jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Arooba khan | Mannan Hassan | Muhammad Suhail Shaikh | Muhammad Farhan | Asad Ullah
Source: Journal of Electrical Systems and Information Technology (article, 2022-12-07)
Field: Materials Science — Materials Chemistry

Tailoring the properties of epoxy/silicone blends for high-voltage capacitor applications Abstract In recent decades, high dielectric constant ( k ) polymer nanocomposites have proved excellent potential in dielectric and energy storage applications. Epoxy/silicon rubber composite materials have shown promising properties in applications such as high-voltage insulation. Three types of nanomaterials (SiO 2 , TiO 2 , and TiO 2 @SiO 2 ) with distinct intrinsic properties are carefully chosen to build high- k epoxy/silicone polymer nanocomposites in this study. To raise the value of K , this work tailored the polarizing ability or permittivity of dielectric material by inserting different weight ratios of nano-fillers (SiO 2 , TiO 2 , TiO 2 @SiO 2 ) in the base matrix of dielectric material in epoxy and silicon rubber blends. With a base matrix containing 75% epoxy and 25% liquid silicone rubber concentration, the maximum value of K obtained is K = 158 for 5% TiO 2 and there is an increase in the dielectric strength to 398 kV/mm. The obtained results indicate that, among the three different kinds of epoxy/silicone, TiO 2 @SiO 2 has the most potential in enhancing the energy storage capabilities of the proposed nanocomposites, owing to the largest increase in k while maintaining low dielectric loss and leakage current. Materials science ; Epoxy ; Dielectric ; Composite material ; Silicone rubber ; Nanocomposite ; Silicone ; Dielectric loss ; Permittivity ; Polymer ; Capacitor ; High-κ dielectric ; Voltage ; Electrical engineering ; Optoelectronics High voltage insulation and dielectric phenomena ; Dielectric materials and actuators ; Polymer Nanocomposites and Properties
Authors: Дмитрий Л. Обыденнов | Diana I. Nigamatova | Alexander S. Shirinkin | Oleg E. Melnikov | Vladislav V. Fedin | Sergey А. Usachev | Alena E. Simbirtseva | Mikhail Y. Kornev | Vyacheslav Ya. Sosnovskikh
Source: Molecules (article, 2022-12-16)
Field: Chemistry — Organic Chemistry

2-(2-(Dimethylamino)vinyl)-4H-pyran-4-ones as Novel and Convenient Building-Blocks for the Synthesis of Conjugated 4-Pyrone Derivatives A straightforward approach for the construction of the new class of conjugated pyrans based on enamination of 2-methyl-4-pyrones with DMF-DMA was developed. 2-(2-(Dimethylamino)vinyl)-4-pyrones are highly reactive substrates that undergo 1,6-conjugate addition/elimination or 1,3-dipolar cycloaddition/elimination followed by substitution of the dimethylamino group without ring opening. This strategy includes selective transformations leading to conjugated and isoxazolyl-substituted 4-pyrone structures. The photophysical properties of the prepared 4-pyrones were determined in view of further design of novel merocyanine fluorophores. A solvatochromism was found for enamino-substituted 4-pyrones accompanied by a strong increase in fluorescence intensity in alcohols. The prepared conjugated structures demonstrated valuable photophysical properties, such as a large Stokes shift (up to 204 nm) and a good quantum yield (up to 28%). Conjugated system ; Pyran ; Solvatochromism ; Chemistry ; Merocyanine ; Cycloaddition ; Conjugate ; Pyrone ; Fluorescence ; Quantum yield ; Photochemistry ; Combinatorial chemistry ; Organic chemistry ; Molecule ; Photochromism ; Polymer Oxidative Organic Chemistry Reactions ; Asymmetric Synthesis and Catalysis ; Porphyrin and Phthalocyanine Chemistry
Authors: Xiajin Rao | Dajian Li | Xiaoxing Zhang | Xiaofei Xia | Yi Su | Yufeng Lü | Boya Peng
Source: Scientific Reports (article, 2022-04-15)
Field: Materials Science — Materials Chemistry

Study on partial discharge characteristics of C6F12O mixed gas The greenhouse effect of SF<sub>6</sub> increasingly limits its application in various gas insulated equipment. C<sub>6</sub>F<sub>12</sub>O combines the advantages of insulation resistance, safety and environmental protection. When mixed with buffer gas, C<sub>6</sub>F<sub>12</sub>O is considered to have potential application prospects in medium and low voltage equipment. In this paper, about the partial discharge characteristics of the mixed gas, an experimental study was carried out. The partial discharge initiation voltage and discharge extinction voltage of mixed gas under power frequency voltage are measured and compared with the breakdown voltage. The results show that the breakdown voltage is greatly improved after adding C<sub>6</sub>F<sub>12</sub>O, with the increase of mixing ratio, the partial discharge initiation voltage and extinction voltage of mixed gas gradually increase, and the effect of gas pressure on high mixing ratio is obvious. The difference between the partial discharge inception voltage and the breakdown voltage is larger than that of pure N<sub>2</sub>. The research in this paper can provide an important reference for the application, operation and protection of C<sub>6</sub>F<sub>12</sub>O mixed gas in medium and low voltage equipment. Partial discharge ; Voltage ; Breakdown voltage ; Materials science ; Partial pressure ; Extinction (optical mineralogy) ; High voltage ; Mixing (physics) ; Greenhouse gas ; Environmental science ; Analytical Chemistry (journal) ; Mechanics ; Nuclear engineering ; Electrical engineering ; Chemistry ; Physics ; Engineering ; Environmental chemistry ; Mineralogy ; Geology High voltage insulation and dielectric phenomena
Authors: Vidyadhar Peesapati | Christos Zachariades | George Callender | Siyu Gao | Oliver Cwikowski | Richard Gardner
Source: High Voltage (article, 2022-04-13)
Field: Materials Science — Materials Chemistry

Investigation of incipient faults in 66 kV oil‐filled cable sealing ends Abstract This paper reports on the investigation of incipient fault mechanisms of a widely used family of Cable Sealing Ends (CSEs) which use an earthed metallic clip on top of the stress cone to fasten the cable termination stress grading. Finite element simulations indicate that the clip is causing localised electric field enhancement which could also cause impurities in the oil to move towards the clip under the influence of the dielectrophoretic force. Accelerated ageing tests of stress cones in the laboratory have corroborated the simulation results, with the high electric field causing degradation of the semi‐conducting tape underneath the clip as well as initiation of tracking on the surface of the stress cone originating from the edges of the clip. Partial discharge (PD) testing using a bespoke test rig in the high voltage laboratory has shown that incipient faults originating from the ageing of the solid CSE materials can only be detected after surface tracking has progressed substantially. Even then, the intermittent nature of PD activity can pose a risk to the accurate condition assessment of CSEs if continuous monitoring is not employed and if the ultra‐high frequency method is used on its own. Materials science ; Stress (linguistics) ; Finite element method ; Electric field ; Structural engineering ; Composite material ; Engineering ; Physics High voltage insulation and dielectric phenomena ; Power Transformer Diagnostics and Insulation ; Non-Destructive Testing Techniques
Authors: Harsha Durvasula | Sahana Vrinda Kakarla | Anant Thazhemadam | Reshma Roy | Arti Arya
Source:  (article, 2022-03-11)
Field: Materials Science — Materials Chemistry

Prediction of Material Properties using Crystal Graph Convolutional Neural Networks Machine Learning principles have found application to scientific domains, such as materials science, owing to their significant efficiency compared with performing ab initio calculations. In recent years, Graph Convolutional Networks (GCNs) have gained popularity in the same domain, for the task of prediction and classification of crystal properties, as they have been proven to be effective for node-representation learning while working with graph-like structures. Fundamentally, similar to conventional Convolution Neural Networks (CNNs), GCNs are also composed of convolution and pooling layers. While there is ample literature focusing on the convolution operations performed in similar models, not enough focus has been given to the pooling mechanisms employed in the same models. In this work, the proposed model employs GCNs in the domain of materials science while incorporating a relatively more sophisticated pooling mechanism that takes into account the structure of the crystals being featurized, with which we've observed better results for all the attributes tested as compared to traditional first-principles methods used for computing the same. Pooling ; Computer science ; Convolutional neural network ; Convolution (computer science) ; Graph ; Artificial intelligence ; Theoretical computer science ; Representation (politics) ; Machine learning ; Popularity ; Deep learning ; Artificial neural network Machine Learning in Materials Science ; Computational Drug Discovery Methods ; X-ray Diffraction in Crystallography
Authors: Lei Zhang | Zhenyu Li
Source: Journal of Physics Conference Series (article, 2022-11-01)
Field: Materials Science — Materials Chemistry

Machine learning for materials classifications from images Computer-aided materials recognition from images is critical for automation in materials industry. In this manuscript, we perform machine learning studies to recognize common materials from images. To this end, an annotated materials image database consisting of typical materials including metal, plastic, glass, fabric, leather and wood is provided. Subsequently, a PSPNET-based deep learning model is constructed to classify these materials from images. The model achieves decent recognition accuracies above 0.60 for leather and wood, which are comparable to human. The machine learning process is also attempted for videos and small-size images to further demonstrate the viability of the image-based machine learning techniques. This study highlights the importance of image-based deep learning studies for materials science, and calls for further machine learning studies to automate and expedite the materials recognition process. Artificial intelligence ; Machine learning ; Computer science ; Deep learning ; Automation ; Process (computing) ; Pattern recognition (psychology) ; Engineering ; Mechanical engineering Machine Learning in Materials Science ; Industrial Vision Systems and Defect Detection ; Thermography and Photoacoustic Techniques
Authors: An Su | Chengwei Zhang | Yuanbin She | Yun‐Fang Yang
Source: Catalysts (article, 2022-11-21)
Field: Materials Science — Materials Chemistry

Exploring Deep Learning for Metalloporphyrins: Databases, Molecular Representations, and Model Architectures Metalloporphyrins have been studied as biomimetic catalysts for more than 120 years and have accumulated a large amount of data, which provides a solid foundation for deep learning to discover chemical trends and structure–function relationships. In this study, key components of deep learning of metalloporphyrins, including databases, molecular representations, and model architectures, were systematically investigated. A protocol to construct canonical SMILES for metalloporphyrins was proposed, which was then used to represent the two-dimensional structures of over 10,000 metalloporphyrins in an existing computational database. Subsequently, several state-of-the-art chemical deep learning models, including graph neural network-based models and natural language processing-based models, were employed to predict the energy gaps of metalloporphyrins. Two models showed satisfactory predictive performance (R2 0.94) with canonical SMILES as the only source of structural information. In addition, an unsupervised visualization algorithm was used to interpret the molecular features learned by the deep learning models. Deep learning ; Computer science ; Artificial intelligence ; Construct (python library) ; Graph ; Machine learning ; Database ; Theoretical computer science Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Asymmetric Hydrogenation and Catalysis
Authors: Nada Ali Hakami | Hanan A. Hosni Mahmoud
Source: Crystals (article, 2022-10-16)
Field: Materials Science — Materials Chemistry

Deep Learning Classification of Crystal Structures Utilizing Wyckoff Positions In materials science, crystal lattice structures are the primary metrics used to measure the structure–property paradigm of a crystal structure. Crystal compounds are understood by the number of various atomic chemical settings, which are associated with Wyckoff sites. In crystallography, a Wyckoff site is a point of conjugate symmetry. Therefore, features associated with the various atomic settings in a crystal can be fed into the input layers of deep learning models. Methods to analyze crystals using Wyckoff sites can help to predict crystal structures. Hence, the main contribution of our article is the classification of crystal classes using Wyckoff sites. The presented model classifies crystals using diffraction images and a deep learning method. The model extracts feature groups including crystal Wyckoff features and crystal geometry. In this article, we present a deep learning model to predict the stage of the crystal structure–property. The lattice parameters and the structure–property commotion values are used as inputs into the deep learning model for training. The structure–property value of a crystal with a lattice width value of one-half millimeter on average is used for learning. The model attains a considerable increase in speed and precision for the real structure–property prediction. The experimental results prove that our proposed model has a fast learning curve, and can have a key role in predicting the structure–property of compound structures. Crystal structure ; Crystal (programming language) ; Artificial intelligence ; Crystal structure prediction ; Property (philosophy) ; Lattice (music) ; Crystallography ; Materials science ; Computer science ; Chemistry ; Physics Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Crystallography and molecular interactions
Authors: Jiandong Deng | Guozhu Jia
Source: Journal of Molecular Liquids (article, 2022-11-28)
Field: Materials Science — Materials Chemistry

Effect of hydrated shell layers on surface tension of electrolyte solutions: Insights from interpretable machine learning Surface tension ; Electrolyte ; Shell (structure) ; Solvation shell ; Surface (topology) ; Molecular dynamics ; Feature (linguistics) ; Function (biology) ; Work (physics) ; Chemistry ; Materials science ; Biological system ; Computer science ; Statistical physics ; Chemical physics ; Thermodynamics ; Ion ; Physics ; Computational chemistry ; Physical chemistry ; Mathematics ; Solvation ; Composite material Machine Learning in Materials Science ; Electrostatics and Colloid Interactions ; Spectroscopy and Quantum Chemical Studies
Authors: Kento Uchida | Genki Sakata | Tetsushi Watari | Yuta Yamakita | Shinichi Shirakawa
Source: Knowledge-Based Systems (article, 2022-10-20)
Field: Materials Science — Materials Chemistry

Generation of microscopic structure of solder material with desirable characteristics based on deep learning Understanding the relationship between material characteristics and microscopic structure is important for the development of solder materials. To clarify this relationship, machine learning approaches are often used to predict material characteristics from microstructural images. Although a trained machine learning model can predict the material characteristics for a given microstructural image, it cannot directly create microstructural images of solder materials with desirable characteristics. Therefore, it is difficult to use machine learning to develop new solder materials. This paper presents a method for generating electron probe micro-analyzer (EPMA) images of a solder with desirable characteristics using deep learning. Our method uses a generative adversarial network (GAN) to generate images, and a convolutional neural network (CNN)-based evaluator to predict their characteristics. A common difficulty in applying machine learning to material science is the lack of training data, which often results in predictions with low accuracy. To address the small dataset problem, we trained the ranking prediction model of the characteristics instead of the regression model. Moreover, we employed transfer learning, in which a CNN model trained on texture datasets was used as the initial model. The experimental results show that the GAN successfully generated EPMA images that were similar to the actual images. The use of the ranking prediction model and transfer learning improved the performance of the CNN-based characteristic evaluator. We then selected promising generated EPMA images using the CNN-based characteristic evaluator and found that the characteristics of the selected EPMA images were consistent with expert experience. Computer science ; Artificial intelligence ; Convolutional neural network ; Machine learning ; Deep learning ; Ranking (information retrieval) ; Pattern recognition (psychology) ; Soldering ; Artificial neural network ; Transfer of learning ; Materials science Machine Learning in Materials Science ; Electron and X-Ray Spectroscopy Techniques ; Integrated Circuits and Semiconductor Failure Analysis
Authors: Chu-I Yang | Yi‐Pei Li
Source: ChemRxiv (preprint, 2022-09-12)
Field: Materials Science — Materials Chemistry

Explainable Uncertainty Quantifications for Deep Learning-Based Molecular Property Prediction Quantifying uncertainty in machine learning is important in new research areas with scarce high-quality data. In this work, we develop an explainable uncertainty quantification method for deep learning-based molecular property prediction. This method can capture aleatoric and epistemic uncertainties separately and attribute the uncertainties to atoms present in the molecule. The atom-based uncertainty method provides an extra layer of explainability to the estimated uncertainties, i.e., one can analyze individual atomic uncertainty values to diagnose the chemical component that introduces uncertainty to the prediction. Our experiments demonstrate that atomic uncertainty can detect unseen chemical structures and identify chemical species whose data are potentially associated with significant noise. Furthermore, we propose a post-hoc calibration method to refine the uncertainty quantified by ensemble models for better confidence interval estimates. This work improves uncertainty calibration and provides a framework for assessing whether and why a prediction should be considered unreliable. Uncertainty quantification ; Measurement uncertainty ; Calibration ; Computer science ; Property (philosophy) ; Uncertainty analysis ; Machine learning ; Artificial intelligence ; Noise (video) ; Interval (graph theory) ; Standard uncertainty ; Data mining ; Mathematics ; Statistics ; Simulation Machine Learning in Materials Science ; Computational Drug Discovery Methods ; History and advancements in chemistry
Authors: A Yann Battie | Adrià Canós Valero | David Horwat | Aotmane En Naciri
Source: Optics Letters (article, 2022-03-25)
Field: Materials Science — Materials Chemistry

Rapid ellipsometric determination and mapping of alloy stoichiometry with a neural network Due to their tunable physical and chemical properties, alloys are of fundamental importance in material science. The determination of stoichiometry is crucial for alloy engineering. Classical characterization tools such as energy-dispersive x-ray spectroscopy (EDX) are time consuming and cannot be performed in an ambient atmosphere. In this context, we introduce a new methodology to determine the stoichiometry of alloys from ellipsometric measurements. This approach, based on the analysis of ellipsometric spectra by an artificial neural network (ANN), is applied to electrum alloys. We demonstrate that the accuracy of this approach is of the same order of magnitude as that of EDX. In addition, the ANN analysis is sufficiently robust that it can be used to characterize rough alloys. Finally, we demonstrate that the exploitation of ellipsometric maps with the ANN is a powerful tool to determine composition gradients in alloys. Stoichiometry ; Ellipsometry ; Artificial neural network ; Alloy ; Characterization (materials science) ; Spectroscopy Machine Learning in Materials Science ; Electron and X-Ray Spectroscopy Techniques ; X-ray Diffraction in Crystallography
Authors: Mahdi Jokar | Xiaolei Guo | G. S. Frankel
Source: CORROSION (article, 2022-10-14)
Field: Materials Science — Materials Chemistry

Machine Learning Approaches to Model Galvanic Corrosion of Coated Al Alloy Systems Previous studies have shown how galvanic coupling susceptibility between stainless steel 316 or titanium alloy fasteners and coated aluminum alloy 7075-T6 depends on the chosen coating system and environmental factors such as relative humidity (RH) and chloride concentration. In this study, several machine learning models were developed to predict, analyze, and quantify galvanic corrosion arising between relatively noble fasteners and coated aluminum alloy panels. Different independent factors including pretreatment, primer coating, topcoat, RH, chloride concentration, fastener material, fastener quantity, existence of a defect, type of environment, and time of wetness were evaluated for their effect on galvanic coupling lost volume. Artificial neural networks (ANN), random forest regression (RFR), and multiple linear regression (MLR) were used to develop damage functions for galvanic corrosion. ANN, RFR, and MLR models all showed a reasonable fit for lost volume as a function of different inputs. Galvanic cell ; Materials science ; Alloy ; Corrosion ; Relative humidity ; Galvanic corrosion ; Metallurgy ; Coating ; Coupling (piping) ; Linear regression ; Composite material ; Computer science ; Machine learning ; Thermodynamics Corrosion Behavior and Inhibition ; Non-Destructive Testing Techniques ; Hydrogen embrittlement and corrosion behaviors in metals