jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Sanka N. Atapattu | Colin F. Poole
Source: Journal of Chromatography A (article, 2025-04-25)
Field: Chemistry — Spectroscopy

Influence of descriptor database selection on modeling retention factors in capillary micellar and microemulsion electrokinetic chromatography using the solvation parameter model Chemistry ; Micellar electrokinetic chromatography ; Chromatography ; Electrokinetic phenomena ; Microemulsion ; Solvation ; Selection (genetic algorithm) ; Capillary electrophoresis ; Capillary action ; Artificial intelligence ; Organic chemistry ; Thermodynamics Analytical Chemistry and Chromatography ; Microfluidic and Capillary Electrophoresis Applications ; Spectroscopy and Chemometric Analyses
Authors: M. Liu | Ling Liu | Hasan Sh. Majdi | Ibrahim Albaijam | Meldi Suhatril | Dalia Helkam Rouchio | Yose Sedghin Gutierez | Riadh Marzouki | H. Asilzodhe
Source: Separation and Purification Technology (article, 2025-06-10)
Field: Chemistry — Inorganic Chemistry

Advanced inorganic membranes for water purification: FeZr-MOF/GO composites with optimized adsorption performance Adsorption ; Membrane ; Composite material ; Materials science ; Chemical engineering ; Chemistry ; Organic chemistry ; Engineering Metal-Organic Frameworks: Synthesis and Applications ; Nanomaterials for catalytic reactions ; Adsorption and biosorption for pollutant removal
Authors: Alexey Semenchuk | Nikolay Kondratyuk | Kirill M. Gerke | Ilia Kopanichuk
Source: Colloids and Surfaces A Physicochemical and Engineering Aspects (article, 2025-07-15)
Field: Materials Science — Materials Chemistry

Towards reproducible wetting studies: Automated contact angle determination by molecular simulations Wetting ; Contact angle ; Materials science ; Nanotechnology ; Chemical engineering ; Chemistry ; Composite material ; Engineering Material Dynamics and Properties ; Surface Modification and Superhydrophobicity ; Block Copolymer Self-Assembly
Authors: Finn Snow | Stephanie E. Doyle | Emily Liu | Dale Rauch | Darcy Millett | Jasley Wilding-McBride | Magdalena Kita | Elena Pirogova | Robert M. I. Kapsa | Anita Quigley
Source: Biofabrication (article, 2025-08-14)
Field: Materials Science — Biomaterials

A detailed guide to melt electro-writing for tissue engineering applications Melt electro-writing (MEW) is an advanced 3D printing technique with significant potential in tissue engineering due to its ability to create highly precise microscale structures using biocompatible materials. This review provides a comprehensive guide to the principles, process parameters, and recent advancements in MEW technology, with a specific focus on its applications in tissue engineering. We explore the core mechanisms behind MEW, including the influence of material selection, nozzle temperature, voltage, and feed rate on scaffold architecture. The review examines both computational and experimental modelling of process parameters and their impact on resolution capabilities, including pore size, thickness, and achievable diameters, alongside their effects on cellular behaviour such as adhesion, proliferation, and differentiation. We also discuss the fabrication of custom MEW devices, the integration of machine learning, and the use of automated design tools to enhance scaffold precision and customization. Furthermore, we address key challenges limiting the widespread adoption of MEW, such as the high cost of commercially available devices and the complexity of building custom machines, while offering strategies to overcome these barriers. Recent<i>in vitro</i>and<i>in vivo</i>studies are discussed, demonstrating the promising potential of MEW in tissue regeneration, particularly in bone, cartilage, and soft tissue engineering. This review aims to serve as a valuable resource for researchers and practitioners working in the field of tissue engineering, offering insights into the capabilities, challenges, and future directions of MEW in advancing regenerative medicine. Scaffold ; Tissue engineering ; Biocompatible material ; Computer science ; Nanotechnology ; Personalization ; Limiting ; Process (computing) ; Microscale chemistry ; Regenerative medicine ; Systems engineering ; Materials science ; Biochemical engineering ; Mechanical engineering ; Biomedical engineering ; Engineering ; Stem cell Electrospun Nanofibers in Biomedical Applications ; Additive Manufacturing and 3D Printing Technologies ; Electrohydrodynamics and Fluid Dynamics
Authors: Harshada Mhetre | Sagar Dhanraj Pande | Babita Singla | Pavan Hiremath | S.N. Sahu | Sarvesh Sorte | Ketan Kotecha | Nithesh Naik
Source: Discover Applied Sciences (article, 2025-04-11)
Field: Materials Science — Biomaterials

Data driven modeling of TiO2 PVP nanofiber diameter using LSTM and regression for enhanced functional performance Abstract The prospective utilization of electrospun nanofibers across diverse fields has elicited substantial scientific attention. Nevertheless, managing their diameter remains problematic due to the intricate interactions among electrospinning variables. This research explores the application of Long Short-Term Memory (LSTM) networks and multiple regression models to forecast the diameters of Titanium Dioxide (TiO₂) and Polyvinyl pyrrolidone (PVP) nanofibers, facilitating improved process regulation and enhancement. TiO₂ + PVP nanofibers were fabricated under diverse conditions, including changes in applied voltage, solution concentration, and distance between tip to collector. The acquired data underwent analysis using LSTM and regression models to assess their predictive capabilities. The outcomes revealed that both approaches effectively estimated nanofiber diameters; however, the regression model surpassed LSTM with a lower error rate of 0.077% compared to 0.305%. This indicates that while LSTM captures nonlinear relationships, conventional regression models yield more precise predictions in this scenario. These findings underscore the potential of machine learning in advancing electrospinning technology by minimizing trial-and-error experiments and boosting nanofiber production efficiency. The incorporation of artificial intelligence-driven modeling into the electrospinning process sets the stage for more accurate control over fiber morphology, resulting in enhanced material properties and expanded applications in biomedical, environmental, and energy sectors. Nanofiber ; Regression ; Materials science ; Regression analysis ; Computer science ; Composite material ; Artificial intelligence ; Chemical engineering ; Machine learning ; Mathematics ; Statistics ; Engineering Electrospun Nanofibers in Biomedical Applications ; Advanced Sensor and Energy Harvesting Materials ; Conducting polymers and applications
Authors: Hongyeun Kim | Maziar Montazerian | Nicholas Clark | A.E. Schoch | Theresa Senti | Alexander Frey | Markus Rampf | John C. Mauro
Source: Journal of the American Ceramic Society (article, 2025-07-16)
Field: Materials Science — Ceramics and Composites

Machine learning‐based microstructure identification in lithium silicate dental glass‒ceramics Abstract This study explores the application of machine learning (ML) to identify and quantify the microstructure of lithium silicate based dental glass‒ceramics (DGCs), a material at the forefront of prosthetic dental applications due to its good mechanical properties and aesthetics. Despite the increasing demand for LS‐based DGCs, the design process has seen little innovation, largely due to the complexity of the potential glass compositions and the associated microstructures. This research builds on previous efforts to model microstructure of glass‒ceramics by utilizing scanning electron microscopy (SEM) and X‐ray diffraction data from 10 commercial LS‐based DGCs. The microstructures were analyzed after various heat treatments, and ML techniques, including Weka segmentation, were employed to classify and label the phases within the SEM images. These labeled images were then used as inputs for an ML algorithm aimed at identifying each microstructure, paving the way for further optimization of DGCs through advanced computational methods. Microstructure ; Materials science ; Lithium (medication) ; Scanning electron microscope ; Ceramic ; Glass-ceramic ; Silicate glass ; Silicate ; Lithium disilicate ; Metallurgy ; Composite material ; Machine learning ; Mineralogy ; Computer science ; Chemical engineering ; Psychology ; Chemistry ; Engineering Glass properties and applications ; Mineralogy and Gemology Studies ; Recycling and utilization of industrial and municipal waste in materials production
Authors: P.L. Narayana | Saurabh Tiwari | Anoop Kumar Maurya | Muhammad Ishtiaq | Nokeun Park | N.S. Reddy
Source: Metals (article, 2025-05-28)
Field: Materials Science — Materials Chemistry

Quantitative and Qualitative Analysis of Atmospheric Effects on Carbon Steel Corrosion Using an ANN Model This study develops an artificial neural network (ANN) model to predict the corrosion rate of carbon steel under a wide range of atmospheric conditions. The model incorporates input variables, including temperature (−3.1–28.2 °C), relative humidity (33.3–91.1%), time of wetness (0.003–0.976), precipitation (13–4656 mm), sulfur dioxide (0–68.2 mg/m2·d), and chloride concentrations (0 to 359.8 mg/m2·d). The model demonstrated excellent predictive capability and reliability, with R2 values of 97.2% and 77.6% for the training and testing datasets, respectively. The model demonstrated a strong predictive performance, with an R2 of 97.2% for the training set and 77.6% for the test set. It achieved a mean absolute error (MAE) of 5.633 μm/year for training and 18.86 μm/year for testing, along with a root mean square error (RMSE) of 0.000055, indicating reliable generalization despite the limited dataset size. The analysis showed that the relative humidity had the most significant impact on the corrosion rate. The practical applications of the model extend to optimizing material selection and devising effective maintenance strategies. Carbon steel ; Corrosion ; Qualitative analysis ; Metallurgy ; Carbon fibers ; Materials science ; Composite material ; Qualitative research ; Sociology ; Composite number ; Social science Corrosion Behavior and Inhibition ; Wind and Air Flow Studies ; Hydrogen embrittlement and corrosion behaviors in metals
Authors: Kai Ren | Zilong Zhuang | Ke Wang | Yu Wei | Jianping Li | Huabing Shu
Source: The Journal of Physical Chemistry C (article, 2025-08-19)
Field: Materials Science — Materials Chemistry

Machine Learning Speeds up Predicting the Mechanical Properties of SiC Nanophononic Heterostructures This work investigates the mechanical properties of a silicon carbide (SiC) nanophononic heterostructure (NPH) tuned by temperatures and pore geometries, using molecular dynamics (MD) simulations in conjunction with the machine learning method. The SiC NPH, constructed with pure SiC and phononic crystal pores, exhibits temperature-dependent fracture behavior, showing a decrease in mechanical strength decreasing as the temperature increases from 50 to 500 K. The fracture strength and strain are markedly influenced by pore size, with a particular emphasis on pore length, whereas pore width exerts a negligible influence. In addition, armchair interfaces can cause a higher mechanical strength compared with zigzag interfaces. The presence of larger phononic pores amplifies stress concentrations and thermal effects, consequently resulting in more significant reductions in mechanical performance. More importantly, a random forest model has been developed to accurately predict fracture characteristics of the SiC NPH, achieving a high degree of precision (R2 = 0.99) and offering a 600-fold increase in computational speed compared to traditional MD methods. These findings provide valuable insights for designing robust SiC-based nanodevices with tunable mechanical properties. Heterojunction ; Materials science ; Engineering physics ; Artificial intelligence ; Composite material ; Computer science ; Optoelectronics ; Engineering Graphene research and applications ; Thermal properties of materials ; Boron and Carbon Nanomaterials Research
Authors: Wenlong Wang | Zhe Wu | Dominic Peters | Berkay Çıtmacı | Carlos G. Morales‐Guio | Panagiotis D. Christofides
Source: Digital Chemical Engineering (review, 2025-04-25)
Field: Chemistry — Electrochemistry

Machine learning in modeling, analysis and control of electrochemical reactors: A tutorial review Electrochemical reactors play a critical role in various industrial sectors, including energy storage, chemical production, and environmental engineering. The complexity of these systems – arising from coupled electrochemical reactions with mass, heat and charge transport phenomena – poses significant challenges in modeling, analysis, and control. Machine learning (ML) has emerged as a promising tool for addressing these challenges by providing data-driven solutions to complex process modeling, optimization, and advanced control. This tutorial review explores the state-of-the-art applications of ML in electrochemical reactor systems, including ML-based modeling techniques and ML-based advanced control strategies, followed by the discussions of practical challenges and their solutions. An electrochemical carbon dioxide (CO 2 ) reduction reactor is used as a demonstration example to show the effectiveness of various modeling and control methods. In addition, an integrated data infrastructure platform is presented for the digitalization and control of the electrochemical CO 2 reduction reactor. By identifying current gaps and future opportunities, this article provides a roadmap for leveraging ML tools to improve the analysis and operation of electrochemical reactors. Computer science ; Control (management) ; Electrochemistry ; Artificial intelligence ; Chemistry ; Electrode Electrochemical Analysis and Applications ; Advancements in Solid Oxide Fuel Cells ; Membrane-based Ion Separation Techniques
Authors: Arnaud Allera | Thomas D. Swinburne | Alexandra M. Goryaeva | Baptiste Bienvenu | Fabienne Ribeiro | Michel Perez | Mihai‐Cosmin Marinica | David Rodney
Source: Nature Communications (article, 2025-09-24)
Field: Materials Science — Materials Chemistry

Activation entropy of dislocation glide in body-centered cubic metals from atomistic simulations The activation entropy of dislocation glide, a key process controlling the strength of many metals, is often assumed to be constant or linked to enthalpy through the empirical Meyer-Neldel law-both of which are simplified approximations. In this study, we take a more direct approach by calculating the activation Gibbs energy for kink-pair nucleation on screw dislocations of two body-centered cubic metals, iron and tungsten. To ensure reliability, we develop machine learning interatomic potentials for both metals, carefully trained on dislocation data from density functional theory. Our findings reveal that dislocations undergo harmonic transitions between Peierls valleys, with an activation entropy that remains largely constant, regardless of temperature or applied stress. We use these results to parameterize a thermally-activated model of yield stress, which consistently matches experimental data in both iron and tungsten. Our work challenges recent studies using classical potentials, which report highly varying activation entropies, and suggests that simulations relying on classical potentials-widely used in materials modeling-could be significantly influenced by overestimated entropic effects. Nucleation ; Dislocation ; Entropy (arrow of time) ; Enthalpy ; Work (physics) ; Jump Machine Learning in Materials Science ; Advanced Materials Characterization Techniques ; Ion-surface interactions and analysis
Authors: E Li | Shabnam J. Semnani
Source: Computer Methods in Applied Mechanics and Engineering (article, 2025-07-24)
Field: Materials Science — Polymers and Plastics

Molecular dynamics enabled data-driven modeling of constitutive behavior and failure in composite materials Modeling the constitutive and damage behavior of composite materials is challenging due to their complex multiscale composition and the difficulty of experimental characterization at small scales. Molecular dynamics (MD) simulations offer detailed insights into the mechanical behavior of materials at the atomic scale. However, existing challenges of MD simulation of composites include high computational costs and selection of effective force fields. In this work, we develop an MD-enabled surrogate modeling (metamodeling) framework that simultaneously captures rate-dependent and path-dependent constitutive behavior and detects failure at the atomic scale. We model each constituent and their interface separately to reflect their distinct behaviors. We discuss the selection of appropriate force fields in order to produce comparable results among constituents. Time step information is embedded in the data prior to model training, enabling the model to infer the rate-dependent response solely based on the given strain path. Multi-task Gated recurrent unit (GRU)-based neural networks are developed to capture rate- and path-dependent constitutive response and failure. We demonstrate the proposed methodology using glass fiber-reinforced epoxy composite as an example, and train the proposed GRU-based model on MD simulation data. The robustness and generalization capability of the models are demonstrated through comparisons with MD simulation results with unseen loading patterns. Constitutive equation ; Molecular dynamics ; Composite number ; Materials science ; Statistical physics ; Structural engineering ; Computer science ; Composite material ; Engineering ; Finite element method ; Physics ; Chemistry ; Computational chemistry Polymer crystallization and properties ; Elasticity and Material Modeling ; High-Velocity Impact and Material Behavior
Authors: Harpriya Minhas | Rahul Kumar Sharma | Biswarup Pathak
Source: The Journal of Physical Chemistry Letters (article, 2025-05-13)
Field: Materials Science — Materials Chemistry

Exploring Combined Stereochemically Active Lone-Pair and Rattling Effects in Thermoelectrics with Machine Learning Potentials Stereochemically active lone pairs (SCALPs) are recognized for their ability to break local symmetry, induce lattice anharmonicity, and influence thermoelectric properties. Similarly, rattling atoms influence the thermal conductivity by introducing additional vibrational modes that disrupt phonon transport. SCALPs containing pnictogen chalcogenides alongside rattling atoms pose challenges for calculating thermal transport properties using <i>ab initio</i> methods because of their noncentrosymmetric structures. Here, we employ machine learning interatomic potentials (MLIPs) to explore the combined effects of SCALPs and rattling atoms in AAsSe<sub>2</sub> (A = Li or Na). The strong anharmonicity in the γ-NaAsSe<sub>2</sub> system arises from rattling modes and active As 4s<sup>2</sup> SCALP-induced electrostatic interactions, leading to reduced lone-pair angles. The unique chemical bonding behind the high anharmonicity is attributed to antibonding states near the valence band and the flat vibrational behavior of rattler atoms, induced phonon softening, structural distortions, and enhanced phonon scattering. This work highlights the combined effects of SCALPs and rattling atoms, leveraging MLIPs to accelerate thermoelectric material design. Lone pair ; Thermoelectric materials ; Thermoelectric effect ; Materials science ; Chemistry ; Physics ; Thermodynamics ; Quantum mechanics Machine Learning in Materials Science ; Advanced Thermoelectric Materials and Devices ; Advanced Chemical Physics Studies
Authors: Hasan Can Gülbalkan | Alper Uzun | Seda Keskın
Source: Carbon Capture Science & Technology (article, 2025-01-21)
Field: Chemistry — Inorganic Chemistry

Assessing CO2 separation performances of IL/ZIF-8 composites using molecular features of ILs Given the vast number and diversity of metal-organic frameworks (MOFs) and ionic liquids (ILs), it is impractical to experimentally test the gas adsorption and separation potential of each one of the possible IL/MOF composites formed by the different combinations of these two components. In this study, we developed a comprehensive computational approach integrating Conductor-like Screening Model for Realistic Solvents (COSMO-RS) calculations, density functional theory (DFT) calculations, Grand Canonical Monte Carlo (GCMC) simulations, and machine learning (ML) algorithms to evaluate a wide variety of IL-incorporated ZIF-8 composites for CO 2 separations. We examined 1322 different types of IL/ZIF-8 composites, covering the largest variety of ILs studied to date (8 cations and 35 anions) at various loadings, for flue gas separation and natural gas purification. We simulated CO 2 , CH 4 , and N 2 adsorption properties of these composites and used this high-quality molecular simulation data to develop ML models that can predict gas uptakes of any IL/ZIF-8 composite when chemical and structural features of the IL are given. The accurate prediction power of these ML models was shown by comparing their estimates with the experimental and simulation data. Our approach significantly accelerates the assessment of a very large number of IL/ZIF-8 composites and reveals the key molecular features of ILs to make composites for achieving superior gas separation performance. Composite material ; Separation (statistics) ; Materials science ; Computer science ; Machine learning Metal-Organic Frameworks: Synthesis and Applications ; Membrane Separation and Gas Transport ; Carbon Dioxide Capture Technologies
Authors: Naïma Sayahi | Bouthaina Othmani | Wissem Mnif | Zaina Algarni | Moncef Khadhraoui | Faouzi Ben Rebah
Source: Surfaces (review, 2025-07-13)
Field: Materials Science — Materials Chemistry

Microbial Extracellular Polymeric Substances as Corrosion Inhibitors: A Review Microbial extracellular polymeric substances (EPSs) are emerging as sustainable alternatives to conventional corrosion inhibitors due to their eco-friendly nature, biodegradability, and functional versatility. Secreted by diverse microorganisms including bacteria, fungi, archaea, and algae, EPSs are composed mainly of polysaccharides, proteins, lipids, and nucleic acids. These biopolymers, chiefly polysaccharides and proteins, are accountable for surface corrosion prevention through biofilm formation, allowing microbial survival and promoting their environmental adaptation. Usually, EPS-mediated corrosion inhibitions can take place via different mechanisms: protective film formation, metal ions chelation, electrochemical property alteration, and synergy with inorganic inhibitors. Even though efficacious EPS corrosion prevention has been demonstrated in several former studies, the application of such microbial inhibitors remains, so far, a controversial topic due to the variability in their composition and compatibility toward diverse metal surfaces. Thus, this review outlines the microbial origins, biochemical properties, and inhibition mechanisms of EPSs, emphasizing their advantages and challenges in industrial applications. Advances in synthetic biology, nanotechnology, and machine learning are also highlighted and could provide new opportunities to enhance EPS production and functionality. Therefore, the adoption of EPS-based corrosion inhibitors represents a promising strategy for environmentally sustainable corrosion control. Extracellular polymeric substance ; Corrosion ; Extracellular ; Chemistry ; Environmental chemistry ; Biology ; Biochemistry ; Biofilm ; Bacteria ; Organic chemistry Corrosion Behavior and Inhibition ; Antimicrobial agents and applications ; Building materials and conservation