jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Mohamad Ghaffarian Niasar | Xiaolei Wang | Respicius Clemence Kiiza
Source: Energies (article, 2021-01-15)
Field: Materials Science — Materials Chemistry

Review of Partial Discharge Activity Considering Very-Low Frequency and Damped Applied Voltage When detecting the presence of partial discharge (PD) activity in the insulation system in high-voltage equipment, the excitation voltages at variable frequency have been widely used instead of power-frequency (50/60 Hz) sinusoidal voltage in order to reduce the charging power. This work reviews the relevant research on PD activity at very low frequency (VLF) method, including sinusoidal or cosine-rectangular voltage shape, and damped AC (DAC) method. Based on the research history and development status, some major PD characteristics, such as PD inception voltage (PDIV), PD amplitude, PD charge, PD phase-resolved pattern, and several hot issues, such as surface charge decay and statistical time lag, have been discussed. Moreover, the advantages, disadvantages, and applied conditions of two reviewed methods has been summarized. Finally, the prospects have been made on the main development trends of this research field in the future. Partial discharge ; Voltage ; Amplitude ; Power (physics) ; Electrical engineering ; Work (physics) ; Phase (matter) ; Materials science ; Electronic engineering ; Physics ; Engineering ; Mechanical engineering ; Optics High voltage insulation and dielectric phenomena ; Power Transformer Diagnostics and Insulation ; Lightning and Electromagnetic Phenomena
Authors: Andrei Lupu | Hengyuan Hu | Jakob Foerster
Source:  (article, 2021-05-03)
Field: Chemistry — Inorganic Chemistry

Trajectory Diversity for Zero-Shot Coordination We study the problem of zero-shot coordination (ZSC), where agents must independently produce strategies for a collaborative game that are compatible with novel partners not seen during training. In particular, our first contribution is to consider the need for diversity in generating such agents. Because self-play agents control their own trajectory distribution during training, their policy only performs well on this exact distribution. As a result, they achieve low scores in ZSC, since playing with another agent is likely to put them in situations they have not encountered during training. To address this issue, we train a common best response (BR) to a population of agents, which we regulate to be as diverse as possible. For that purpose, we introduce Trajectory Diversity (TrajeDi) - a differentiable objective for generating diverse reinforcement learning (RL) policies. We present TrajeDi as a generalization of the Jensen-Shannon divergence (JSD) between policies and motivate it experimentally in a simple matrix game, where it allows to find the unique ZSC-optimal solution. Diversity (politics) ; Zero (linguistics) ; Trajectory ; Shot (pellet) ; Computer science ; Physics ; Political science ; Quantum mechanics ; Materials science ; Law Radioactive element chemistry and processing
Authors: B. Madhavilatha | D. Parajuli | K. Jayadev | Ch Komali | N. Murali | V. Veeraiah | K. Samatha | J Ortiz-Quinonez | U Pal | M Villanueva | Structural | P Himakar | N Murali | D Parajuli | V Veeraiah | K Samatha | T Mammo | K Batoo | M Hadi | E Raslan | S Adil | A Ramakrishna | T Sagar | T Rao | K S; Naidu | R Kambale | P Shaikh | C Bhosale | K Rajpure | Y Kolekar | ; Mehak | Somnath | I Sharma | G Kumar | G Raju | M Prasad | B Suresh | D Babu | M Kiran | M Wegayehu | B Babu | S Mercy | Y Ramakrishna | F Ansari | A Sobhani | M Salavati-Niasari | S Desai | S Shirsath | M Khan | S Patange | S Margarette | I Nlebedim | D Jiles | P Rao | V Raghavendra | B Suryanarayana | T Paulos | P Varma | R Prasad | K Chandramouli | T Dippong | E Levei | I Deac | E Neag | O Cadar | Y Sileshi | T Arunamani | S Dabagh | A Ati | S Ghoshal | S Zare | R Rosnan | A Jbara | Z Othaman | C Kumari | R Vemuri
Source: Biointerface Research in Applied Chemistry (article, 2021-06-10)
Field: Materials Science — Materials Chemistry

Effect of Cu Substitution on Magnetic Properties of Co0.6Ni0.4Fe2O4 Nanoferrites This work is focusing on synthesizing the cobalt nanoferrite materials substituted by copper forming Co0.6Ni0.4-xCuxFe2O4 with x = 0.0, 0.1, and 0.3 using the sol-gel auto-combustion method. The phase analysis of XRD showed the spinel structure with the lattice parameter in the range 8.36-8.39 Å. FESEM image showed the grain size initially decreasing and then increasing with Cu concentration. The FTIR curve's two absorption bands in the specified range of frequency assured the spinel nano ferrite structure. The values of remanent ratios obtained from VSM showed their isotropic nature forming single domain ferrimagnetic particles. Spinel ; Ferrimagnetism ; Materials science ; Ferrite (magnet) ; Analytical Chemistry (journal) ; Lattice constant ; Copper ; Cobalt ; Fourier transform infrared spectroscopy ; Coercivity ; Mineralogy ; Chemical engineering ; Metallurgy ; Diffraction ; Composite material ; Chemistry ; Condensed matter physics ; Magnetization ; Optics ; Chromatography Magnetic Properties and Synthesis of Ferrites
Authors: Qifeng Jiang | Sydnee Wong | Rebekka S. Klausen
Source: Polymer Chemistry (article, 2021-01-01)
Field: Chemistry — Inorganic Chemistry

Effect of polycyclosilane microstructure on thermal properties Thermal characterization of polysilanes has focused on the influence of organic side chains, whereas little is understood about the influence of silane backbone microstructure on thermal stability, phase properties, and pyrolysis. Microstructure ; Pyrolysis ; Silane ; Thermal stability ; Thermal ; Characterization (materials science) ; Side chain ; Materials science ; Chemical engineering ; Phase (matter) ; Chemistry ; Polymer chemistry ; Composite material ; Organic chemistry ; Polymer ; Nanotechnology ; Thermodynamics Synthesis and characterization of novel inorganic/organometallic compounds ; Organoboron and organosilicon chemistry ; Advanced ceramic materials synthesis
Authors: Satish Bhalerao | Nitin Ambhore | Munjadas Kadam | M Javidan | J Kim | D Rajak | D Pagar | R Kumar | C Pruncu | J Hadi | S Aziz | M Mustafa | M Hamsan | R Abdulwahid | M Kadir | H Ghareeb | E Jayamani | G Nair | K Soon | Z Zhao | P Bai | W Du | B Liu | D Pan | R Das | C Liu | Z Guo | X Zhang | T Hu | J Rufner | T Lagrange | G Campbell | E Lavernia | J Schoenung | K Benthem | P Mallick | A Adebisi | M Maleque | M Rahman | D Boccaccini | D Cannio | E Bernardo | A Boccaccini | W Krenkel | S Flauder | G Puchas | S Zachariah | S Shenoy | D Pai | F El-Abbassi | M Assarar | R Ayad | A Bourmaud | C Baley | J Wang | Y Wang | F Wang | S Li | J Xiao | Q Shen | W Yang | H Ali | R Akrami | S Fotouhi | F Pashmforoush | C Fragassa | M Fotouhi | I Plea | P Noingher | S Schlgl | C Sumereder | M Muhr | D Zhang | M He | S Qin | J Yu | U Babu | N Sai | R Sahu | P Dharmavarapu | M Reddy | Z Khan | A Arsad | Z Mohamad | U Habib | M Zaini | S Huang | Q Fu | L Yan | B Kasal | P Irwin | W Zhang | Y Cao | X Fang | D Tan | V Mahesh | S Joladarashi | S Kulkarni | X Jiang | J Song
Source: Biointerface Research in Applied Chemistry (review, 2021-12-10)
Field: Materials Science — Materials Chemistry

Polymer Matrix Composite in High Voltage Applications: A Review In recent years, natural fibers have become more widely used as reinforcement in polymer composites to generate low-cost products. Fibrous reinforcements in polymer matrices lead to good mechanical and electrical properties for composite materials. Depending on the grade and orientation, composites can be one-fifth the weight of steel while offering similar or better stiffness and strength. In addition, unlike steel or aluminum, composites do not rust or corrode. Composite materials reinforcing phase gives durability, strength, and stiffness. Composite materials have traditionally been employed as structural materials. Composite materials are increasingly being used in electrical applications such as bushings, circuit breakers, coupling capacitors, and so on, thanks to the growing growth of the electrical sector. The design parameters for structural and electrical composites differ dramatically due to the enormous differences in property requirements. Depending on the application, structural composites. Structural composites prioritize sufficient strength and modulus, while electrical composites prioritize superior dielectric constant, thermal conductivity and low thermal expansion, and shielding effectiveness. In the electrical industry, low density is desired because it allows for weight reduction. It is also desirable to have a high strength-weight ratio and dielectric properties. This paper provides a brief review of the properties of polymer composite materials and their application in the high voltage industry. Composite material ; Materials science ; Specific strength ; Composite number ; Dielectric ; Stiffness ; Advanced composite materials High voltage insulation and dielectric phenomena ; Thermal Analysis in Power Transmission ; Electrical Fault Detection and Protection
Authors: Mohammed El Amine Slama | Maurizio Albano | A. Haddad | R.T. Waters | Oliver Cwikowski | Ibrahim Iddrissu | Jon Knapper | Oliver Scopes
Source: Energies (article, 2021-05-18)
Field: Materials Science — Materials Chemistry

Monitoring of Dry Bands and Discharge Activities at the Surface of Textured Insulators with AC Clean Fog Test Conditions The aim of this study is the presentation of the results of an in-lab comparative study of electrical and thermal monitoring of artificially polluted, HTV-textured silicone rubber insulators, with different pollution levels. This work is a preliminary study of an in-situ monitoring of 400 kV SiR textured in a polluted environment. The results showed that the rms leakage current magnitude and pulses, and the average dissipated power depended on the pollution levels and the dry-bands formation. The discharge activity and their nature are governed by the pollution level and the voltage. A differentiation and a quantification between dry-band discharge onset and dry-band arc inception is highlighted. Silicone rubber ; Pollution ; Electric arc ; Environmental science ; Insulator (electricity) ; Contamination ; Materials science ; Remote sensing ; Environmental engineering ; Composite material ; Chemistry ; Electrode ; Geology High voltage insulation and dielectric phenomena ; Power Transformer Diagnostics and Insulation ; Electrical Fault Detection and Protection
Authors: Yunxing Zuo | Mingde Qin | Chi Chen | Weike Ye | Xiangguo Li | Jian Luo | Shyue Ping Ong
Source: arXiv (Cornell University) (preprint, 2021-04-20)
Field: Materials Science — Materials Chemistry

Accelerating Materials Discovery with Bayesian Optimization and Graph Deep Learning Machine learning (ML) models utilizing structure-based features provide an efficient means for accurate property predictions across diverse chemical spaces. However, obtaining equilibrium crystal structures typically requires expensive density functional theory (DFT) calculations, which limits ML-based exploration to either known crystals or a small number of hypothetical crystals. Here, we demonstrate that the application of Bayesian optimization with symmetry constraints using a graph deep learning energy model can be used to perform "DFT-free" relaxations of crystal structures. Using this approach to significantly improve the accuracy of ML-predicted formation energies and elastic moduli of hypothetical crystals, two novel ultra-incompressible hard materials MoWC2 (P63/mmc) and ReWB (Pca21) were identified and successfully synthesized via in-situ reactive spark plasma sintering from a screening of 399,960 transition metal borides and carbides. This work addresses a critical bottleneck to accurate property predictions for hypothetical materials, paving the way to ML-accelerated discovery of new materials with exceptional properties. Bottleneck ; Bayesian optimization ; Spark plasma sintering ; Materials science ; Density functional theory ; Computer science ; Crystal (programming language) ; Bayesian probability ; Algorithm ; Statistical physics ; Artificial intelligence ; Physics ; Computational chemistry ; Sintering ; Chemistry ; Composite material Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Computational Drug Discovery Methods
Authors: Jize Zhang | Bhavya Kailkhura | T. Yong-Jin Han
Source: ACS Omega (article, 2021-05-04)
Field: Materials Science — Materials Chemistry

Leveraging Uncertainty from Deep Learning for Trustworthy Material Discovery Workflows In this paper, we leverage predictive uncertainty of deep neural networks to answer challenging questions material scientists usually encounter in machine learning-based material application workflows. First, we show that by leveraging predictive uncertainty, a user can determine the required training data set size to achieve a certain classification accuracy. Next, we propose uncertainty-guided decision referral to detect and refrain from making decisions on confusing samples. Finally, we show that predictive uncertainty can also be used to detect out-of-distribution test samples. We find that this scheme is accurate enough to detect a wide range of real-world shifts in data, e.g., changes in the image acquisition conditions or changes in the synthesis conditions. Using microstructure information from scanning electron microscope (SEM) images as an example use case, we show that leveraging uncertainty-aware deep learning can significantly improve the performance and dependability of classification models. Computer science ; Leverage (statistics) ; Workflow ; Artificial intelligence ; Deep learning ; Machine learning ; Dependability ; Artificial neural network ; Range (aeronautics) ; Trustworthiness ; Data mining ; Database Machine Learning in Materials Science ; Advanced X-ray and CT Imaging ; Electron and X-Ray Spectroscopy Techniques
Authors: F J Domínguez-Gutiérrez | J Byggmästar | K Nordlund | F Djurabekova | U von Toussaint
Source: Modelling and Simulation in Materials Science and Engineering (article, 2021-03-24)
Field: Materials Science — Materials Chemistry

Computational study of crystal defect formation in Mo by a machine learning molecular dynamics potential Abstract In this work, we study the damage in crystalline molybdenum material samples due to neutron bombardment in a primary knock-on atom (PKA) range of 0.5–10 keV at room temperature. We perform classical molecular dynamics (MD) simulations using a previously derived machine learning (ML) interatomic potential based on the Gaussian approximation potential (GAP) framework. We utilize a recently developed software workflow for fingerprinting and visualizing defects in damaged crystal structures to analyze the Mo samples with respect to the formation of point defects during and after a collision cascade. As a benchmark, we report results for the total number of Frenkel pairs (a self-interstitial atom and a single vacancy) formed and atom displacements as a function of the PKA energy. A comparison to results obtained using an embedded atom method (EAM) potential is presented to discuss the advantages and limits of the MD simulations utilizing ML-based potentials. The formation of Frenkel pairs follows a sublinear scaling law as ξ b where b is a fitting parameter and ξ = E PKA / E 0 with E 0 as a scaling factor. We found that the b = 0.54 for the GAP MD results and b = 0.667 for the EAM simulations. Although the average number of total defects is similar for both methods, the MD results show different atomic geometries for complex point defects, where the formation of crowdions by the GAP potential is closer to the DFT-based expectation. Finally, ion beam mixing results for GAP MD simulations are in a good agreement with experimental mixing efficiency data. This indicates that the modeling of atom relocation in cascades by machine learned potentials is suited to interpret the corresponding experimental findings. Molecular dynamics ; Atom (system on chip) ; Crystal (programming language) ; Frenkel defect ; Crystallographic defect ; Interatomic potential ; Scaling ; Mixing (physics) Machine Learning in Materials Science ; Fusion materials and technologies ; Nuclear Materials and Properties
Authors: Kazuki Ishizaki | Ryota Sugimoto | Yuki Hagiwara | Hideko Koshima | Takuya Taniguchi | Toru Asahi
Source: CrystEngComm (article, 2021-01-01)
Field: Materials Science — Materials Chemistry

Actuation performance of a photo-bending crystal modeled by machine learning-based regression The bending deflection and blocking force of photo-bending crystals of different sizes were experimentally measured at various light intensities, and then modeled by the machine learning-based regression. Deflection (physics) ; Bending ; Materials science ; Regression analysis ; Structural engineering ; Optics ; Computer science ; Composite material ; Machine learning ; Engineering ; Physics Photochromic and Fluorescence Chemistry ; Photonic Crystals and Applications ; Advanced Sensor and Energy Harvesting Materials
Authors: Swetha Chittam | Balakrishna Gokaraju | Zhigang Xu | Jagannathan Sankar | Kaushik Roy
Source: Applied Sciences (article, 2021-09-16)
Field: Materials Science — Materials Chemistry

Big Data Mining and Classification of Intelligent Material Science Data Using Machine Learning There is a high need for a big data repository for material compositions and their derived analytics of metal strength, in the material science community. Currently, many researchers maintain their own excel sheets, prepared manually by their team by tabulating the experimental data collected from scientific journals, and analyzing the data by performing manual calculations using formulas to determine the strength of the material. In this study, we propose a big data storage for material science data and its processing parameters information to address the laborious process of data tabulation from scientific articles, data mining techniques to retrieve the information from databases to perform big data analytics, and a machine learning prediction model to determine material strength insights. Three models are proposed based on Logistic regression, Support vector Machine SVM and Random Forest Algorithms. These models are trained and tested using a 10-fold cross validation approach. The Random Forest classification model performed better on the independent dataset, with 87% accuracy in comparison to Logistic regression and SVM with 72% and 78%, respectively. Random forest ; Big data ; Computer science ; Support vector machine ; Data mining ; Logistic regression ; Machine learning ; Artificial intelligence ; Data science Machine Learning in Materials Science ; Aluminum Alloy Microstructure Properties ; Welding Techniques and Residual Stresses
Authors: Onur Çaylak | Björn Baumeier
Source: Journal of Chemical Theory and Computation (article, 2021-07-27)
Field: Materials Science — Materials Chemistry

Machine Learning of Quasiparticle Energies in Molecules and Clusters We present a Δ-machine learning approach for the prediction of GW quasiparticle energies (ΔMLQP) and photoelectron spectra of molecules and clusters, using orbital-sensitive representations (OSRs) based on molecular Cartesian coordinates in kernel ridge regression-based supervised learning. Coulomb matrix, bag-of-bond, and bond-angle-torsion representations are made orbital-sensitive by augmenting them with atom-centered orbital charges and Kohn-Sham orbital energies, both of which are readily available from baseline calculations at the level of density functional theory (DFT). We first illustrate the effects of different constructions of the OSRs on the prediction of frontier orbital energies of 22k molecules of the QM8 data set and show that it is possible to predict the full photoelectron spectrum of molecules within the data set using a single model with a mean absolute error below 0.1 eV. We further demonstrate that the OSR-based ΔMLQP captures the effects of intra- and intermolecular conformations in application to water monomers and dimers. Finally, we show that the approach can be embedded in multiscale simulation workflows, by studying the solvatochromic shifts of quasiparticle and electron-hole excitation energies of solvated acetone in a setup combining molecular dynamics, DFT, the GW approximation, and the Bethe-Salpeter equation. Our findings suggest that the ΔMLQP model allows us to predict quasiparticle energies and photoelectron spectra of molecules and clusters with GW accuracy at DFT cost. Quasiparticle ; Physics ; Molecular orbital ; Molecule ; Density functional theory ; Atomic physics ; Molecular physics ; Quantum mechanics Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Catalysis and Oxidation Reactions
Authors: Yuri Haraguchi | Yasuhiko Igarashi | Hiroaki Imai | Yuya Oaki
Source: Advanced Theory and Simulations (article, 2021-09-06)
Field: Materials Science — Materials Chemistry

Size‐Distribution Control of Exfoliated Nanosheets Assisted by Machine Learning: Small‐Data‐Driven Materials Science Using Sparse Modeling Abstract 2D materials exhibit emergent properties originating from their characteristic nanostructures. In general, monolayered and few‐layered nanosheets are obtained by exfoliation of the precursor layered materials. However, lateral‐size distribution of the nanosheets is not easily controlled through the exfoliation because of the unpredictable down‐sizing processes. The present work shows selective syntheses of transition‐metal‐oxide nanosheets with the monodisperse and polydisperse lateral sizes by the assistance of machine learning on small experimental data. The precursor layered composites of host transition‐metal oxides and interlayer organic guests are exfoliated into the surface‐modified nanosheets in organic dispersion media. A prediction model of the size distribution is constructed by sparse modeling, a method of machine learning, on the experimental data. The host‐guest‐medium combinations achieving the monodisperse and polydisperse lateral sizes are recommended by the prediction model. Therefore, the nanosheets with the controlled lateral‐size distribution are selectively obtained in a limited number of the experiments. Moreover, self‐assembly of the polydispersed nanosheets forms the homogeneous thin film exhibiting interference color. The prediction model and its construction method can be applied to the other 2D materials. Moreover, the present work implies that sparse modeling is an effective approach for small‐data‐driven materials science. Dispersity ; Materials science ; Exfoliation joint ; Nanotechnology ; Nanostructure ; Nanoparticle ; Sizing ; Dispersion (optics) ; Graphene ; Optics ; Polymer chemistry Graphene research and applications ; Covalent Organic Framework Applications ; 2D Materials and Applications
Authors: Sanjay Kumar Pal | Kinsuk Naskar
Source: Materials Today Communications (article, 2021-02-27)
Field: Materials Science — Polymers and Plastics

Machine learning model predict stress-strain plot for Marlow hyperelastic material design Hyperelastic material ; Materials science ; Plot (graphics) ; Coefficient of determination ; Artificial intelligence ; Mean squared error ; Workflow ; Computer science ; Linear regression ; Machine learning ; Statistics ; Mathematics ; Structural engineering ; Database ; Finite element method ; Engineering Polymer Nanocomposites and Properties ; Dielectric materials and actuators ; Polymer crystallization and properties