks show that surface integrals over the stress tensor offer a robust and reliable alternative to the direct use of the Hellmann-Feynman theorem for force computations in DFT with discontinuous basis sets, particularly in cases where wavelet-based methods are employed. In addition, we integrate this approach with machine learning techniques, demonstrating that the forces obtained through surface integrals are sufficiently accurate to be used as training data for machine-learned potentials. This stands in contrast to forces calculated using the Hellmann-Feynman theorem, which do not offer this level of accuracy. Wavelet ; Density functional theory ; Noise (video) ; Surface (topology) ; Mathematics ; Computer science ; Acoustics ; Statistical physics ; Physics ; Artificial intelligence ; Geometry ; Quantum mechanics Machine Learning in Materials Science ; Phase Equilibria and Thermodynamics ; High-pressure geophysics and materials
Authors: María Lara-Banda | Facundo Almeraya-Calderón | Jesús Manuel Jáquez-Muñoz | Demetrio Nieves-Mendoza | Miguel Ángel Baltazar-Zamora | Javier Olguín-Coca | Francisco Estupiñán-López | José Ángel Cabral Miramontes | Griselda Santiago-Hurtado | Citlalli Gaona-Tiburcio
Source: Metals (article, 2025-01-11)
Field: Materials Science — Metals and Alloys

A Study of the Corrosion Behavior of AHSS Complex-Phase CP 780 Employing an Electrochemical Noise Technique Analyzed by Different Methods The automotive industry employs structural steels with E-coats to reduce weight and increase the corrosion resistance of chassis, reducing CO2 emissions. Due to their mechanical properties, part of the chassis is a composite of advanced high-strength steels (AHSS). AHSSs are coated by conversion methods such as phosphate to increase epoxy coating adherence and corrosion resistance. The main point of this research is to characterize an AHSS complex-phase (CP) 780 in blank, with a phosphate coating and an E-coat organic coating using electrochemical noise, employing time-domain, frequency-domain, time–frequency-domain, and chaotic system methods to determine the type and corrosion kinetics of each system. The electrochemical noise technique was made with a conventional three-electrode cell, using a saturated calomel as a reference electrode. Data were recorded at 1024 s, at 1 data per second in a 3.5 wt. % NaCl electrolyte, according to ASTM G199-09. The results show how AHSS CP 780 presented uniform corrosion, similarly to the phosphate sample; however, the E-coat presented a trend of a localized process when analyzed by Wavelets transform. On the other hand, corrosion resistance increased for the E-coat sample, with values of 2.58 × 106 Ω·cm2. According to the results of the research, all the samples are susceptible to present localized corrosion. Electrochemical noise ; Corrosion ; Materials science ; Coating ; Saturated calomel electrode ; Galvanization ; Electrode ; Metallurgy ; Electrochemistry ; Composite material ; Reference electrode ; Chemistry Hydrogen embrittlement and corrosion behaviors in metals ; Corrosion Behavior and Inhibition ; Material Properties and Failure Mechanisms
Authors: Raouf Hassan | Mohammad Reza Kazemi
Source: Scientific Reports (article, 2025-10-10)
Field: Materials Science — Materials Chemistry

Accurate prediction of green hydrogen production based on solid oxide electrolysis cell via soft computing algorithms The solid oxide electrolysis cell (SOEC) presents significant potential for transforming renewable energy into green hydrogen. Traditional modeling approaches, however, are constrained by their applicability to specific SOEC systems. This study aims to develop robust, data-driven models that accurately capture the complex relationships between input and output parameters within the hydrogen production process. To achieve this, advanced machine learning techniques were utilized, including Random Forests (RFs), Convolutional Neural Networks (CNNs), Linear Regression, Artificial Neural Networks (ANNs), Elastic Net, Ridge and Lasso Regressions, Decision Trees (DTs), Support Vector Machines (SVMs), k-Nearest Neighbors (KNN), Gradient Boosting Machines (GBMs), Extreme Gradient Boosting (XGBoost), Light Gradient Boosting Machines (LightGBM), CatBoost, and Gaussian Process. These models were trained and validated using a dataset consisting of 351 data points, with performance evaluated through various metrics and visual methods. The dataset's suitability for model training was confirmed using the Monte Carlo outlier detection method. Results indicate that within the dataset and evaluation framework of this study, ANNs, CNNs, Gradient Boosting, and XGBoost models have demonstrated high accuracy and reliability, achieving the largest R-squared scores and the smallest error metrics. Sensitivity analysis reveals that all input parameters significantly influence hydrogen production magnitude. Game-theoretic SHAP values underline current and cathode electrode conditions as critical factors. This research determines the performance of machine learning models, particularly ANNs, CNNs, Gradient Boosting, and XGBoost, in predicting hydrogen production through the SOEC process. The outcomes of this paper can provide a certain reference for related research and applications in the hydrogen production field. Gradient boosting ; Hydrogen production ; Support vector machine ; Monte Carlo method ; Convolutional neural network ; Random forest ; Artificial neural network ; Stochastic gradient descent Advancements in Solid Oxide Fuel Cells ; Machine Learning in Materials Science ; Chemical Looping and Thermochemical Processes
Authors: Min Hao | Xingtai CAO | Jianying Sun | Yupeng Sun | Jiaxuan Wang | Hao Zhang
Source: Agriculture (article, 2025-12-29)
Field: Chemistry — Analytical Chemistry

Detection of External Defects in Seed Potatoes Using Spectral–Spatial Fusion of Hyperspectral Images and Deep Learning To improve the accuracy of detecting external defects in seed potatoes and address the reliance of current hyperspectral imaging methods on single-dimensional data, this study proposes a multi-dimensional spectral–spatial information fusion approach via concatenation based on a one-dimensional convolutional neural network (1DCNN) within the framework of deep learning. Hyperspectral three-dimensional data were acquired for normal seed potatoes and for samples presenting six types of external defects—decay, mechanical damage, wormhole, common scab, black scurf, and frostbite—across a wavelength range of 935–1721 nm. From the hyperspectral images, one-dimensional spectral data and two-dimensional spatial data were extracted. The one-dimensional spectral data were preprocessed using six methods: Savitzky–Golay smoothing (SG), standard normal variate (SNV), multiplicative scatter correction (MSC), first derivative (FD), second derivative (SD), and orthogonal signal correction (OSC). Feature wavelengths were subsequently selected through the successive projections algorithm (SPA) and competitive adaptive reweighted sampling (CARS), serving as inputs for traditional machine learning models. Two-dimensional spatial data were first subjected to dimensionality reduction via principal component analysis (PCA). Texture features were then extracted from each principal component using the gray-level co-occurrence matrix (GLCM). Following normalization, all spatial texture data were fused with the preprocessed spectral data to form the inputs for the deep learning models Basic1DCNN and Stacked1DCNN. The results demonstrate that the fusion data with the Stacked1DCNN model yielded the best performance in identifying normal seed potatoes and six types of external defects. The overall accuracy, precision, recall, F1 score, and mean average precision reached 98.77%, 98.77%, 98.93%, 98.73%, and 99.66%, respectively, outperforming traditional machine learning approaches. Compared with the Stacked1DCNN model trained using spectral data alone, these metrics improved by 2.81%, 2.78%, 3.20%, 3.01%, and 1.11%. This study offers theoretical and technical insights into the development of automated sorting and non-destructive detection systems for seed potatoes. Hyperspectral imaging ; Pattern recognition (psychology) ; Smoothing ; Principal component analysis ; Deep learning ; Dimensionality reduction ; Convolutional neural network ; Feature (linguistics) ; Sensor fusion Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Remote Sensing in Agriculture
Authors: Lan Yang | Xiao Zhou | Xiaowu Ni | Li Huang | Lianduan Zeng | Zhongyang Wang | Jun Song | Tongxiang Fan
Source: npj Computational Materials (article, 2025-05-31)
Field: Materials Science — Ceramics and Composites

Quantitative prediction of optical static refractive index in complex oxides The optical static refractive index, a critical intrinsic property of materials, plays a vital role in advanced optoelectronic applications. Accurate prediction of this index is essential for the efficient design and optimization of materials with tailored optical properties. Here, we present a robust predictive model that accurately forecasts the optical static refractive indices of complex oxides across diverse crystal structures and compositions. By leveraging chemical bond theory, our model elucidates the influence of intrinsic physical properties, including chemical bonds and d-electron bands, on the refractive index. Through rigorous analysis of 41 complex oxide systems and 5 doped systems, we demonstrate that our predictions align closely with experimental data, showcasing the model’s high accuracy and broad applicability. This work not only accelerates the development of novel materials and spectral design but also provides profound physical insights for optimizing and customizing optical properties. Refractive index ; Index (typography) ; Materials science ; Optics ; Computer science ; Optoelectronics ; Physics ; World Wide Web Glass properties and applications ; Photorefractive and Nonlinear Optics ; Transition Metal Oxide Nanomaterials