jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Hua Zhang | Jiangtao Wang | Jie Wu | Huoding Bian | Yikun Wei
Source: Textile Research Journal (article, 2023-02-22)
Field: Materials Science — Polymers and Plastics

Active tension control of the conical winding system based on the neural network control algorithm of radial-basis functions A conical winding formation and tension control system was proposed in the doubling operation based on the yarn guide mode of a single spindle in this study. Conical winding formation realized the radial unwinding of wound package. An overfeed mechanism was introduced to achieve closed-loop control of yarn tension. The overfeed wheel was driven by a brushless Direct Current motor. The tension control system combined a Proportion Integration Differentiation controller with a radial-basis-function neural network, whose purpose was to meet the control requirements of the brushless DC motor. This system consisted of three main steps: Firstly, the radial-basis-function neural network was used to identify the system online. Secondly, the gradient descent method was used to adjust the node weight, center vector, and baseband width. Finally, incremental PID parameters online were adjusted according to the identified Jacobian information. A mathematical model of a control system was established in Matrix Laboratory. An experimental platform was designed for doubling winder to compare the control effects of Radial-basis-function-PID with traditional PID. The simulation results showed that the RBF-PID had a smaller overshoot of yarn tension, shorter adjustment time, and smaller steady-state error compared with the traditional PID controller in doubling operation by simulating the mathematical model. Experimental results showed the RBF-PID controller had good performance and stability and could be applied to yarns with different average linear velocity, yarn counts and strands . The yarn tension fluctuation will not exceed ±3% of the target value when the experimental materials and the cone angles are unchanged. PID controller ; Control theory (sociology) ; Radial basis function ; Overshoot (microwave communication) ; Artificial neural network ; Control system ; Tension (geology) ; Controller (irrigation) ; Engineering ; Computer science ; Control engineering ; Artificial intelligence ; Physics ; Control (management) ; Temperature control Textile materials and evaluations ; Vibration and Dynamic Analysis ; Mechanical stress and fatigue analysis
Authors: Abderrahmane Benhadjira | M.I. Sayyed | Omar Bentouila | Kamal Eddine Aiadi
Source: Nuclear Engineering and Technology (article, 2023-09-15)
Field: Materials Science — Materials Chemistry

Artificial neural network approach for calculating mass attenuation coefficient of different glass systems In this study, we propose an alternative approach using Artificial Neural Networks (ANN) for determining Mass Attenuation Coefficients (MAC) in various glass systems. This method takes into account the weights of glass compositions, density, and photon energy as input features. The ANN model was trained and tested on a dataset consisting of 650 data points and subsequently validated through a K-fold cross-validation procedure. Our findings demonstrate a high level of accuracy, with R2 values ranging from 0.90 to 0.99. Additionally, the model exhibits robust extrapolation capabilities with an R2 score of 0.87 for predicting MAC values in a new glass system. Furthermore, this approach significantly reduces the need for costly and time-consuming computations and experiments, making it a potential tool for selecting materials for effective radiation protection. Extrapolation ; Artificial neural network ; Attenuation ; Computation ; Ranging ; Mass attenuation coefficient ; Algorithm ; Biological system ; Energy (signal processing) ; Computer science ; Mathematics ; Artificial intelligence ; Statistics ; Optics ; Physics Radiation Shielding Materials Analysis ; Glass properties and applications ; Digital Radiography and Breast Imaging
Authors: Bohayra Mortazavi | Fazel Shojaei | Fei Ding | Xiaoying Zhuang
Source: FlatChem (article, 2023-10-30)
Field: Materials Science — Materials Chemistry

Anomalous tensile strength and thermal expansion, and low thermal conductivity in wide band gap boron monoxide monolayer Materials science ; Monolayer ; Thermal expansion ; Density functional theory ; Thermal conductivity ; Bulk modulus ; Band gap ; Direct and indirect band gaps ; Graphene ; Ultimate tensile strength ; Thermal stability ; Condensed matter physics ; Computational chemistry ; Composite material ; Nanotechnology ; Chemistry ; Organic chemistry Thermal properties of materials ; Boron and Carbon Nanomaterials Research ; Thermal Expansion and Ionic Conductivity
Authors: Lewis M. Bass | Luke H. Elder | Dan E. Folescu | Negin Forouzesh | Igor S. Tolokh | Anuj Karpatne | Alexey V. Onufriev
Source: Journal of Chemical Theory and Computation (article, 2023-12-27)
Field: Materials Science — Materials Chemistry

Improving the Accuracy of Physics-Based Hydration-Free Energy Predictions by Machine Learning the Remaining Error Relative to the Experiment The accuracy of computational models of water is key to atomistic simulations of biomolecules. We propose a computationally efficient way to improve the accuracy of the prediction of hydration-free energies (HFEs) of small molecules: the remaining errors of the physics-based models relative to the experiment are predicted and mitigated by machine learning (ML) as a postprocessing step. Specifically, the trained graph convolutional neural network attempts to identify the "blind spots" in the physics-based model predictions, where the complex physics of aqueous solvation is poorly accounted for, and partially corrects for them. The strategy is explored for five classical solvent models representing various accuracy/speed trade-offs, from the fast analytical generalized Born (GB) to the popular TIP3P explicit solvent model; experimental HFEs of small neutral molecules from the FreeSolv set are used for the training and testing. For all of the models, the ML correction reduces the resulting root-mean-square error relative to the experiment for HFEs of small molecules, without significant overfitting and with negligible computational overhead. For example, on the test set, the relative accuracy improvement is 47% for the fast analytical GB, making it, after the ML correction, almost as accurate as uncorrected TIP3P. For the TIP3P model, the accuracy improvement is about 39%, bringing the ML-corrected model's accuracy below the 1 kcal/mol threshold. In general, the relative benefit of the ML corrections is smaller for more accurate physics-based models, reaching the lower limit of about 20% relative accuracy gain compared with that of the physics-based treatment alone. The proposed strategy of using ML to learn the remaining error of physics-based models offers a distinct advantage over training ML alone directly on reference HFEs: it preserves the correct overall trend, even well outside of the training set. Overfitting ; Approximation error ; Computer science ; Set (abstract data type) ; Solvation ; Test set ; Algorithm ; Artificial neural network ; Statistical physics ; Machine learning ; Physics ; Molecule ; Quantum mechanics Machine Learning in Materials Science ; Protein Structure and Dynamics ; Computational Drug Discovery Methods
Authors: Prapti Kakkar | Shivanshu Mishra | Anjali Sharma | Mohammad Babar | Rupali Verma | Gaurav Verma
Source: Journal of Dispersion Science and Technology (article, 2023-02-28)
Field: Chemistry — Physical and Theoretical Chemistry

Using deep learning for the prediction of mixing patterns in two component-colored solutions as a proxy to dispersion in nanocomposite coatings Dispersion of nanofiller in the polymer matrix is a vital factor that influences the properties of the fabricated nanocomposites at the laboratory level. Characterization techniques like TEM and FESEM, having a small sample size, tend to miss out on the big picture for the analysis of mixing on a larger scale. At the industrial level, conducting such testing with varying experimental conditions is not viable in terms of both cost and time. Through this study, we propose a simple method to examine the extent of dispersion using a simple camera and employing deep learning (DL) models. For this purpose, an analogous study has been performed to study the sensitivity of the processing techniques, to better understand the findings in our previous articles. A two component-colored solution (oil–water) was utilized as a proxy for the nanofiller-polymer matrix system. Different processing methods were employed namely ultrasonication, homogenization, sequential ultrasonication and homogenization and simultaneous ultrasonication and homogenization. The variation in processing technique significantly affects the dispersion which is attributed to the different mixing mechanisms (turbulent, diffusive, and convective) incurred in these processing techniques. Inferences are withdrawn by detecting patterns in a large sample size which highlights that DL models provide us with a holistic viewpoint of real-time observations. It also ameliorates human interpretation by unraveling obscure information which can go unnoticed by human eyes. Homogenization (climate) ; Colored ; Materials science ; Sonication ; Polymer ; Dispersion (optics) ; Biological system ; Nanocomposite ; Computer science ; Artificial intelligence ; Nanotechnology ; Composite material ; Chemical engineering ; Optics ; Engineering ; Physics Electrostatics and Colloid Interactions ; Enhanced Oil Recovery Techniques ; Microfluidic and Bio-sensing Technologies
Authors: Sara Mantach | Mike Partyka | Valeria Pevtsov | Ahmed Ashraf | Behzad Kordi
Source: IEEE Transactions on Dielectrics and Electrical Insulation (article, 2023-08-17)
Field: Materials Science — Materials Chemistry

Unsupervised Deep Learning for Detecting Number of Partial Discharge Sources in Stator Bars Regular maintenance and testing of high-voltage assets are important for avoiding outages. Measurement of partial discharges (PDs) is commonly employed to monitor the health of the insulation materials of such assets, and the detection of the PD sources would help justify remedial actions such as inspection and repair. Before classifying the PD sources, the identification of the number of PD sources is necessary (clustering of the PD pulses). Different clustering techniques have been used for this purpose such as time–frequency maps, <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${K}$ </tex-math></inline-formula> -means clustering, and principal component analysis. However, there are limitations in these techniques in regard to the additional information that these algorithms need. For example, the number of clusters and the minimum distance between data points must be assumed in some algorithms. In order to overcome the mentioned limitations, a robust unsupervised deep learning model is proposed for unsupervised clustering, based on a convolutional AE and an adaptive clustering technique. In a laboratory setup, defects are introduced to a generator stator bar, simulating common PD sources, and PD-induced pulses in the ground connection of the bar are measured. The inputs to the deep learning model are the unlabelled, time-series PD pulses, and the output is the predicted number of PD sources. The proposed system showed better prediction of the number of PD sources compared to the existing techniques, which require human judgment. In addition, the proposed system demonstrated immunity to noise, where additive white Gaussian noise (AWGN) is added artificially to the measured PD waveforms. Partial discharge ; Cluster analysis ; Computer science ; Stator ; Noise (video) ; Artificial intelligence ; Unsupervised learning ; Pattern recognition (psychology) ; Autoencoder ; White noise ; Waveform ; Data mining ; Deep learning ; Machine learning ; Engineering ; Voltage ; Electrical engineering High voltage insulation and dielectric phenomena ; Power Transformer Diagnostics and Insulation ; Non-Destructive Testing Techniques
Authors: Kevin P. McGuinness | Anton O. Oliynyk | Sangjoon Lee | Beatriz Molero-Sánchez | Paul Kwesi Addo
Source: Physical Chemistry Chemical Physics (article, 2023-01-01)
Field: Materials Science — Materials Chemistry

Machine-learning prediction of thermal expansion coefficient for perovskite oxides with experimental validation Perovskite oxides have been of high-interest and relatively well studied over the last 20 years due to their various applications, specifically for solid oxide fuel cells (SOFCs) and solid oxide electrolysis cells (SOECs). One of the key properties for a perovskite to perform well as a component in SOFCs, SOECs, and other high-temperature applications is its thermal expansion coefficient (TEC). The use of machine learning (ML) to predict material properties has greatly increased over the years and has proven to be a very useful tool for materials screening. The process of synthesizing and testing perovskite oxides is laborious and costly, and the use of physics-based models is often highly computationally expensive. Due to the amount of elements able to be accommodated in the ABO<sub>3</sub> structure and the ability for crystallographic mixing in both the A and B-sites, there are a massive amount of possible ABO<sub>3</sub> perovskites. In this paper, a ML model for the prediction of the TECs of AA'BB'O<sub>3</sub> perovskites is produced and applied to millions of potential compositions resulting in reliable TEC predictions for 150 451 of the compositions. Thermal expansion ; Perovskite (structure) ; Oxide ; Materials science ; TEC ; Electrolysis ; Thermodynamics ; Chemical engineering ; Chemistry ; Physical chemistry ; Physics ; Metallurgy ; Electrolyte Machine Learning in Materials Science ; Advancements in Solid Oxide Fuel Cells ; Catalysis and Oxidation Reactions
Authors: Zhipeng Xiong | Yifeng Yu | Huan Chen | Lichun Bai
Source: Nanotechnology (article, 2023-07-06)
Field: Materials Science — Materials Chemistry

A coarse-grained study on mechanical behaviors of diamond-like carbon based on machine learning Diamond-like carbon (DLC) films have broad application potential due to their high hardness, high wear resistance, and self-lubricating properties. However, considering that DLC films are micron-scale, neither finite element methods nor macroscopic experiments can reveal their deformation and failure mechanisms. Here we propose a coarse-grained molecular dynamics (CGMD) approach which expands the capabilities of molecular dynamics simulations to uniaxial tensile behavior of DLC films at a higher scale. The Tersoff potential is modified by high-throughput screening calculations for CGMD. Given this circumstance, machine learning (ML) models are employed to reduce the high-throughput computational cost by 86%, greatly improving the efficiency of parameter optimization in second- and fourth-order CGMD. The final obtained coarse-grained tensile curves fit well with that of the all-atom curves, showing that the ML-based CGMD method can investigate DLC films at higher scales while saving a large number of computational resources, which is important for promoting the research and production of high-performance DLC films. Materials science ; Molecular dynamics ; Diamond-like carbon ; Ultimate tensile strength ; Diamond cubic ; Diamond ; Composite material ; Throughput ; Finite element method ; Nanotechnology ; Thin film ; Computational chemistry ; Structural engineering ; Computer science ; Engineering Diamond and Carbon-based Materials Research ; Force Microscopy Techniques and Applications ; Metal and Thin Film Mechanics
Authors: John Greenhall | Dipen N. Sinha | Cristian Pantea
Source: IEEE Transactions on Ultrasonics Ferroelectrics and Frequency Control (article, 2023-05-22)
Field: Chemistry — Analytical Chemistry

Genetic Algorithm-Wavelet Transform Feature Extraction for Data-Driven Acoustic Resonance Spectroscopy Acoustic resonance spectroscopy (ARS) enables highly accurate measurement of the properties (geometry/material) of a structure based on the structure's natural vibrational resonances. In general, measuring a specific property in multibody structures presents a significant challenge due to the complex overlapping peaks within the resonance spectrum. We present a technique for extracting useful features from a complex spectrum by isolating resonance peaks that are sensitive to the measured property and insensitive to other properties (noise peaks). We isolate specific peaks by selecting frequency regions of interest and performing wavelet transformation, where the frequency regions and wavelet scales are tuned via a genetic algorithm. This contrasts greatly from the traditional wavelet transformation/decomposition techniques, which use a large number of wavelets at different scales to represent the signal, including the noise peaks, and results in a large feature size, thus decreasing machine learning (ML) generalizability. We provide a detailed description of the technique and demonstrate the feature extraction technique, for example, regression and classification problems. We observe reductions of 95% and 40% in regression and classification errors, respectively, when using the genetic algorithm/wavelet transform feature extraction, compared to using no feature extraction, or using wavelet decomposition, which is common in optical spectroscopy. The feature extraction has potential to significantly increase the accuracy of spectroscopy measurements based on a wide range of ML techniques. This would have significant implications for ARS, as well as other data-driven methods for other types of spectroscopy, e.g., optical. Wavelet ; Feature extraction ; Wavelet transform ; Artificial intelligence ; Pattern recognition (psychology) ; Transformation (genetics) ; Spectroscopy ; Noise (video) ; Wavelet packet decomposition ; Feature (linguistics) ; Discrete wavelet transform ; Algorithm ; Genetic algorithm ; Computer science ; Mathematics ; Physics ; Chemistry Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Advanced Chemical Sensor Technologies
Authors: P. Sreekala | A. Ramkumar | K. Rajesh
Source: Measurement Sensors (article, 2023-01-13)
Field: Materials Science — Materials Chemistry

Performance comparison and analysis of mathematical, ANSYS and neural network model of a thermo electrical generator - The need to generate energy from waste energy is growing as the demand for energy rises. Heat engines can only exchange around 35% of accessible energy, leaving the excess as surplus heat. A thermoelectric generator can transform this waste heat into useful energy. The Seebeck principle is used to create a thermoelectric device, which consists of a P-Type and N-Type semiconductor coupled electrically in series and thermally in parallel. Thermoelectric generators immediately convert thermal energy to electrical energy without the use of rotating parts, and because there are no moving parts, they are possibly less prone to failure. With the rising cost of fossil fuels and their negative influence on the atmosphere, it is past time to consider renewable energy sources (RES). Thermoelectric generators (TEG) are widely utilized as autonomous RES, with applications ranging from mW to W power. In this paper mainly focusing the comparison of three different model of Thermo Electric Generator. Out of these three mathematical model give a better result compared to neural and ANSYS model. Neural network model was developed from the hardware result obtained from the experiment. In practical case the temperature difference is very less so the output voltage current and power are very less. The maximum hot side temperature of SP1848 27145 SA TEG is 50 °C and minimum temperature is 27 °C. The seebeck coefficient, voltage, current, Figure of Merit performances are also plotted. Thermoelectric generator ; Thermoelectric effect ; Renewable energy ; Electric potential energy ; Voltage ; Generator (circuit theory) ; Waste heat ; Electric power ; Thermal energy ; Power (physics) ; Maximum power principle ; Seebeck coefficient ; Electrical engineering ; Electricity generation ; Nuclear engineering ; Materials science ; Mechanical engineering ; Engineering ; Heat exchanger ; Thermodynamics ; Physics Advanced Thermoelectric Materials and Devices ; Thermal Radiation and Cooling Technologies ; Advanced Thermodynamics and Statistical Mechanics
Authors: Shrikant Kapse | Priya Kedia | Shankar B. Kausley | Beena Rai
Source: Journal of Nondestructive Evaluation (article, 2023-04-01)
Field: Chemistry — Analytical Chemistry

Nondestructive Evaluation of Banana Maturity Using NIR AS7263 Sensor Sugar ; Maturity (psychological) ; Mathematics ; Brix ; Mean squared error ; Artificial neural network ; Biological system ; Artificial intelligence ; Food science ; Chemistry ; Statistics ; Computer science ; Biology Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Smart Agriculture and AI
Authors: Dipshikha Tamili | Susovan Jana | Paramita Bhattacharjee
Source: Journal of Chemometrics (article, 2023-10-03)
Field: Chemistry — Spectroscopy

Chromatographic method development for simultaneous determination of serotonin, melatonin, and L‐tryptophan: Mass transfer modeling, chromatographic separation factors, and method prediction by artificial neural network Abstract This work endeavored to develop a high performance liquid chromatography (HPLC) method for simultaneous quantification of three important biotherapeutic molecules namely, L‐tryptophan, serotonin, and melatonin, present in low amounts in agro‐commodities. In the first approach, using pure chemical standards of the same in a mixture, chromatogram separation parameters such as peak sharpness, peak shouldering, and peak separation were judged by a human panel and biasness‐cum‐decision uncertainties were averted using fuzzy logic analysis. In the second approach, the separation parameters such as peak resolution and separation factors were evaluated to obtain a well‐resolved chromatogram. The parameters of the said separation process were successfully modeled by dimensionless numbers of mass transfer of the analytes in the column; and post‐fitting of mass transfer equations, high R 2 was obtained suggesting successful development of the chromatographic process. Euclidean distances between the values of each chromatographic separation parameter and their respective ideal values; the defuzzified scores; findings of mass transfer study; and separation factors concomitantly established that the mobile phase of composition 1% acetic acid in HPLC water (A)–HPLC grade methanol (B) in gradient elution conditions (90% A–10% B) at a flow rate of 1 mL/min could simultaneously quantify the three molecules (μg) in button mushrooms with good resolution. The presence of the said biomolecules in the extract of button mushrooms was also confirmed by electrospray ionization (ESI)‐mass spectrometer (MS). An artificial neural network model (high R 2 ) was also developed for chromatographic users, which would allow accurate prediction of the chromatographic parameters by varying mobile phase composition and flow rates. Chromatography ; Chemistry ; High-performance liquid chromatography ; Mass spectrometry ; Resolution (logic) ; Separation process ; Chemometrics ; Elution ; Analyte ; Analytical Chemistry (journal) ; Mass transfer ; Artificial intelligence ; Computer science Analytical Chemistry and Chromatography ; Chromatography in Natural Products