jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Dayu Fajrul Falaakh | Chi Bum Bahn
Source: npj Materials Degradation (article, 2025-12-11)
Field: Materials Science — Metals and Alloys

Machine learning for modeling intergranular stress corrosion cracking of stainless steels in light water reactors with uncertainty quantification and explainability Intergranular stress corrosion cracking (IGSCC) of austenitic stainless steels remains a critical degradation mechanism in light water reactors (LWRs), driven by coupled mechanical, electrochemical, and microstructural factors. Accurate prediction of crack growth rates (CGR) is essential for effective monitoring and mitigation. Here, we develop a comprehensive machine learning (ML) framework to model CGR in both boiling water reactors (BWRs) and pressurized water reactors (PWRs) using a curated database, integrating experimental measurements with ML-based and physics-informed preprocessing. Categorical boosting (CatBoost) was used for point prediction and uncertainty quantification (UQ) of CGR, and compared with natural gradient boosting (NGBoost), Gaussian process regression (GPR), and TabNet. CatBoost outperformed other models and, when combined with post-hoc calibration, provided reliable uncertainty estimates. Shapley Additive Explanations (SHAP) applied to CatBoost revealed both individual and interaction effects of features on CGR and enabled comparison of CGR behavior between BWR and PWR environments. Stress corrosion cracking ; Uncertainty quantification ; Intergranular corrosion ; Cracking ; Boiling water reactor ; Gradient boosting ; Corrosion ; Uncertainty analysis ; Random forest Hydrogen embrittlement and corrosion behaviors in metals ; Model Reduction and Neural Networks ; Nuclear Materials and Properties
Authors: Mohamed Askar | Mahmoud Abu El Ela | Ahmed H. El-Banbi | M.H. Sayyouh
Source: Journal of King Saud University - Engineering Sciences (article, 2025-10-28)
Field: Materials Science — Biomaterials

Development of machine learning models for predicting the deposition of sulfide scales in oil production wells Abstract Scale deposition in oil wells poses numerous operational challenges that can lead to blockage of the completion string; therefore, it is recommended to predict scale accumulation before its occurrence. The current study aims to develop Artificial Neural Network (ANN) models and other Ensemble Machine Learning (ML) models to detect the presence of sulfide scales in oil wells and estimate the percentage of the sulfide scale composition in the accumulated scale. The studied sulfide scales are zinc, lead, and iron sulfides. The investigated ML models are Artificial Neural Network, Decision Tree, Random Forest, Support Vector Machine, K-Nearest Neighbors, Gradient Boosting, Extreme Gradient Boosting, Adaptive Boosting, Light Gradient Boosting Machine, and Ridge Regression. The ML models were constructed using actual field data for lab-analyzed and physically collected scale samples. These samples were collected from 347 production wells in 17 fields over a 22-year period. The original database consists of 1486 scale deposition incidences, and it was randomly split into 80% for training and validation, and 20% for testing. The assigned input data for scale prediction involve the relevant surface and downhole data, including: water ion analysis, water pH, production rates of well fluids, and content of acid gases, downhole pressure, downhole temperature, and injection gas rate for gas-lifted wells. The ANN models were implemented in several field applications and yielded promising results compared to scale tendency calculations from commercial scale prediction software. Moreover, variant models of Gradient Boosting and KNN models showed the highest model accuracy in predicting sulfide scale percentage. Artificial neural network ; Gradient boosting ; Deposition (geology) ; Scale (ratio) ; Sulfide ; Predictive modelling ; Support vector machine ; Oil field Calcium Carbonate Crystallization and Inhibition ; Enhanced Oil Recovery Techniques ; Hydraulic Fracturing and Reservoir Analysis
Authors: Omar Zahhaf | Giulia D’Ambrogio | François Grasland | Guilhem Rival | Minh‐Quyen Le | Pierre‐Jean Cottinet | Jean‐Fabien Capsal
Source: Ceramics (article, 2025-10-09)
Field: Materials Science — Ceramics and Composites

Hydrothermal Synthesis Optimization of High-Aspect Ratio α-Al2O Microfibers for Thermally Conductive Soft Composites This work presents a comprehensive study on the synthesis and application of Al2O3 fibers derived from an ammonium aluminum carbonate hydroxide (AACH) precursor. Through a hydrothermal route, the influence of critical synthesis parameters, including aluminum nitrate and urea concentrations, reaction temperature and time, and stirring conditions, on fiber morphology and aspect ratio was systematically investigated. The as-synthesized AACH fibers were subsequently converted into thermodynamically stable α-alumina fibers via controlled annealing. These high-aspect ratio alumina fibers were incorporated into polydimethylsiloxane (PDMS) to produce electrically insulating, thermally conductive composites. The thermal performance of fiber-filled composites was benchmarked against that of particle-filled counterparts, with the former exhibiting significantly enhanced thermal conductivity. Furthermore, the dielectrophoretic alignment of alumina fibers led to an additional increase in thermal conductivity, underlining the importance of high-aspect ratio fillers. This study uniquely combines the controlled synthesis of alumina fibers with their incorporation and alignment in a polymer matrix, presenting a novel and effective approach for engineering anisotropic, thermally conductive, and electrically insulating composite materials. Dielectrophoretic alignment of α-Al2O3 fibers synthesized through optimized hydrothermal conditions and incorporated into PDMS composites deliver over 95 % higher thermal conductivity than spherical fillers. Microfiber ; Hydrothermal synthesis ; Hydrothermal circulation ; Fiber ; Composite number ; Aspect ratio (aeronautics) ; Polydimethylsiloxane ; Thermal conductivity Advanced ceramic materials synthesis ; Thermal properties of materials ; Additive Manufacturing and 3D Printing Technologies
Authors: Yoshifumi Amamoto | Hiroteru Kikutake | Ken Kojio | Atsushi Takahara | Kei Hirose | Maiya Hori | Kei Terayama
Source: Research Square (preprint, 2025-11-27)
Field: Materials Science — Materials Chemistry

Data-Driven Process–Structure–Property Framework for the Design, Understanding, and Prediction of Sustainable Polymers Process (computing) ; Curse of dimensionality ; Dimensionality reduction ; Key (lock) ; Sustainable development ; Reduction (mathematics) ; Predictive modelling Machine Learning in Materials Science ; Polymer crystallization and properties ; biodegradable polymer synthesis and properties
Authors: Brandi Ransom | Riccardo Bosio | Dmitry Yu. Zubarev | James L. Hedrick | Nathaniel H. Park
Source: npj Computational Materials (article, 2025-09-24)
Field: Materials Science — Biomaterials

High-throughput computational analysis of kinetic barriers to ring-closing depolymerization for aliphatic polycarbonates Abstract The chemical reversion of polymers via ring-closing depolymerization (RCD) to their monomeric constituents is a highly promising avenue to enable end-of-life recycling and reuse. However, most reported systems using RCD revolve around bespoke monomer designs to facilitate facile depolymerization, and there exists relatively few investigations into the influence of functional groups on the ability of a particular monomer to cleanly undergo depolymerization. Here, we perform computational investigations into the energy barriers for RCD of 6-membered aliphatic carbonates in different solvents. The results corroborate trends observed in prior experimental studies, validating the utility of computational investigations towards understanding RCD. Experimental evaluation of the thermal depolymerization in two of the studied polycarbonates confirmed their ability to undergo RCD. Overall, this work highlights the advantage of high-throughput energy barrier computations to provide meaningful insight into broad reactivity trends that would be highly laborious to access experimentally. Depolymerization ; Monomer ; Reactivity (psychology) ; Polymer ; Work (physics) ; Bespoke biodegradable polymer synthesis and properties ; Polymer crystallization and properties ; Carbon dioxide utilization in catalysis
Authors: Zigeng Huang | Zhen Guo | Changsu Cao | Hung Q. Pham | Xuelan Wen | George H. Booth | Ji Chen | Dingshun Lv
Source: Nature Communications (article, 2025-10-21)
Field: Materials Science — Materials Chemistry

A multi-resolution systematically improvable quantum embedding scheme for large-scale surface chemistry calculations Predictive simulation of surface chemistry is critical in fields from catalysis to electrochemistry and clean energy generation. Ab-initio quantum many-body methods should offer deep insights into these systems at the electronic level but are limited by their steep computational cost. Here, we build upon state-of-the-art correlated wavefunctions to reliably reach 'gold standard' accuracy in quantum chemistry for extended surface chemistry. Efficiently harnessing graphics processing unit acceleration along with systematically improvable multi-resolution techniques, we achieve linear computational scaling up to 392 atoms. These large-scale simulations demonstrate the importance of converging to these extended system sizes, achieving consistency between simulations with different boundary conditions for the interaction of water on a graphene surface. We provide a benchmark for this water-graphene interaction that clarifies the preference for water orientations at the graphene interface. This is extended to the adsorption of carbonaceous molecules on chemically complex surfaces, including metal oxides and metal-organic frameworks, where we consistently achieve chemical accuracy compared to experimental references. This advances the simulation of molecular adsorption on surfaces, enabling reliable and improvable first-principles modeling of such problems by ab-initio quantum many-body methods. Quantum chemistry ; Graphene ; Scaling ; Embedding ; Quantum ; Electronic structure ; Wave function ; Molecule ; Benchmark (surveying) ; Linear scale Machine Learning in Materials Science ; Spectroscopy and Quantum Chemical Studies ; Quantum and electron transport phenomena
Authors: Xiuxia Yang | Ting Wang | Lei Zhao | Xuhui Xu
Source: Advanced Science (article, 2025-12-08)
Field: Materials Science — Materials Chemistry

Recent Progress in Mechanoluminescence for Multi‐Dimensional Stress Monitoring Mechanoluminescence (ML) materials with their unique stress-to-light conversion capability, have been driving the evolution of stress sensing technology from single-point detection to multi-dimensional imaging. Recent advances in material defect engineering, ion doping, and heterostructure design have significantly improved the luminosity, response kinetics, and environmental stability of ML materials. These enhancements have established a technical foundation for multimodal stress visualization across diverse application scenarios. To systematically analyse the stress response mechanism and visual behavior of materials across different spatial dimensions, this review offers in-depth discussions on material design principles, device integration methods, and application scenarios for 0D, 1D, 2D, and 3D stress sensing technologies. It further proposes targeted solutions to common challenges in multi-dimensional stress sensing, such as material performance, device integration synergy, and data processing. Finally, the development path of multi-dimensional stress visualization technology will be looked forward with higher spatial resolution in intelligent diagnosis, biomechanics, and extreme environmental monitoring, providing a theoretical framework and technical support for multi-dimensional stress visualization detection. Mechanoluminescence ; Visualization ; Stress (linguistics) ; Environmental stress ; System integration Luminescence Properties of Advanced Materials ; Advanced Sensor and Energy Harvesting Materials ; Luminescence and Fluorescent Materials
Authors: Isaac Toda‐Caraballo | Jan Wróbel | D. Nguyen-Manh
Source: npj Computational Materials (article, 2025-10-10)
Field: Materials Science — Materials Chemistry

Unified generalized universal equation of states for magnetic Co, Cr, Fe, Mn and Ni: an approach for non-collinear atomistic modelling Abstract Despite great efforts to study magnetic properties of 3d-transition metals from both fundamental and applied interest, there exists no modelling approach that would be able to describe magnetic and structural phase stability of all these elements on a unified formalism. In this work, we propose a qualitative improvement of the Generalisation of the Universal Equation of States (GUES) that we presented recently in a previous work developed and tested for cubic structures in Fe. The GUES is now extended to other 3d-transition magnetic elements and crystal lattices, where now magnetic Co, Cr, Mn, and Ni are considered, including both cubic and hexagonal structures, and also covering ferromagnetic (FM) and antiferromagnetic (AFM) configurations. An extensive database has been developed and used to fit all parameters and functions for all considered elements. The current GUES unifies the two previous separate approaches for FM and AFM configurations, allowing for non-collinear calculations, which are tested for Co, Cr, Fe, Mn and Ni. The approach is consistent with the Stoner model of band magnetism and the Ginzburg-Landau approximation used in the magnetic cluster expansion method, as well as with non-collinear magnetism described in the Heisenberg-Landau Hamiltonians. Importantly, it also includes magneto-volume effects, which are important for understanding defect properties in magnetic materials. This work permits considering the development of a new class of magnetic interatomic potentials for non-collinear simulations based on the approach proposed by the GUES. (The figures shown in this article can be seen in colour only in the electronic version). Magnetism ; Ferromagnetism ; Antiferromagnetism ; Stability (learning theory) ; Work (physics) ; Cluster (spacecraft) ; Magnetic field ; Interatomic potential X-ray Diffraction in Crystallography ; Rare-earth and actinide compounds ; High-pressure geophysics and materials
Authors: Ashish K. Sarangi | Lizaranee Tripathy | Himansu Bhusan Samal | Itishree Jogamaya Das | Satyajit Panda | Ram Babu | Anuj Kumar Tripathi | Azaj Ansari | Sumit Sahil Malhotra | Ranjan K. Mohapatra | Sanjeeb Kumar Tripathy | Biswajit Mishra
Source: Discover Chemistry. (article, 2025-12-22)
Field: Materials Science — Materials Chemistry

Critical narrative review of degradation mechanisms and corrosion resistance in nonmetallic materials The degrading mechanisms of non-metallic materials, such as polymers, ceramics, and composites, are not as well understood as those of metals, despite their increasing use in the chemical, pharmaceutical, aerospace, and marine sectors. In order to scientifically assess corrosion resistance and preventative techniques in non-metallic systems, this review compiles data from over 100 experiments conducted between 2000 and 2024. Laboratory studies indicate that green inhibitors, smart coatings, and nanocomposites can reduce corrosion rates, in some cases approaching 90%, though performance varies significantly depending on material type, environmental conditions, and testing protocols. Computational modeling is increasingly applied to design multifunctional inhibitors and protective materials, but its predictive reliability under complex service conditions remains under evaluation. Adapting these technologies to real-world service contexts remains a major problem, even as development focuses on creating pathways for sustainability and application-specific durability. This synthesis outlines potential directions for multidisciplinary research toward more efficient and ecologically sustainable corrosion prevention strategies for non-metallic materials, while recognizing that practical implementation will require further validation. Corrosion ; Multidisciplinary approach ; Reliability (semiconductor) ; Sustainability ; Narrative review ; Service (business) Corrosion Behavior and Inhibition ; Effects and risks of endocrine disrupting chemicals ; Polymer Science and PVC
Authors: Nurullah Kartaloğlu | Okan UÇAR
Source: Journal of Advanced Research in Natural and Applied Sciences (article, 2025-12-31)
Field: Materials Science — Polymers and Plastics

Evaluation of the Effect of Oil Types Used in Alkyd Resin Synthesis on Application Potentials Using the DFT Approach Alkyd resins are one of the most widely used classes of film-forming polymers, such as paints and coatings. The degree of unsaturation, fatty acid composition, and molecular structure of the oil used in resin synthesis determine the film formation rate, hardness, yellowing resistance, and overall performance of the resin. Tall Oil Fatty Acid (TOFA) contains a high percentage of oleic/linoleic acid, while hemp seed oil is rich in linoleic/linolenic acid. This study investigates the thermodynamic stability and potential performance differences of alkyd resins based on these two oil sources using density functional theory (DFT)-based theoretical calculations. The calculated quantum and thermodynamic parameters provide comprehensive information on the resins' electronic structures and potential reactivity properties, providing valuable preliminary information into the application potential of bio-based alkyd resins. The findings provide a crucial foundation for the design and development of sustainable materials. Alkyd ; Density functional theory ; Quantum chemical ; Edible oil ; Reactivity (psychology) ; Fatty acid Polymer composites and self-healing ; Epoxy Resin Curing Processes ; Lignin and Wood Chemistry
Authors: Huajian Yu | Peng Lü | Zihao Gao | Álvaro Blanco | Miguel Á. Camblor
Source: Dalton Transactions (article, 2025-01-01)
Field: Chemistry — Inorganic Chemistry

An imidazolium dication affords a stable UWY-like zeolite and enables a subtle structure direction towards EMM-17 Zeolites with interconnected medium (10Ring) and large (12R) pores, such as the <b>UWY</b> framework type, hold significant potential for catalysis but may suffer from instability due to a high germanium content required for their synthesis. Addressing this limitation, we report the synthesis of HPM-9, a novel <b>UWY</b>-like germanosilicate zeolite, utilizing 1,1'-(1,8-octanediyl)bis(3-methylimidazolium) (8BMI) as the organic structure-directing agent (OSDA) in fluoride media. HPM-9 achieves significantly lower germanium content (Ge<sub>f</sub> = Ge/(Ge + Si) down to 0.14) compared to the original IM-20 (Ge<sub>f</sub> = 0.3), resulting in markedly improved stability upon calcination and exposure to water. Structural analysis using synchrotron powder X-ray diffraction (SPXRD) suggested disorder and DIFFaX simulations revealed that HPM-9 exhibits minimal stacking faults along the <i>a</i>-axis, corresponding to an intergrowth with a polymorph where double four-membered rings (<i>d4r</i>) are partially replaced by single four-membered rings (<i>s4r</i>), likely driven by the reduced Ge content. Furthermore, decreasing the Ge content further (Ge<sub>f</sub> ≤ 0.05) subtly shifts the structure direction towards EMM-17, a zeolite featuring interconnected 11R and 10R pores. <sup>19</sup>F MAS NMR suggests the presence of <i>d4r</i> units in the synthesized EMM-17, and we propose a mechanism where the less stable <i>d4r</i>-containing polymorph C nucleates first, templating the subsequent growth of <i>d4r</i>-free polymorphs A and B by epitaxial intergrowth. Comparative studies with analogous OSDAs (7BMI and 9BMI) confirmed the optimal linker length of 8BMI for directing towards stable <b>UWY</b> structures. The enhanced stability of HPM-9 opens avenues for the practical application of <b>UWY</b>-type zeolites in catalysis. Dication ; Zeolite ; Chemistry ; Materials science ; Computational chemistry ; Catalysis ; Molecule ; Organic chemistry Zeolite Catalysis and Synthesis ; Chemical Synthesis and Characterization ; Catalytic Processes in Materials Science
Authors: Jawad Ahmed | Imdad Ali | Sajad Hussain | Abdul Latif | Qadir Baksh | Waqar Ahmed | Muhammad Ahmed
Source: Sukkur IBA Journal of Emerging Technologies (article, 2025-06-03)
Field: Materials Science — Polymers and Plastics

OPTIMIZING FUEL CELL MEMBRANE EFFICIENCY THROUGH COMPARATIVE THERMAL ANALYSIS OF HYDROGEN FUEL VARIANTS Abstract This study investigates the thermal behavior of polymer electrolyte membrane (PEM) fuel cells using hydrogen and hydrogen methanol fuels. An extensive 3D model was constructed for the simulation of the temperature, current density, and thermal efficiency distribution, with Nafion EW1100 membranes under high-temperature conditions using COMSOL Multiphysics. Moreover, this study highlights the essential connection between temperature profiles and the performance of the entire fuel cell. However, at a given voltage of 0.4 V and 0.8 V, hydrogen consistently operated at lower temperatures between the Gas Diffusion Layer (GDL), Gas Diffusion Electrode (GDE), and the PEM compared to the hydrogen methanol fuel. For instance, at 0.4 V hydrogen temperatures 4-5 K lower than that of hydrogen, and methanol at 0.8 V, this difference increased to 4-6 K. The temperature differential is indicative of hydrogen's ability to manipulate its heat-generating and dissipating processes more efficiently than PET. This demonstrates hydrogen’s advantages over other fuel cells because the current density correlates with temperature. For all temperatures, hydrogen provides higher current densities than hydrogen-methanol, supporting its usefulness in improving fuel cell efficiency. This increased thermal management not only improves the thermal efficiency of the fuel cell but also prolongs PEM, GDL, and GDE life by decreasing the thermal stress. Hence, from this analysis, it is shown that hydrogen fuel contributed to better thermal management causing superior performance and greater lifetime for PEMFC. This could be more efficient fuel cell systems for the application of advanced membrane technology such as Nafion EW1100. Materials science ; Actuator ; Composite material ; Computer science ; Artificial intelligence Polymer Science and PVC ; Dielectric materials and actuators