jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Danzer, Michael A. | Plank, Christian | Rüther, Tom
Source: EPub Bayreuth (University of Bayreuth) (article, 2024-01-01)
Field: Chemistry — Electrochemistry

Electrochemical system analysis from impedance data to system identification Summary Electrochemical impedance spectroscopy is a non-destructive experimental technique for the operando characterization of electrochemical systems. Here, we develop electrochemical system analysis (ELSA) as a data-driven, systems-theoretical approach to the analysis of measured impedance spectra. It solves the problem of finding an interpretable model description that explains given impedance data with high accuracy and without prior model assumptions. ELSA adopts the comprehensive analysis of the generalized distribution of relaxation time analysis and builds upon the data-driven and regularization-free Loewner method. ELSA systematically interprets the transfer function resulting from the Loewner method to reliably identify serial elements and resistive-capacitive and resistive-inductive processes with characteristic time constants. It also finds resonant elements by identifying conjugate complex poles. Based on the Shannon entropy of the residuals and the curvature of the locus, ELSA automatically and reproducibly identifies the minimal model order, thus avoiding overfitting. System characterization results are presented for different types of electrochemical power sources: batteries, fuel cells, and double-layer capacitors. Electrical impedance ; Transfer function ; Dielectric spectroscopy ; System identification ; Curvature ; Entropy (arrow of time) ; Identification (biology) ; Equivalent circuit Electrochemical Analysis and Applications ; Fuel Cells and Related Materials ; Advanced Battery Technologies Research
Authors: Elizabeth R. Belden | Avery Brown | Randy Paffenroth | Nikolaos Kazantzis | Michaël T. Timko
Source: Industrial & Engineering Chemistry Research (article, 2024-10-11)
Field: Materials Science — Materials Chemistry

Data-Driven Discovery of Reaction Pathways for Modeling Catalytic Cracking of Hydrocarbons Modeling complex chemical reactions is a long-standing challenge in chemical reaction engineering. Group type analysis is commonly used for reducing the complexity of the reaction model while retaining sufficient chemical information for a particular application. This study evaluates a data-driven approach based on mathematical similarity as a new tool for identification of groups for use in group type reaction models. Data for dodecane cracking in the supercritical state over Zeolite Socony Mobil-5 (ZSM-5) was used as a test system. Mathematical similarity analysis of the raw data differentiated as many as five different groups. Adding synthetic data did not affect the groups identified by similarity analysis, indicating that the separation was not limited by the number of data points. Scaling or normalizing the data improved the separation of the similarity analysis. To test the data-identified groups, different types of reaction models were generated systematically, kinetic parameters regressed, and the resulting predictions compared with experimental data. The resulting reaction models consisted either of parallel or sequential reactions. As a general statement, the reaction models consisting of parallel reactions were more accurate than those consisting of sequential reactions. Additional tests showed that user defined groups could be added to those identified mathematically to improve the accuracy of predictions for target species without sacrificing overall accuracy. The similarity approach was applied to a data set consisting of catalytic dodecane cracking in the presence of water added to the reaction mixture (50 wt %). The proposed similarity analysis identified different groups in the presence and absence of water, indicating that the data-driven approach can be used to identify qualitative differences in the reaction pathways. It is therefore demonstrated that data-driven identification of group types represents a useful new tool for development of group type models. Fluid catalytic cracking ; Catalysis ; Cracking ; Chemistry ; Computer science ; Organic chemistry Machine Learning in Materials Science ; Microbial bioremediation and biosurfactants ; Computational Drug Discovery Methods
Authors: Renato Rodriguez | Omidreza Ahmadzadeh | Yan Wang | Damoon Soudbakhsh
Source: SSRN Electronic Journal (preprint, 2024-01-01)
Field: Materials Science — Materials Chemistry

Physics-Informed and Temperature-Dependent Battery Digital Twin of State of Charge Dynamics Dynamics (music) ; Charge (physics) ; Battery (electricity) ; State (computer science) ; Physics ; Engineering physics ; Statistical physics ; Electrical engineering ; Computer science ; Quantum mechanics ; Engineering ; Acoustics ; Algorithm Machine Learning in Materials Science ; Electric Power Systems and Control ; Digital Transformation in Industry
Authors: Luke P. J. Gilligan | Matteo Cobelli | Hasan M. Sayeed | Taylor D. Sparks | Stefano Sanvito
Source: Materials Today Communications (article, 2024-11-14)
Field: Materials Science — Materials Chemistry

Sampling latent material-property information from LLM-derived embedding representations Vector embeddings derived from large language models (LLMs) show promise in capturing latent information from the literature. Interestingly, these can be integrated into material embeddings, potentially useful for data-driven predictions of materials properties. We investigate the extent to which LLM-derived vectors capture the desired information and their potential to provide insights into material properties without additional training. Our findings indicate that, although LLMs can be used to generate representations reflecting certain property information, extracting the embeddings requires identifying the optimal contextual clues and appropriate comparators. Despite this restriction, it appears that LLMs still have the potential to be useful in generating meaningful materials-science representations. Materials science ; Property (philosophy) ; Embedding ; Sampling (signal processing) ; Artificial intelligence ; Computer science ; Computer vision Machine Learning in Materials Science ; Welding Techniques and Residual Stresses ; Hydrogen embrittlement and corrosion behaviors in metals
Authors: João António | Pétia Georgieva
Source:  (article, 2024-06-30)
Field: Materials Science — Materials Chemistry

Iterative Stacking Regression for Materials Properties Prediction Most common data-driven approach for material analysis are the regression models that estimate specific material properties as a function of other related physical quantities. Data for the estimated targets and the input variables are assumed to be available.In this paper we go beyond the baseline regression approach and propose an iterative stacking regression model to subsequently estimate material properties that are difficult to measure and therefore not available for all data samples. The proposed approach is inspired by the stacking ensemble learning that combines the predictions of multiple ML models to improve the regressor performance. Overall this work provides valuable alternative how to deal with regression problems when data is small and incomplete. Stacking ; Computer science ; Regression ; Regression analysis ; Artificial intelligence ; Machine learning ; Statistics ; Mathematics ; Physics Machine Learning in Materials Science ; Hydrogen embrittlement and corrosion behaviors in metals ; X-ray Diffraction in Crystallography
Authors: Ye Kwon Huh | Minhee Kim | Katie Olivas | Todd R. Allen | Kaibo Liu
Source: Journal of Quality Technology (article, 2024-07-31)
Field: Materials Science — Materials Chemistry

Degradation modeling using Bayesian hierarchical piecewise linear models: A case study to predict void swelling in irradiated materials In this case study, we illustrate the use of a data-driven degradation model in a nuclear-specific application called void swelling. Void swelling is a complex, radiation-induced degradation mechanism that changes the dimensions of materials and damages the structural integrity. Accurate modeling and prediction of void swelling processes is crucial in nuclear power plant (NPP) management and maintenance planning by providing a guideline on the future state of the materials subject to reactor irradiation. Using a Bayesian hierarchical piecewise linear regression framework with a real-world void swelling dataset, we address the following three research questions: (1) How can we construct a data-driven degradation model such that its predictions satisfy the physical properties of void swelling? (2) How can we measure the joint effect of multiple experimental factors on the swelling process? (3) How can we accurately predict the future swelling status under limited data availability? The results on a real-world void swelling dataset not only improve our understanding of the swelling process but also provide a useful reference for nuclear practitioners and degradation researchers. Swelling ; Void (composites) ; Bayesian probability ; Materials science ; Computer science ; Composite material ; Artificial intelligence Nuclear Materials and Properties ; Nuclear reactor physics and engineering ; Graphite, nuclear technology, radiation studies
Authors: Toshihiro YAMAZAKI
Source: Keisan Rikigaku Koenkai koen ronbunshu/Keisan Rikigaku Kouenkai kouen rombunshuu (article, 2024-01-01)
Field: Materials Science — Materials Chemistry

QuesTek's Materials Design Approach for Leading Materials Development in the DX Era Since materials research and development require significant time and cost, there is increasing demand for improvements and optimization in materials design and practical production. QuesTek's materials design approach is based on the concept of Integrated Computational Materials Engineering (ICME), which leverages computational science over multi-scale, integrated simulations. This approach is characterized by using physics-based computational models to predict material properties. For the issue of scale-up for actual manufacturing, Accelerated Insertion of Materials (AIM) method is used, which combines computational models, data-driven methods, and statistical methods to quantify material composition tolerance and heat treatment uncertainty with small amounts of data, and predicts variations in property values and minimum design tolerances at manufacturing scale. QuesTek's proven material design approach enables rapid, accurate, and realistic material modeling, allowing for the property prediction and the process optimization through prototyping and full-scale manufacturing. In this paper, we introduce QuesTek's materials design approach with some case studies. Engineering ethics ; Engineering ; Architectural engineering Machine Learning in Materials Science
Authors: O.O. Shtofel | V.V. Holovko | Daniil Korolenko
Source:  (article, 2024-01-01)
Field: Materials Science — Materials Chemistry

Analysing the influence of structure morphology on weld metal properties, searching for relationships through multifractal indices The orientation of the structure has a significant effect on mechanical properties. If the grains are oriented in the longitudinal direction, i.e., when deformed along the crystallographic planes, which characterise the direction of grain growth during crystallisation, the ductility, as well as the short-term and long-term strength of the alloy, increase. The orientation of the grains in the direction of maximum stresses eliminates the presence of transverse grain boundaries, so the development of cracks along the boundaries in the longitudinal grain orientation is more difficult than in the transverse or equilibrium structure. The paper shows a solution to the problem of finding the angle of inclination of crystallites, calculating their sizes on the example of a model; the connection with the fractal parameter and mechanical properties of the distribution by angles of misorientation of the real structure. The model contains several objects of an arbitrary elongated shape. The task is to determine the orientation of the grains by finding the angle of inclination of each object to the selected axis. The programme, written in phyton with the use of artificial intelligence, is designed to analyse objects of different geometries in the input image. The results of the work consist of two parts. The first part is the program code corresponding to the theory, and the second part is the analysis of the data obtained and comparison with the existing mechanical properties and fractal dimensions of the components. The programme includes: 1) Converting the image to a black-white scale. 2) Detecting various shapes in the image using a thresholding technique. 3) Calculating properties - the length, width, centroid, orientation and angle. 4) Create data files. Further processing of the data allows you to build distributions of orientation angles on a flat structure, analyse the mechanical characteristics and compare them with the fractal dimension of the images. Misorientation ; Materials science ; Fractal ; Orientation (vector space) ; Transverse plane ; Multifractal system ; Grain boundary ; Crystallite ; Geometry ; Deformation (meteorology) ; Microstructure ; Structural engineering ; Mathematics ; Mathematical analysis ; Composite material ; Metallurgy ; Engineering Material Properties and Failure Mechanisms ; Engineering Diagnostics and Reliability ; Engineering and Environmental Studies
Authors: Leah Okoruwa | Fatih Tarak | Farzaneh Sameni | Ehsan Sabet
Source: Preprints.org (preprint, 2024-11-14)
Field: Chemistry — Organic Chemistry

Bridging Experimentation and Computation: OMSP for Advanced Acrylate Characterization and Digital Photoresin Design in Vat Photopolymerization Vat Photopolymerization (VPP) is an additive manufacturing method that requires the design of photocurable resins to act as feedstock and binder for the printing of parts, both monolithic or composite. The design of a suitable photoresin is costly and time-consuming. The development of one formulation requires the consumption of kilograms of costly materials, weeks of printing and performance testing, as well as the need to have developers with the expertise and knowledge of the materials used, making the development process cost thousands. This paper presents a new characterization methodology for acrylates that allows for the computerization of the photoresin formulation development process, reducing the timescale to less than a week. Okoruwa Maximum Saturation Potential (OMSP) is a methodology that uses Attenuated Total Reflection (ATR-FTIR) to study the functional group of acrylates, assigning numerical outputs to characterize monomers, oligomers and formulations, allowing for more precise distinguishment between materials. It utilizes the principles of Gaussian normal distribution for the storage, recall and computerization of acrylate data and formulation design without the need to database numerous files of spectral data to an average coefficient of determination (R² ) of 0.97. The same characterization method can be used to define the potential reactivity of acrylate formulations without knowing the formulation components, something not possible when using properties such as functionality. This allows for modifications to be made to unknown formulations without prior knowledge of their contents. Validation studies were performed to define the boundaries of the operation of OMSP and assess the methodology’s reliability as a characterization tool. OMSP can confidently detect changes caused by the presence of various acrylates made to the photoresin system and distinguish between acrylates of the same viscosity and functionality. OMSP can compare digitally mixed formulations to physically mixed formulations and provides a high degree of accuracy (R² of 0.9406 to 0.9964), highlighting the future potential for building foundations for artificial intelligence in VPP, the streamlining of photoresin formulation design and transforming the way acrylates are characterized, selected and used. Photopolymer ; Bridging (networking) ; Acrylate ; Characterization (materials science) ; Computation ; Computer science ; Materials science ; Nanotechnology ; Programming language ; Composite material ; Polymer Photopolymerization techniques and applications ; Advanced Polymer Synthesis and Characterization ; Manufacturing Process and Optimization
Authors: Xinze Xu | Zhang Cheng | Wenbo Liu | Chunhua Lin | Weiguo Miao | Heyang Yuan
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2024-11-30)
Field: Chemistry — Analytical Chemistry

Direct Feature Identification from Raman Spectra and Precise Data-driven Classification of Phytopathogens at Single Conidium-Species Level ABSTRACT Conidia can cause outbreaks of fungal plant diseases, resulting in significant losses in crop yield and economy. Traditional diagnosis of phytopathogenic conidia based on morphology and molecular biology is time-consuming, labor-intensive, and often fails to differentiate fungal conidia. To overcome these challenges, a new classification approach was developed in this study by integrating Raman spectroscopy with data-driven modeling. Eight fungal species were selected and characterized using Raman spectra. Three characteristic Raman wavenumbers at 1003 to 1005 cm -1 , 1153 to 1157 cm -1 , and 1515 to 1522 cm -1 shared a consistent pattern across species and could be attributed to carotenoids. Clustering of the Raman spectra using principal component analysis (PCA) showed substantial overlap, indicating inaccurate classification of conidia. Three data-driven models, support vector machines (SVMs), decision trees (DTs), and eXtreme Gradient Boosting Forest (XGBoost) were trained with three categories of features (number of peaks, maximum peak, and curve roughness) identified within eight characteristic wavenumber ranges, The optimal SVM, DT, and XGBoost determined by hyperparameter tuning achieved prediction precision of 0.88, 0.88, and 0.96, respectively. PCA-XGBoost trained by feeding principal components of PCA to XGBoost achieved prediction precision of 0.94, suggesting that features extracted from the raw datasets outperformed those extracted with PCA in terms of data-driven classification. This study has demonstrated the great potential of Raman spectroscopy combined with data-driven modeling for classification of phytopathogenic conidia. Identification (biology) ; Conidium ; Feature (linguistics) ; Raman spectroscopy ; Physics ; Botany ; Biology ; Chemistry ; Artificial intelligence ; Computer science ; Optics ; Philosophy ; Linguistics Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Identification and Quantification in Food
Authors: Long Zhao | Haiwei Wang | Gu He | Dongbo Song | Zuosheng Zhang | Xin Tong | Teng Tian
Source:  (article, 2024-11-22)
Field: Materials Science — Materials Chemistry

Multi-Sensor Partial Discharge Prediction in Switchgear Cabinets Using Machine Learning Models This study develops a multi-sensor, data-driven approach for accurate and timely partial discharge (PD) prediction in switchgear cabinets, a critical factor for power system reliability. By integrating Gaussian Mixture Models (GMM) for anomaly detection, the model incorporates data-driven PD confidence directly into classification, effectively distinguishing true PD events from normal variations. Advanced feature engineering captures essential chemical dynamics during discharge, while physical knowledge is embedded as constraints in the model training process, aligning predictions with known PD behavior and enhancing learning efficiency. Experimental results show the GMM-restricted SVM model achieves 98% accuracy, 99% recall, a 0.95 AUC, and reduces alert time error to 3 seconds, compared to 8 seconds in baseline models. Ablation studies confirm that combining GMM-based confidence with physical constraints strengthens both precision and recall. This framework provides a robust and interpretable solution for predictive maintenance in power systems, with future work focusing on expanding sensor inputs, real-time monitoring, and finer discharge classification. Switchgear ; Partial discharge ; Computer science ; Predictive modelling ; Machine learning ; Artificial intelligence ; Reliability engineering ; Engineering ; Electrical engineering ; Voltage High voltage insulation and dielectric phenomena ; Elevator Systems and Control ; Power Transformer Diagnostics and Insulation
Authors: R. R. Oberle | Robert Tadej Boich | Brikena Shazimani | Thomas Gries
Source: Communications in development and assembling of textile products (article, 2024-12-30)
Field: Materials Science — Polymers and Plastics

Investigation on a smart textile heating concept for energy consumption-optimised heat transfer Conventional non-stationary artificial heating methods, such as infrared radiators, have various deficits: The heat fields are small, their range is short, and the installation options are limited. In addition, they are environmentally harmful due to high emissions. To make in- and outdoor areas usable at lower temperatures, an energy consumption-optimized heat transfer is required. In this work we are investigating on an innovative, intelligent, modular textile for indoor and outdoor use. The use of conductive wires, sensors and a data-driven control circuit in the textile enables targeted heat radiation, which can be expanded as required thanks to its modular design. This reduces emissions and precisely controls energy consumption. By developing a demonstrator, the concept of the layered structure of the textile module, including unidirectional radiation and sensor technology, is being investigated. The conductive wires are incorporated into a flexible carrier material using a modified embroidery machine. In the process, the carrier material must meet the previously defined design requirements that are based on the material investigation and preliminary tests on embroidery feasibility. The structure of the intelligent textile is constructed, realized as a prototype, and validated. Through the combination of a heat source and flexible support material, it is possible to bring a competitive product to the market. Textile ; Energy consumption ; Consumption (sociology) ; Heat transfer ; Process engineering ; Architectural engineering ; Mechanical engineering ; Environmental science ; Materials science ; Engineering ; Composite material ; Electrical engineering ; Thermodynamics ; Art ; Physics Textile materials and evaluations
Authors: Fan Xie | Qiaoling Liu | Longhai Zhuo | Haitao Wei | Yu‐Xuan Shang | Tao Liu | Zhaoqing Lu
Source: SSRN Electronic Journal (preprint, 2024-01-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Hierarchically Engineered Sandwich-Structured H-Mxene/Anf Hybrid Films with Tunable Electromagnetic Interference Shielding and Exceptional Environmental Resilience Electromagnetic shielding ; Resilience (materials science) ; Electromagnetic interference ; Interference (communication) ; Materials science ; Nanotechnology ; Optoelectronics ; Engineering ; Composite material ; Electronic engineering ; Telecommunications Electromagnetic wave absorption materials ; MXene and MAX Phase Materials ; Advancements in Battery Materials
Authors: Guiyu Chu | Zhuguang Nie | Yanmeng Peng | Huanyu Xu | Yang Xiao | Xiaoli Guo | Mingyu Jiang | Fanghong Dong | Zilu Guo | Shuhua Qi | Junping Zhang
Source: SSRN Electronic Journal (preprint, 2024-01-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Spin-Coating Anf Based Multilayer Symmetric Composite Films for Enhanced Electromagnetic Interference Shielding and Thermal Management Electromagnetic shielding ; Materials science ; Coating ; Composite number ; Electromagnetic interference ; Spin coating ; Interference (communication) ; Spin (aerodynamics) ; Thermal ; Composite material ; Condensed matter physics ; Physics ; Engineering ; Electronic engineering ; Telecommunications ; Thermodynamics Electromagnetic wave absorption materials ; Electromagnetic Compatibility and Measurements ; Electromagnetic Compatibility and Noise Suppression