jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Stefano Rodinò | Giuseppina Rota | Matteo Chiodo | Alberto Corigliano | Carmine Maletta
Source: Micromachines (review, 2025-06-30)
Field: Materials Science — Materials Chemistry

Artificial Intelligence Control Methodologies for Shape Memory Alloy Actuators: A Systematic Review and Performance Analysis Shape Memory Alloy (SMA) actuators are pivotal in modern engineering due to their unique thermomechanical properties, but their inherent non-linearities, hysteresis, and temperature sensitivity pose significant control challenges. This systematic review evaluates artificial intelligence (AI)-based control methodologies to address these limitations, analyzing their efficacy in enhancing precision, adaptability, and reliability for SMA and Magnetic SMA (MSMA) systems. A PRISMA-guided literature review (2003-2025) identified 24 studies, which were categorized by control architectures (hybrid AI-linear, pure AI, adaptive, and model predictive control) and evaluated through quantitative metrics, including Root Mean Square Error (RMSE%) and a weighted scoring system for experimental rigor. Results revealed hybrid AI-linear controllers as the dominant approach (36%), with online-trained neural networks achieving superior accuracy (+2.4%) over offline methods. Feedforward neural networks outperformed recurrent architectures (+3.1%), while Model Predictive Control (MPC) excelled for SMA actuators (+5.8% accuracy) but underperformed for MSMAs (-7.7%). Sensorless strategies proved advantageous for MSMAs (+5.0%), leveraging intrinsic material properties like electrical resistance for state estimation. The analysis underscores AI's capacity to mitigate hysteresis and non-linear dynamics, though material-specific optimization is critical: SMA systems favor dynamic control and MPC, whereas MSMAs benefit from sensorless AI and pure neural networks. Challenges persist in computational demands for online training and reinforcement learning's exploration-exploitation trade-offs. Future research should prioritize adaptive algorithms for fatigue compensation, lightweight AI models for embedded deployment, and standardized benchmarking to bridge material-specific performance gaps. This synthesis establishes AI as a transformative paradigm for SMA actuation, enabling precise control in aerospace, biomedical, and soft robotics applications. SMA* ; Model predictive control ; Artificial neural network ; Computer science ; Benchmarking ; Actuator ; Artificial intelligence ; Feed forward ; Machine learning ; Control engineering ; Mean squared error ; Control (management) ; Engineering ; Algorithm ; Mathematics Shape Memory Alloy Transformations ; Aeroelasticity and Vibration Control ; Piezoelectric Actuators and Control
Authors: Chong Zhang | Chaofeng Zhao | Huize Cui | Bo Wang | Chumeng Luo | Ruilu Guo | Shuo Chen | Wenwen Gu | Wenpeng Li
Source: Polymers (article, 2025-01-03)
Field: Materials Science — Materials Chemistry

Molecular Dynamics Study on Thermal Conductivity Properties and Dielectric Behaviors of Graphene-Based Epoxy Resin Nanocomposites In order to increase the thermal conductivity of neat epoxy resin and broaden its practical application in high-voltage insulation systems, we have constructed four kinds of epoxy resin nanocomposite models (a neat epoxy resin (EP), a graphene-doped epoxy resin nanocomposite (EP/GR) and hydroxyl- or carboxyl-functionalized graphene-doped epoxy resin nanocomposites (EP/GR-OH or EP/GR-COOH)) to systematically investigate their thermodynamic and electrical properties using molecular dynamics (MD) simulations. Compared with the EP model, carboxyl-functionalized graphene particles enhanced the thermal conductivity of the EP/GR-COOH model by 66.5% and increased its <i>T<sub>g</sub></i> by 26.6 K. Furthermore, the dielectric constant of the EP/GR-COOH model was significantly reduced. To investigate the intrinsic mechanism, the lowest fraction of free volume (13.22%) and the largest number of hydrogen bonds (102.2) in the EP/GR-COOH model were identified as playing essential roles for its excellent thermodynamic properties and favorable electrical performance. The present study provides a molecular-level understanding of the satisfactory thermodynamic and electrical properties of the EP/GR-COOH nanocomposite, which will aid in designing novel epoxy resin nanocomposite materials with high thermal conductivity. Epoxy ; Graphene ; Nanocomposite ; Materials science ; Dielectric ; Thermal conductivity ; Molecular dynamics ; Composite material ; Polymer nanocomposite ; Polymer ; Chemical engineering ; Nanotechnology ; Chemistry ; Computational chemistry High voltage insulation and dielectric phenomena ; Thermal properties of materials ; Dielectric materials and actuators
Authors: S. Girish | M. Vinay | D. Saravana Bavan | R. Ahmed | P. M. G. Bashir Asdaque | M. Revanasiddappa | P. Dandapani | Y. P. Ravitej
Source: Discover Applied Sciences (article, 2025-10-22)
Field: Materials Science — General Materials Science

Evaluation of thermal properties of epoxy composites filled by nanofiller materials The integration of nanofillers in epoxy matrices is found to be a very effective way to increase the thermal properties of thermosetting polymers, which are very popular. This survey cumulates the latest findings concerning the influence of nanofillers on the thermal behaviour of epoxy composites and is, therefore, an indispensable source of information for materials scientists and engineers who are interested in the design of these materials for their specific applications. Neat epoxy resins come with some difficulties such as brittleness, low thermal conductivity, and large thermal expansion coefficient, which can be easily solved by adding nanofillers. Nanofillers like carbon-nanotubes, graphenesilica nanoparticles, and metal oxides have displayed the capacity to bring substantial improvements to the thermal conductivity, as well as the thermal and dimensional stabilities of epoxy composites. The outcome of the above-mentioned nanocomposites outperforms well the neat-epoxy-based ones, which makes nanofillers applicable to a multitude of new high-tech sectors including but not exclusive to electronics thermal management, aerospace heat resistance, and high-temperature operation of automotive parts. The challenges stem from the fact that uniform nanofiller dispersion is still out of reach, constant interfacial adhesion is yet to be optimized, and cost and scalability issues persist. Only after these obstacles are tackled can we be sure that the potential of nanofillers in epoxy composites is fully exploited. It is suggested that the future research should be conducted to transform these challenges, thus enabling nanofillers’ full potential realization through the usage of these materials over increasingly extended periods and lead the path to their extension in the wide spectrum of applications. This review delivers the first unified, side-by-side evaluation of thermal, mechanical, and electrical properties for a broad spectrum of epoxy–nanofiller systems under consistent processing criteria, and proposes an integrative framework to map emerging trends, critical challenges, and future research directions in epoxy nanocomposites. Epoxy ; Thermosetting polymer ; Thermal ; Aerospace ; Nanocomposite ; Thermal expansion ; Electronics ; Dispersion (optics) Material Properties and Applications ; Tribology and Wear Analysis ; Advanced materials and composites
Authors: Kazem Emdadi | Majid Gandomkar | Ali Aranizadeh | Behrooz Vahidi | Mirpouya Mirmozaffari
Source: Sensors (article, 2025-11-20)
Field: Materials Science — Materials Chemistry

Overview of Monitoring, Diagnostics, Aging Analysis, and Maintenance Strategies in High-Voltage AC/DC XLPE Cable Systems High-voltage (HV) cable systems-particularly those insulated with cross-linked polyethylene (XLPE)-are increasingly deployed in both AC and DC applications due to their excellent electrical and mechanical performance. However, their long-term reliability is challenged by partial discharges (PD), insulation aging, space charge accumulation, and thermal and electrical stresses. This review provides a comprehensive survey of the state-of-the-art technologies and methodologies across several domains critical to the assessment and enhancement of cable reliability. It covers advanced condition monitoring (CM) techniques, including sensor-based PD detection, signal acquisition, and denoising methods. Aging mechanisms under various stressors and lifetime estimation approaches are analyzed, along with fault detection and localization strategies using time-domain, frequency-domain, and hybrid methods. Physics-based and data-driven models for PD behavior and space charge dynamics are discussed, particularly under DC conditions. The article also reviews the application of numerical tools such as FEM for thermal and field stress analysis. A dedicated focus is given to machine learning (ML) and deep learning (DL) models for fault classification and predictive maintenance. Furthermore, standards, testing protocols, and practical issues in sensor deployment and calibration are summarized. The review concludes by evaluating intelligent maintenance approaches-including condition-based and predictive strategies-framed within real-world asset management contexts. The paper aims to bridge theoretical developments with field-level implementation challenges, offering a roadmap for future research and practical deployment in resilient and smart power grids. This review highlights a clear gap in fully integrated AC/DC diagnostic and aging analyses for XLPE cables. We emphasize the need for unified physics-based and ML-driven frameworks to address HVDC space-charge effects and multi-stress degradation. These insights provide concise guidance for advancing reliable and scalable cable assessment. Software deployment ; Reliability (semiconductor) ; Condition monitoring ; Fault detection and isolation ; Bridge (graph theory) ; Field (mathematics) ; Fault (geology) ; Predictive maintenance ; Asset management High voltage insulation and dielectric phenomena ; Electrical Fault Detection and Protection ; Power Transformer Diagnostics and Insulation
Authors: Karina Dragasiute | Gediminas Monastyreckis | Daiva Zeleniakienė
Source: Polymers (article, 2025-04-25)
Field: Materials Science — Polymers and Plastics

Electrically Conductive Nanoparticle-Enhanced Epoxy Adhesives for Localised Joule Heating-Based Curing in Composite Bonding This study investigates the application of carbon nanotube (CNT)-enhanced epoxy adhesives for localised Joule heating-based curing in composite bonding. The electrical, thermal, and mechanical properties of epoxy with 0.25-1 wt% CNT loadings were evaluated. A simple CNT alignment method using DC voltage showed improved electrical conductivity, greatly reducing the percolation threshold. Transient thermal analysis using finite element modelling of representative volume elements revealed that aligned CNTs led to increased localised temperatures near the CNT clusters. The model was validated with infrared thermal imaging analysis, which also showed similar non-linear heat distribution and more uniform heating under higher CNT loading. Additionally, power distribution mapping was evaluated through inverse modelling techniques, suggesting different conductivity zones and cluster distribution within the single-lap joint. The numerical and experimental results demonstrated that CNT alignment significantly enhanced localised conductivity, thereby improving curing efficiency at lower voltages. The lap shear test results showed a peak shear strength of 10.16 MPa at 0.5 wt% CNT loading, 9% higher than pure epoxy. Scanning electron microscopy analysis confirmed the formation of aligned CNT clusters, and how CNT loading affected the failure modes, transitioning from cohesive to void-rich fracture patterns at a higher wt%. These findings establish CNT-enhanced Joule heating as a viable and scalable alternative for efficient composite bonding in aerospace and structural applications. Epoxy ; Materials science ; Curing (chemistry) ; Adhesive ; Composite material ; Joule heating ; Composite number ; Nanoparticle ; Electrical conductor ; Electrically conductive ; Adhesive bonding ; Nanotechnology ; Layer (electronics) Synthesis and properties of polymers ; Epoxy Resin Curing Processes ; Nanomaterials and Printing Technologies
Authors: Jianguang Xu | Yongsheng Li | Yue Huang | Guojian Yuan | Zhonglu Cao | W.F. Zhang | Heping Zheng | Yun Zang | Xingtai Mao | Mengmeng Li
Source: Buildings (article, 2025-04-30)
Field: Materials Science — Ceramics and Composites

Understanding the Role of Quartz Powder Content and Fineness on the Micro-Structure and Mechanical Performance of UHPC To enhance the microstructure and mechanical properties and to optimize the formulations of high-strength and high-flow ultra-high-performance concrete (UHPC), the effects of different quartz powder contents, fineness, and curing temperatures of UHPC were systematically studied. It was hypothesized that using the appropriate content and fineness of quartz powder can improve the microstructure of UHPC and, thus, improve its mechanical properties, especially at higher curing temperatures. To test this hypothesis, the flowability, compressive strength, flexural strength, and tensile strength of UHPC mixtures with different quartz powder dosages (0%, 15%, 30%) and fineness (4 µm, 8 µm), cured at 20 °C, 45 °C, and 90 °C, were investigated. The results indicate that as the dosage of quartz powder increases and the particle size decreases, the flowability of UHPC decreases. The compressive strength of UHPC first increases and then decreases with an increase in quartz powder dosage. Finer quartz powder usage considerably enhances packing density and pore structure. When the content of quartz powder is 15%, UHPC achieves optimal mechanical properties and pore structure, showing an improvement of 3.6% to 14.4% compared to UHPC with a coarse particle size. Additionally, an increase in curing temperature leads to the consistent growth of the compressive strength of UHPC. Under 90 °C steam curing, UHPC incorporating 15% fine quartz powder (4 μm) achieved a peak compressive strength of 182.1 MPa, increased by 19.8% compared to that under 20 °C, which is attributed to the enhanced pozzolanic activity of fine quartz powder. These findings provide valuable guidance for the mix design of UHPC in precast concrete components, long-span bridges, and marine structures, where high early strength and durability are critical. Fineness ; Quartz ; Materials science ; Composite material ; Content (measure theory) ; Metallurgy Advanced ceramic materials synthesis ; Advanced materials and composites ; Powder Metallurgy Techniques and Materials
Authors: Minhi Han | Tetsuya Yokoo | Jin-Yong Park | Kenichi Oyaizu | Sungnam Park
Source: Chemical Engineering Journal (article, 2025-08-05)
Field: Materials Science — Materials Chemistry

Deep learning prediction of ionic conductivity in polymer electrolytes using hierarchical polymer graphs Polymer ; Electrolyte ; Conductivity ; Polymer electrolytes ; Ionic conductivity ; Ionic bonding ; Materials science ; Chemical engineering ; Polymer science ; Computer science ; Chemistry ; Ion ; Composite material ; Engineering ; Organic chemistry ; Electrode ; Physical chemistry Machine Learning in Materials Science ; Fuel Cells and Related Materials ; Computational Drug Discovery Methods
Authors: Qi Zhang | Rui Yang | Zhengran Wang | Yi-Fan Li | Fangbing Dong | Junjie Liu | Shenglin Xiong | Aimin Zhang | Jinkui Feng
Source: Energy storage materials (article, 2025-04-15)
Field: Materials Science — Materials Chemistry

Machine-learning-assisted design of cathode catalysts for metal-sulfur/oxygen/carbon dioxide batteries Metal-sulfur/oxygen/carbon dioxide batteries, which are promising high-energy power systems, all suffer from the drawback of slow reaction kinetics in cathode reactions, resulting in suboptimal battery performance. Cathode catalysts can effectively accelerate reaction kinetics, thereby enhancing battery performance. However, challenges remain in catalyst screening, and there is an unclear understanding of catalytic mechanisms. Machine learning offers a rapid approach to screening efficient catalysts and deeply exploring the mechanism of catalysis, making it a promising tool for advancing catalyst development. Nowadays, comprehensive reviews on the role of machine learning in aiding the development of cathode catalysts for metal-sulfur/oxygen/carbon dioxide batteries are rare. This review systematically summarizes the application of machine learning in cathode catalysts and presents some perspectives for future research. This review may be useful for developing Metal-sulfur/oxygen/carbon dioxide batteries and related areas. A systematic review with regard to application of machine learning in the screening of cathode catalysts for metal-sulfur/oxygen/carbon dioxide battery systems. Due to its efficiency and the ability to establish relationships between input data and outputs, machine learning can be used to deeply explore the reaction mechanisms of catalysts. Currently, machine learning has been applied to various cathode catalysts, including transition metal catalysts, single-atom catalysts, dual-metal site catalysts, and alloy catalysts, and so on cathode catalyst, machine learning, metal-sulfur/oxygen/carbon dioxide battery, review and perspective. Materials science ; Cathode ; Catalysis ; Oxygen ; Carbon dioxide ; Sulfur ; Metal ; Chemical engineering ; Sulfur dioxide ; Inorganic chemistry ; Metallurgy ; Organic chemistry ; Electrical engineering ; Engineering Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Advancements in Battery Materials
Authors: Henrik Klein Moberg | Giuseppe Abbondanza | Ievgen I. Nedrygailov | David Albinsson | Joachim Fritzsche | Christoph Langhammer
Source: Nature Communications (article, 2025-08-05)
Field: Materials Science — Materials Chemistry

Deep-learning-enabled online mass spectrometry of the reaction product of a single catalyst nanoparticle Extracting weak signals from noise is a generic challenge in experimental science. In catalysis, it manifests itself as the need to quantify chemical reactions on nanoscopic surface areas, such as single nanoparticles or even single atoms. Here, we address this challenge by combining the ability of nanofluidic reactors to focus reaction product from tiny catalyst surfaces towards online mass spectrometric analysis with the high capacity of a constrained denoising auto-encoder to discern weak signals from noise. Using CO oxidation and C<sub>2</sub>H<sub>4</sub> hydrogenation on Pd as model reactions, we demonstrate that the catalyst surface area required for online mass spectrometry can be reduced by ≈ 3 orders of magnitude compared to state of the art, down to a single nanoparticle with 0.0072 ± 0.00086 μm<sup>2</sup> surface area. These results advocate deep learning to improve resolution in mass spectrometry in general and for online reaction analysis in single-particle catalysis in particular. Catalysis ; Mass spectrometry ; Nanoparticle ; Nanoscopic scale ; Nanotechnology ; Noise (video) ; Particle (ecology) ; Focus (optics) ; Materials science ; Chemistry ; Computer science ; Physics ; Chromatography ; Organic chemistry ; Artificial intelligence Machine Learning in Materials Science ; Mass Spectrometry Techniques and Applications ; Catalytic Processes in Materials Science
Authors: Dang‐Hyok Yoon | Jaekyu Lee | Sang-Yub Lee
Source: Applied Sciences (article, 2025-03-26)
Field: Materials Science — Materials Chemistry

Deep Learning-Driven Molecular Generation and Electrochemical Property Prediction for Optimal Electrolyte Additive Design Recently, generative models have rapidly advanced and are being applied to various domains beyond vision and large language models (LLMs). In the field of chemistry and molecular generation, deep learning-based models are increasingly utilized to reduce experimental exploration and research costs. In this study, we conducted research on Variational Autoencoder-based molecular generation and property prediction to screen for optimal molecules in the design of electrolyte additives for lithium-ion batteries. Using a dataset composed of promising electrolyte additive candidate molecules, we generated new molecules and predicted HOMO and LUMO values, which are key factors in electrolyte additive design. For approximately 1000 newly generated electrolyte additive candidate molecules, we performed DFT calculations to obtain HOMO and LUMO values and calculated the mean absolute error (MAE) between the predicted values from the trained model and the DFT-calculated values. As a result, the model demonstrated exceptionally low errors of approximately 0.04996 eV (HOMO) and 0.06895 eV (LUMO), respectively. This means that battery experts can receive recommendations for new molecules, refer to their predicted HOMO and LUMO values, and select potential electrolyte additives for further validation through experiments. By replacing the traditional electrolyte additive development process with deep learning models, this method has the potential to significantly reduce the overall development time and improve efficiency. Electrolyte ; Electrochemistry ; Materials science ; Computer science ; Nanotechnology ; Chemistry ; Electrode Machine Learning in Materials Science ; Fuel Cells and Related Materials ; Electrocatalysts for Energy Conversion
Authors: Shiyan Wang | Chaopeng Liu | Wen Sheng Hao | Xianjun Zhu | Xianghong Niu | Dongwei Ma | Longlu Wang | Qiang Zhao
Source: Journal of Energy Chemistry (article, 2025-11-27)
Field: Materials Science — Materials Chemistry

Machine learning accelerates the design and discovery of single-atom catalysts for electrochemical reactions Workflow ; Key (lock) ; Rational design ; Catalysis ; Bridging (networking) ; Sustainable energy Machine Learning in Materials Science ; CO2 Reduction Techniques and Catalysts ; Electrocatalysts for Energy Conversion
Authors: Hannes Stagge | Robert Güttel
Source: Chemical Engineering Journal (article, 2025-03-17)
Field: Materials Science — Materials Chemistry

The findability of microkinetic parameters by heterogeneous chemical reaction neural networks (hCRNNs) Finding microkinetic parameters for heterogeneously catalyzed processes with conventional methods is a challenging task. Recently, the use of artificial neural networks has been described as a promising and flexible tool for kinetic parameter estimation. In this work, an extension to the methodology of chemical reaction neural networks (CRNNs) to heterogeneously catalyzed reaction networks (hCRNNs) is proposed. The developed network architecture encapsulates physically interpretable layers for the Arrhenius expression, coverage dependency, and power-law terms encountered in a typical microkinetic model and accounts for possible reversibility of all elementary step reactions in the mechanism. Thus, it is fully interpretable and acts as a drop-in replacement for a conventional kinetic expression. The methodology is further examined on a prototypical heterogeneously catalyzed reaction mechanism under transient conditions and various operational and kinetic regimes. This work offers a framework for quantifying network errors and interpreting its predictions as well as a systematic overview assessing its ability to identify kinetic parameters. It is found that kinetic behavior is generally described very well by the network. Additionally, kinetic discovery is possible for the fastest reaction in the mechanism, if observed. A link between the results and the transient regime is established. With this, the design of suitable hCRNNs training strategies becomes possible. • hCRNNs are an extension of Chemical Reaction Neural Networks to heterogeneous catalysis. • Fully physically interpretable neural network architecture based on microkinetics. • hCRNNS discover kinetic behavior from differential transient kinetic data. • Kinetic parameters identified for the dominating reactions observed. Chemistry ; Computer science Machine Learning in Materials Science ; Fuel Cells and Related Materials ; Neural Networks and Applications