jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Ashish Paramane | K. Sathish Kumar | Mohd Hafizi Ahmad | M. A. M. Piah
Source: IET Science Measurement & Technology (article, 2018-08-08)
Field: Materials Science — Materials Chemistry

Space charge and conductivity measurement of XLPE nanocomposites for HVDC insulation–permittivity as a nanofiller selection parameter Cross‐linked polyethylene (XLPE) insulation is successfully used for high‐voltage AC transmission. However, it is still under development for high‐voltage DC application due to space charge accumulation, which distorts the internal electrical field distribution and leads to its aging/failure. Therefore, the space charge should be measured and carefully analysed. On the other side, conductivity measurement helps to forecast the degradation probability of the insulation. Higher conductivity represents the severe degradation. Nanofiller addition, such as SiO 2 , TiO 2 , MgO and so on (&lt;5 wt%), particularly surface‐modified nanofiller due to its better dispersion significantly suppresses the space charge accumulation and conductivity. Nevertheless, the choice of suitable nanofiller has still remained a challenge. With this context, space charge and conductivity of XLPE‐silica and XLPE‐magnesium oxide (MgO) surface‐modified nanocomposites are measured. This study proposes a parameter for nanofiller selection that will deliver optimal properties for the intended application. Results show that nanocomposites with higher nanofiller permittivity (i.e. MgO) have less space charge accumulation and low conductivity and are justified with the help of a band gap theory model. Materials science ; Permittivity ; Space charge ; Conductivity ; Nanocomposite ; Selection (genetic algorithm) ; Composite material ; Partial discharge ; Dielectric ; Electrical engineering ; Computer science ; Voltage ; Physics ; Optoelectronics ; Engineering High voltage insulation and dielectric phenomena ; Dielectric materials and actuators ; Electrical Fault Detection and Protection
Authors: Yonglin Xu | XiangMeng WANG | Xin Li | Lili Xi | Jianyue Ni | Wenhao Zhu | Wu Zhang | Jiong Yang
Source: Scientia Sinica Technologica (article, 2018-12-05)
Field: Materials Science — Materials Chemistry

New materials band gap prediction based on the high-throughput calculation and the machine learning <p indent="0mm">The bandgap often plays an important role in functional materials applications. For example, optoelectronic materials are generally wide bandgap semiconductors, while thermoelectric materials are narrow bandgap semiconductor materials. Therefore, predicting the bandgap rapidly and accurately for a given class of materials structures has great scientific importance for the functional materials applications. However, considering that the method of obtaining high-precision band gaps based on first-principles high-throughput calculations is time consuming and inefficient, and it is also not realistic to systematically measure a large number of material system band gaps. Machine learning methods based the statistics may be a promising alternative. This paper designs an ensemble learning model for effectively and accurately predicting bandgap values. Based on the calculated band gap values of diamond-like structures in thermoelectric materials, on the one hand, single component substitution strategy was used to generate large quantities of similar compounds, and the repetitive structures was filtered out by using the structural repeatability examination technique, resulting in 356 unique material structures. On the other hand, in combination with machine learning techniques, an efficient band gap prediction model was constructed, and by which the band gap values ​​of 50 similar material systems are predicted and verified. As is the result of the experiment, this prediction model has 77.73% accuracy. It is enough robustness and stability to be widely used in thermoelectric materials application scenarios which require large band gap prediction. Throughput ; Computer science ; Machine learning ; Artificial intelligence ; Materials science ; Telecommunications Machine Learning in Materials Science
Authors: Jatin Kumar | Qianxiao Li | Karen Yuanting Tang | Tonio Buonassisi | Anibal L. Gonzalez-Oyarce | Jun Ye
Source: ChemRxiv (preprint, 2018-12-28)
Field: Materials Science — Materials Chemistry

Machine Learning Enables Polymer Cloud-Point Engineering via Inverse Design &lt;div&gt;&lt;div&gt;&lt;div&gt;&lt;p&gt;Inverse design is an outstanding challenge in disordered systems with multiple length scales such as polymers, particularly when designing polymers with desired phase behavior. We demonstrate high-accuracy tuning of poly(2-oxazoline) cloud point via machine learning. With a design space of four repeating units and a range of molecular masses, we achieve an accuracy of 4°C root mean squared error (RMSE) in a temperature range of 24– 90°C, employing gradient boosting with decision trees. The RMSE is &gt;3x better than linear and polynomial regression. We perform inverse design via particle-swarm optimization, predicting and synthesizing 17 polymers with constrained design at 4 target cloud points from 37 to 80°C. Our approach challenges the status quo in polymer design with a machine learning algorithm, that is capable of fast and systematic discovery of new polymers.&lt;/p&gt;&lt;/div&gt;&lt;/div&gt;&lt;/div&gt; Mean squared error ; Particle swarm optimization ; Inverse ; Polymer ; Point cloud ; Computer science ; Mathematics ; Algorithm ; Artificial intelligence ; Materials science ; Statistics ; Geometry ; Composite material Machine Learning in Materials Science ; Optical Polarization and Ellipsometry ; Phase Equilibria and Thermodynamics
Authors: Yumeng Li | Weirong Xiao | Pingfeng Wang
Source:  (article, 2018-11-09)
Field: Materials Science — Materials Chemistry

Uncertainty Quantification of Artificial Neural Network Based Machine Learning Potentials Atomistic simulations play an important role in the material analysis and design by being rooted in the accurate first principles methods that free from empirical parameters and phenomenological models. However, successful applications of MD simulations largely depend on the availability of efficient and accurate force field potentials used for describing the interatomic interactions. As a powerful tool revolutionizing many areas in science and technology, machine learning techniques have gained growing attentions in the field of material science and engineering due to their potentials to accelerate the material discovery through their applications in surrogate model assisted material design. Despite tremendous advantages of employing machine learning techniques for the development of force field potentials as compared to conventional approaches, the uncertainty involved in the machine learning interpolated atomic potential energy surface has not drew much attention although it is an important issue. In this paper, the uncertainty quantification study is performed for the machine learning interpolated atomic potentials, and applied to the titanium dioxide (TiO2), an industrially relevant and well-studies material. The study results indicated that quantifying uncertainties is an indispensable task that must be performed along with the atomistic simulation process for a successful application of the machine learning based force field potentials. Machine learning ; Computer science ; Artificial intelligence ; Field (mathematics) ; Artificial neural network ; Process (computing) ; Force field (fiction) Machine Learning in Materials Science
Authors: Yousef Mohammadi | Mohammad Reza Saeb | Alexander Penlidis
Source: Macromolecular Theory and Simulations (article, 2018-01-09)
Field: Materials Science — Materials Chemistry

Heuristic Search Strategy for Transforming Microstructural Patterns to Optimal Copolymerization Recipes Abstract Manipulation and optimization of copolymer microstructure for tailoring final properties is of great importance in macromolecular science and engineering. Uncovering the complexities of the interrelationships between copolymerization recipe and copolymer microstructure (a challenging field of study in its own right) is a multiobjective optimization problem, which has attracted a lot of attention in the last 10–15 years. In the present study, a powerful optimizer is developed based on the Non‐dominated Sorting Genetic Algorithm (NSGA‐II) for transforming desired microstructural copolymerization profiles, including molecular weight distribution and chemical composition distribution, back to optimal copolymerization recipes and operating conditions. The optimizer developed has the beneficial features of robust machine learning and multiobjective optimization based upon heuristic search strategies. The metallocene‐catalyzed ethylene/α‐olefin copolymerization is selected as a sufficiently complex system to challenge the proposed optimization tool. The developed computer code is used to explore copolymerization recipes (polymerization temperature and concentrations of ethylene, 1butene, cocatalyst, and hydrogen) needed to synthesize copolymers having desired microstructural features. Based on the results obtained, it is now possible to produce various grades or tailor‐make the copolymer structure by suggesting the “best” copolymerization recipe/conditions as reliably as possible. Copolymer ; Materials science ; Heuristic ; Computer science ; Ethylene ; Polymerization ; Polymer science ; Algorithm ; Polymer ; Chemistry ; Organic chemistry ; Catalysis ; Artificial intelligence ; Composite material Machine Learning in Materials Science ; Polymer crystallization and properties ; Process Optimization and Integration
Authors: Zakieh Alizadeh | M. R. Mohammadizadeh
Source: Physica C Superconductivity (article, 2018-12-31)
Field: Materials Science — Materials Chemistry

Predicting electron-phonon coupling constants of superconducting elements by machine learning Superconductivity ; Coupling constant ; Condensed matter physics ; Phonon ; Coupling (piping) ; Electron ; Physics ; Materials science ; Quantum mechanics ; Metallurgy Machine Learning in Materials Science ; Nuclear Physics and Applications ; Nuclear Materials and Properties
Authors: Konstantin Gubaev | Evgeny V. Podryabinkin | Gus L. W. Hart | Alexander V. Shapeev
Source: Computational Materials Science (preprint, 2018-09-27)
Field: Materials Science — Materials Chemistry

Accelerating high-throughput searches for new alloys with active learning of interatomic potentials Interatomic potential ; Convex hull ; Lattice (music) ; Cluster (spacecraft) ; Computer science ; Throughput ; Cluster expansion ; Space (punctuation) ; Algorithm ; Statistical physics ; Materials science ; Computational science ; Regular polygon ; Physics ; Chemistry ; Computational chemistry ; Molecular dynamics ; Mathematics ; Thermodynamics Machine Learning in Materials Science ; Advanced Materials Characterization Techniques ; Electron and X-Ray Spectroscopy Techniques
Authors: P. Mikheenko
Source: Journal of Superconductivity and Novel Magnetism (article, 2018-12-18)
Field: Chemistry — Physical and Theoretical Chemistry

Possible Superconductivity in the Brain Superconductivity ; Quantum ; Quantum computer ; Quantum information ; Quantum fluctuation ; Graphene ; High-temperature superconductivity Advanced Physical and Chemical Molecular Interactions ; Microtubule and mitosis dynamics ; Functional Brain Connectivity Studies
Authors: Zelun Li | Xuling Chang | Ying Wang | Changting Wei | Juan Wang | Kelong Ai | Ye Zhang | Lehui Lu
Source: Analytical Chemistry (article, 2018-07-31)
Field: Chemistry — Organic Chemistry

Point-and-Shoot Strategy for Identification of Alcoholic Beverages The lack of point-and-shoot detection methods of alcoholic beverages (ABs) available for ordinary people is a common cause of the overflow of various counterfeit ABs. Here, we, for the first time, provide a point-and-shoot identification for ABs via a smartphone. Using density functional theory, we find the binding ability of an ethylenediamine-functionalized polydiacetylene (P4) can reach a desirable trade-off among organic molecules in ABs. We therefore construct a versatile array consisting of P4 with different concentrations, which is able to generate unique color response patterns toward different ABs. The color response patterns are further analyzed by a custom-designed image processing algorithm based on machine learning. Finally, the identification of ABs can be achieved by capturing and analyzing the color pattern using an imaging recognition programmer on a smartphone, and the entire process is as fast as quick response (QR) code scanning. Our point-and-shoot strategy makes the identification of ABs accessible to every mobile phone user. Identification (biology) ; Chemistry ; Point (geometry) ; Shoot ; Counterfeit ; Mobile device ; Ethylenediamine ; Artificial intelligence ; Pattern recognition (psychology) ; Computer science ; Botany ; Mathematics ; World Wide Web Polydiacetylene-based materials and applications ; Advanced Chemical Sensor Technologies ; Luminescence and Fluorescent Materials
Authors: Maria M. Trush | Vasyl Kovalishyn | Alla D. Ocheretniuk | Лариса Калашнікова | В. М. Прокопенко | O. V. Holovchenko | Oleksandr Kobzar | В. С. Броварец | Larysa Metelytsia
Source: Letters in Drug Design & Discovery (article, 2018-02-20)
Field: Chemistry — Organic Chemistry

New 1,3-oxazolylphosphonium Salts as Potential Biocides: QSAR Study, Synthesis, Antibacterial Activity and Toxicity Evaluation Quantitative structure–activity relationship ; Chemistry ; Antibacterial activity ; Phosphonium ; Combinatorial chemistry ; Toxicity ; Cationic polymerization ; Stereochemistry ; Organic chemistry ; Bacteria Synthesis and biological activity ; Synthesis and Characterization of Heterocyclic Compounds ; Synthesis and Reactivity of Sulfur-Containing Compounds
Authors: Benoı̂t Piro | Giorgio Mattana | Steeve Reisberg
Source: Biosensors (review, 2018-07-04)
Field: Materials Science — Polymers and Plastics

Transistors for Chemical Monitoring of Living Cells We review here the chemical sensors for pH, glucose, lactate, and neurotransmitters, such as acetylcholine or glutamate, made of organic thin-film transistors (OTFTs), including organic electrochemical transistors (OECTs) and electrolyte-gated OFETs (EGOFETs), for the monitoring of cell activity. First, the various chemicals that are produced by living cells and are susceptible to be sensed in-situ in a cell culture medium are reviewed. Then, we discuss the various materials used to make the substrate onto which cells can be grown, as well as the materials used for making the transistors. The main part of this review discusses the up-to-date transistor architectures that have been described for cell monitoring to date. Transistor ; Substrate (aquarium) ; Electrolyte ; Nanotechnology ; Materials science ; Thin-film transistor ; Electrochemistry ; Optoelectronics ; Chemistry ; Electrode ; Biology ; Electrical engineering ; Engineering ; Ecology Conducting polymers and applications ; Analytical Chemistry and Sensors ; Electrochemical sensors and biosensors
Authors: Xiaoyan Ma | Yanbin Zhang | Hui Cao | Shiliang Zhang | Yan Zhou
Source: Journal of Spectroscopy (article, 2018-01-01)
Field: Chemistry — Analytical Chemistry

Nonlinear Regression with High-Dimensional Space Mapping for Blood Component Spectral Quantitative Analysis Accurate and fast determination of blood component concentration is very essential for the efficient diagnosis of patients. This paper proposes a nonlinear regression method with high-dimensional space mapping for blood component spectral quantitative analysis. Kernels are introduced to map the input data into high-dimensional space for nonlinear regression. As the most famous kernel, Gaussian kernel is usually adopted by researchers. More kernels need to be studied for each kernel describes its own high-dimensional feature space mapping which affects regression performance. In this paper, eight kernels are used to discuss the influence of different space mapping to the blood component spectral quantitative analysis. Each kernel and corresponding parameters are assessed to build the optimal regression model. The proposed method is conducted on a real blood spectral data obtained from the uric acid determination. Results verify that the prediction errors of proposed models are more precise than the ones obtained by linear models. Support vector regression (SVR) provides better performance than partial least square (PLS) when combined with kernels. The local kernels are recommended according to the blood spectral data features. SVR with inverse multiquadric kernel has the best predictive performance that can be used for blood component spectral quantitative analysis. Kernel (algebra) ; Support vector machine ; Mathematics ; Component analysis ; Kernel method ; Principal component regression ; Spectral space ; Pattern recognition (psychology) ; Regression analysis ; Nonlinear system ; Component (thermodynamics) ; Gaussian ; Regression ; Artificial intelligence ; Computer science ; Statistics Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Metabolomics and Mass Spectrometry Studies
Authors: Rajamohan Jayabal | K. Vijayarekha | S. Rakesh Kumar
Source: Energies (article, 2018-12-04)
Field: Materials Science — Materials Chemistry

Design of ANFIS for Hydrophobicity Classification of Polymeric Insulators with Two-Stage Feature Reduction Technique and Its Field Deployment Hydrophobicity of polymeric insulator plays a vital role in determining the insulation quality in outdoor overhead electrical transmission and distribution lines. Loss of hydrophobicity increases the leakage current and leads to flashover. Monitoring hydrophobicity becomes a fundamental requirement to ensure continuity of power line operations. Hydrophobicity of polymeric insulator is classified according to STRI (Swedish Transmission Research Institute) guidelines. This paper proposes an intelligent ANFIS (Adaptive Neuro-Fuzzy Inference System) based classifier to determine the hydrophobicity quality using the digital image of the insulator. Ten statistical features are extracted from the digital images. Two stages of feature reduction are employed to reduce the number of features. Pre-design stage uses PCA (Principal Component Analysis) and reduces the number of features to six from ten and the post-design stage analyzes the accumulation effect to reduce the number of features to four. Various ANFIS classifiers are trained using these reduced features extracted from the image. The performance of these ANFIS classifiers is evaluated in both field and laboratory specimens. Results indicate classification accuracy of 96.4% and 93.3% during the training and testing phase when triangular membership function with linear output function is employed in ANFIS. A GUI (Graphical User Interface) has also been designed to facilitate the use of the proposed system by field operators. Adaptive neuro fuzzy inference system ; Artificial intelligence ; Insulator (electricity) ; Computer science ; Classifier (UML) ; Digital image ; Feature extraction ; Pattern recognition (psychology) ; Machine learning ; Fuzzy logic ; Engineering ; Reliability engineering ; Image processing ; Fuzzy control system ; Electrical engineering ; Image (mathematics) High voltage insulation and dielectric phenomena ; Water Systems and Optimization ; Power Transformer Diagnostics and Insulation
Authors: Jaqueline Pereira Januário | Thiago Belarmino de Souza | Stefânia Neiva Lavorato | Tatiane Cristina Silva Maiolini | Olívia da Silva Domingos | João L. Baldim | Laís Regina dos Santos Folquitto | Marisi G. Soares | Daniela Aparecida Chagas‐Paula | Danielle Ferreira Dias | Marcelo Henrique dos Santos
Source: Molecules (article, 2018-07-26)
Field: Chemistry — Organic Chemistry

Design and Synthesis of New Benzophenone Derivatives with In Vivo Anti-Inflammatory Activity through Dual Inhibition of Edema and Neutrophil Recruitment A series of novel benzophenone derivatives containing a thiazole heterocyclic nucleus were designed by molecular hybridization. Molecular docking studies have demonstrated the inhibitory potential of the designed compounds against cyclooxygenase (COX) isoenzymes. These compounds were synthesized, characterized, and evaluated for their anti-inflammatory properties by the croton oil-induced ear edema assay to examine their effect on both prostaglandin (PG) production and neutrophils recruitment. The thiazole derivatives displayed a potent effect in terms of reducing ear edema. The analysis suggested that the presence of 4-phenyl-2-hydrazinothiazole and the absence of C4'-OCH₃ on the benzophenone derivative structure are strongly related to the inhibition of PG production. In addition, the derivatives <b>2e</b>, <b>3a</b> and <b>3c</b> concomitantly inhibit PG production and neutrophil recruitment, which may be a mechanism of action better than of common NSAIDs due to their inability to inhibit the neutrophil recruitment. Thus, these compounds can be considered as potential lead compounds toward the development of new anti-inflammatory drugs with an innovating mechanism of action. Benzophenone ; In vivo ; Chemistry ; Edema ; Pharmacology ; Inflammatory response ; Anti-inflammatory ; Inflammation ; Medicine ; Immunology ; Biology ; Internal medicine ; Organic chemistry Synthesis and biological activity ; Inflammatory mediators and NSAID effects ; Synthesis and Reactions of Organic Compounds
Authors: Mario Fordellone | Andrea Bellincontro | Fabio Mencarelli
Source: IRIS Research product catalog (Sapienza University of Rome) (preprint, 2018-06-25)
Field: Chemistry — Analytical Chemistry

Partial least squares discriminant analysis: A dimensionality reduction method to classify hyperspectral data The recent development of more sophisticated spectroscopic methods allows acqui- sition of high dimensional datasets from which valuable information may be extracted using multivariate statistical analyses, such as dimensionality reduction and automatic classification (supervised and unsupervised). In this work, a supervised classification through a partial least squares discriminant analysis (PLS-DA) is performed on the hy- perspectral data. The obtained results are compared with those obtained by the most commonly used classification approaches. Dimensionality reduction ; Linear discriminant analysis ; Pattern recognition (psychology) ; Hyperspectral imaging ; Partial least squares regression ; Artificial intelligence ; Computer science ; Multivariate statistics ; Discriminant ; Mathematics ; Statistics ; Data mining ; Machine learning Spectroscopy and Chemometric Analyses ; Remote-Sensing Image Classification ; Advanced Chemical Sensor Technologies