jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Dr.B.karthik A.Aravind
Source: Solid State Technology (article, 2020-10-15)
Field: Materials Science — Materials Chemistry

Hybrid Technique based Voltage Stabilizers for XLPE Cable Insulation Cross-Linked Polyethylene (XLPE) power cable is commonly used as the medium for transmitting power supply in the power system network. However, frequent exposure of XLPE cable to the high voltage causes its insulation to degrade over time leading to electricity outages. Electrical treeing is a commonly observed phenomenon associated with dielectric breakdown in solid dielectrics. Electrical treeing tends to exhibit a fractal shape with more branches at a relatively high discharge speed relative to the rate of damage. It establishes the fact that Partial Discharge (PD) activity in insulation highly depends upon certain factors like applied voltage, size of the void and dielectric constant of insulation material. The aim of the development model is to understand the effect of charges accumulation in the XLPE insulation void and voltage stabilization. Hence to accomplish the objective, composite of Elephant Herd Optimization (EHO) algorithm and Recurrent Neural Network (RNN) algorithm is utilized, which controls the stability constraints of the XLPE cable insulation. However, the efficiency of the voltage stabilizers available today is low, as a load is needed to significantly increase the breakdown strength. Furthermore, the addition of a voltage stabilizer should be kept as low as possible in order not to influence other properties of the material. The new voltage controls presented in this paper have increased the electrical tree inception field. The simulation results from MATLAB/Simulink are validated and tested. The evaluation result of proposed methodology is compared with Whale Optimization Algorithm (WOA) with RNN and EHO algorithm. Electrical treeing ; Power cable ; Partial discharge ; Materials science ; Voltage ; Dielectric ; Composite material ; Void (composites) ; High voltage ; High-voltage cable ; Polyethylene ; Cross-linked polyethylene ; Electrical engineering ; Engineering ; Optoelectronics High voltage insulation and dielectric phenomena ; Power Transformer Diagnostics and Insulation ; Transport Systems and Technology
Authors: Qi Wang | Jun Ding | E. Ma
Source: arXiv (Cornell University) (preprint, 2020-06-24)
Field: Materials Science — Ceramics and Composites

Predicting the propensity for thermally activated $β$ events in metallic glasses via interpretable machine learning The elementary excitations in metallic glasses (MGs), i.e., $β$ processes that involve hopping between nearby sub-basins, underlie many unusual properties of the amorphous alloys. A high-efficacy prediction of the propensity for those activated processes from solely the atomic positions, however, has remained a daunting challenge. Recently, employing well-designed site environment descriptors and machine learning (ML), notable progress has been made in predicting the propensity for stress-activated $β$ processes (i.e., shear transformations) from the static structure. However, the complex tensorial stress field and direction-dependent activation would induce non-trivial noises in the data, limiting the accuracy of the structure-property mapping learned. Here, we focus on the thermally activated elementary excitations and generate high-quality data in several Cu-Zr MGs, allowing quantitative mapping of the potential energy landscape. After fingerprinting the atomic environment with short- and medium-range interstice distribution, ML can identify the atoms with strong resistance or high compliance to thermal activation, at an unprecedented accuracy over ML models for stress-driven activation events. Interestingly, a quantitative "between-task" transferring test reveals that our learnt model can also generalize to predict the propensity of shear transformation. Our dataset is potentially useful for benchmarking future ML models on structure-property relationships in MGs. Metal ; Materials science ; Artificial intelligence ; Econometrics ; Machine learning ; Economics ; Computer science ; Metallurgy Glass properties and applications
Authors: Ranjan Kumar Ghadai | Kanak Kalita
Source: International Journal of Materials Research (formerly Zeitschrift fuer Metallkunde) (article, 2020-07-01)
Field: Materials Science — Materials Chemistry

Accurate estimation of DLC thin film hardness using genetic programming Abstract In the current research, diamond-like carbon thin films are deposited on silicon substrates by plasma-enhanced chemical vapor deposition. The effect of argon-C 2 H 2 flow rate, hydrogen flow rate and deposition temperature on the thin film hardness is investigated. Morphology of the DLC films is investigated by scanning electron microscopy and atomic force microscopy, while the nano-hardness is investigated using nanoindentation. Raman spectroscopy is used for the characterization of the structural properties of the film. A metamodel of the DLC deposition process with argon- C 2 H 2 flow rate, H 2 flow rate and deposition temperature as the regressor variables and coating hardness as the response is built by using a novel symbolic regression approach. A state-of-the-art machine learning approach - genetic programming (GP) - is used for the symbolic regression. By carefully evaluating the performance of the current GP metamodel against a classical RSM (response surface methodology) metamodel, it is seen that the GP significantly outperforms RSM. Nanoindentation ; Materials science ; Thin film ; Scanning electron microscope ; Diamond-like carbon ; Chemical vapor deposition ; Raman spectroscopy ; Volumetric flow rate ; Genetic programming ; Composite material ; Deposition (geology) ; Argon ; Symbolic regression ; Analytical Chemistry (journal) ; Nanotechnology ; Chemical engineering ; Machine learning ; Optics ; Computer science ; Thermodynamics ; Chemistry Diamond and Carbon-based Materials Research ; Semiconductor materials and devices ; Metal and Thin Film Mechanics
Authors: Romain Gaillac | Siwar Chibani | François‐Xavier Coudert
Source: ChemRxiv (preprint, 2020-02-04)
Field: Materials Science — Materials Chemistry

Speeding Up Discovery of Auxetic Zeolite Frameworks by Machine Learning &lt;div&gt; &lt;div&gt; &lt;div&gt; &lt;p&gt;The characterization of the mechanical properties of crystalline materials is nowadays considered a routine computational task in DFT calculations. However, its high computational cost still prevents it from being used in high-throughput screening methodologies, where a cheaper estimate of the elastic properties of a material is required. In this work, we have investigated the accuracy of force field calculations for the prediction of mechanical properties, and in particular for the characterization of the directional Poisson’s ratio. We analyze the behavior of about 600,000 hypothetical zeolitic structures at the classical level (a scale three orders of magnitude larger than previous studies), to highlight generic trends between mechanical properties and energetic stability. By comparing these results with DFT calculations on 991 zeolitic frameworks, we highlight the limitations of force field predictions, in particular for predicting auxeticity. We then used this reference DFT data as a training set for a machine learning algorithm, showing that it offers a way to build fast and reliable predictive models for anisotropic properties. The accuracies obtained are, in particular, much better than the current “cheap” approach for screening, which is the use of force fields. These results are a significant improvement over the previous work, due to the more difficult nature of the properties studied, namely the anisotropic elastic response. It is also the first time such a large training data set is used for zeolitic materials. &lt;/p&gt;&lt;/div&gt;&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;div&gt;&lt;div&gt; &lt;/div&gt; &lt;/div&gt; &lt;/div&gt; Characterization (materials science) ; Force field (fiction) ; Anisotropy ; Computer science ; Stability (learning theory) ; Field (mathematics) ; Work (physics) ; Set (abstract data type) ; Auxetics ; Task (project management) ; Throughput ; Materials science ; Algorithm ; Machine learning ; Artificial intelligence ; Nanotechnology ; Mathematics ; Physics ; Thermodynamics ; Engineering ; Composite material Machine Learning in Materials Science ; Zeolite Catalysis and Synthesis ; Catalysis and Oxidation Reactions
Authors: Nirmal Dorothy | Abid Rahman | A. Shake Ashik | Rekha Lakshmanan | U Scholar | Stephen Sagayaraj | & Ramya | Dhanaraj | Dr Stephen Sagayaraj | Devi Kalavathi | R Mahendran | G Jayashree | K Alagusundaram | A Stephensagayara | R Pradeepa | P Suganthi | S Vinothini | T Vishvapriya | K Patel | M Khan | A Kar | Y Kumar | L Bal | D Sharma | S Jha | S Rupali | S Jadhav | Patil
Source: International Journal for Research in Engineering Application & Management (article, 2020-05-30)
Field: Chemistry — Analytical Chemistry

AQUiD: Automated Quality Assessment Using Digital Image Processing Products in the market are expected to satisfy the consumer’s quality requirements. Agriculture being one of the main occupation of the people of India, the raw products must be sorted to determine whether they fit the quality description so that high quality products are obtained as the end result. The proposed method is designed to ensure the availability of good quality coconut oil in the market by assessing the quality of each individual sample going into the production line. 70% of moisture content present naturally in copra(dried coconut kernel) is dried to almost 7% for coconut oil production. To prevent the growth of bacteria and fungus on the surface of the copra, sulphur is added as a preservative. Allergenic reactions and lung performance restrictions can be caused due to the presence of sulphur in copra. The presence of moisture may also adversely affect oil quality. The texture features such as wrinkles, moulds, fungi growth on the surface also deplete the oil quality. The features of different kinds of copra are analysed and is used train the machine. The machine learning methodology is adopted for the classification of copra as usable and unusable. Copra ; Quality (philosophy) ; Coconut oil ; Agricultural engineering ; Pulp and paper industry ; Food science ; Environmental science ; Biotechnology ; Mathematics ; Engineering ; Chemistry ; Biology Spectroscopy and Chemometric Analyses ; Coconut Research and Applications ; Advanced Chemical Sensor Technologies
Authors: Chenru Duan | Fang Liu | Aditya Nandy | Heather J. Kulik
Source: ChemRxiv (preprint, 2020-07-02)
Field: Materials Science — Materials Chemistry

Semi-Supervised Machine Learning Enables the Robust Detection of Multireference Character at Low Cost Multireference (MR) diagnostics are common tools for identifying strongly correlated electronic structure that makes single reference (SR) methods (e.g., density functional theory or DFT) insufficient for accurate property prediction. However, MR diagnostics typically require computationally demanding correlated wavefunction theory (WFT) calculations, and diagnostics often disagree or fail to predict MR effects on properties. To overcome these challenges, we introduce a semi-supervised machine learning (ML) approach with virtual adversarial training (VAT) of an MR classifier using 15 WFT and DFT MR diagnostics as inputs. In semi-supervised learning, only the most extreme SR or MR points are labeled, and the remaining point labels are learned. The resulting VAT model outperforms the alternatives, as quantified by the distinct property distributions of SR- and MR-classified molecules. To reduce the cost of generating inputs to the VAT model, we leverage the VAT model’s robustness to noisy inputs by replacing WFT MR diagnostics with regression predictions in a MR decision engine workflow that preserves excellent performance. We demonstrate the transferability of our approach to larger molecules and those with distinct chemical composition from the training set. This MR decision engine demonstrates promise as a low-cost, high-accuracy approach to the automatic detection of strong correlation for predictive high-throughput screening. Transferability ; Computer science ; Artificial intelligence ; Machine learning ; Leverage (statistics) ; Robustness (evolution) ; Workflow ; Training set ; Chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Metabolomics and Mass Spectrometry Studies
Authors: Alistair Wick
Source: cIRcle (University of British Columbia) (article, 2020-01-01)
Field: Materials Science — Polymers and Plastics

Skinprobe 2.0 : development of a system for low-cost measurement of human soft tissues We present "SkinProbe 2.0," a prototype system for low cost, high volume measurement of the physical properties of human soft tissues through direct contact and perturbation of the skin. Our solution encompasses a handheld device and associated cloud-based AI processing pipeline, and derives physically-representative values of stiffness and thickness directly from video. These input videos include images of the surface under contact, and of a "flexure," our novel apparatus for optical force measurement. Videos are captured using a smartphone embedded in the device. Our system processes these videos, generating dense optical flow fields for selected frames, and passing these frames and flow fields through two bespoke Neural Networks: one providing estimated force readings, and one providing estimates of soft-body material properties in the contact vicinity. We automate the collection of training data for our networks with robotics and a 3D-printed apparatus, along with custom-made silicone tissue phantoms, and a cloud pipeline for data collection, storage, and retrieval. This allows us to scale to thousands of samples in each training dataset, with minimal human involvement in collection, and a highly repeatable collection process. We demonstrate the functionality of our measurement device, cloud pipeline, and force estimation system, and show promising material estimation results on our tissue phantoms. We further consider directions for future research in improving our system, both for handheld data collection, and for eventual usage on human subjects. Computer science ; Business ; Risk analysis (engineering) Textile materials and evaluations
Authors: Viktor Christian Birschitzky
Source:  (article, 2020-01-01)
Field: Materials Science — Materials Chemistry

Polaron configurational energies using machine learning Polaronen werden in einer Vielzahl von Materialien beobachtet und stellen aufgrund ihrer fundamentalen Bedeutung und vielversprechender potenzieller Anwendungen ein interessantes und fruchtbares Forschungsfeld in der Materialwissenschaft dar. Die Modellierung der Polaronenbildung und -dynamik durch \texit{ab initio} Methoden, wie Dichtefunktionaltheorie (DFT), hat sich insbesondere in den letzten Jahren als nutzliches Werkzeug erwiesen. Die Suche nach dem polaronischen Grundzustand ist jedoch aufgrund des grosen Konfigurationsraums, der fur Polaronen zur Verfugung steht, mit Standard-Berechnungsmethoden eine anspruchsvolle Aufgabe. In dieser Arbeit wenden wir datengetriebene Methoden des maschinellen Lernens auf das Polaronproblem an, um die Berechnung der Energie fur eine grose Anzahl von polaronischen Konfigurationen zu beschleunigen. Durch das Testen verschiedener Schemata konnten wir ein Modell realisieren, das in der Lage ist, Polaronenenergien auf Rutil $\mathrm{TiO_2}$(110) mit einer Genauigkeit auf DFT-Niveau vorherzusagen. Auf der Grundlage dieser Ergebnisse konnten wir erfolgreich das Phasendiagramm dieses Materials reproduzieren, von dem bekannt ist, dass es stark von Polaronen abhangt. Unsere Ergebnisse stellen einen ersten Schritt hin zu einem allgemeineren Machine-Learning-Schema dar, das in der Lage sein konnte, Polaron-Eigenschaften in jedem Material mit geringem Rechenaufwand zu untersuchen. Physics ; Humanities ; Philosophy Machine Learning in Materials Science ; 2D Materials and Applications ; X-ray Diffraction in Crystallography
Authors: R.R. Madaki | Abdulwahab Lawan
Source: Journal of Computer Science and Its Application (article, 2020-02-09)
Field: Materials Science — Polymers and Plastics

Saki pattern recognition in a woven fabrics using neuro-fuzzy system The importance of pattern recognition cannot be over emphasized as it cuts across many fields. Majority of the work on fabric pattern recognition focuses on determining the nature of the wefts and wrap in a given cloth. Others are more concerned with defect detection of hand-woven fabrics. However, there are limited studies on Saki Pattern recognition. Saki is atraditional Fulani hand-woven material worn as everyday cloth by the Fulani Clan of Westand Central Africa. This research is aimed at recognizing a Saki pattern in woven fabricsusing a Neuro-fuzzy system. A total of 1500 images from ten (10) different samples of Saki are collected and pre-processed to extract relevant features. Principal component analysis(PCA) is used for dimension reduction and the images are trained using Back-propagationalgorithm (BP) Neural Network using Matlab. Fuzzy inference rules are then used forclassification. The result obtained from the experiment showed that all the ten (10) Saki samples were predicted accurately with an average of 80% similarity. Thus, providing a lotof information on Saki, this may help in preserving the Fulani cultural heritage and advancethe Saki textile industry globally.Keywords: Fuzzy inference, Image processing, Pattern recognition, Fulani wearVol.26 No.1 June 2019 Pattern recognition (psychology) ; Artificial intelligence ; Principal component analysis ; Clan ; Fuzzy logic ; Computer science ; Similarity (geometry) ; Artificial neural network ; Fuzzy inference ; Computer vision ; Image (mathematics) ; Adaptive neuro fuzzy inference system ; Fuzzy control system ; Anthropology ; Sociology Textile materials and evaluations ; Industrial Vision Systems and Defect Detection
Authors: И. В. Рудакова | А. А. Пешехонов | А. В. Черникова | Svetlana Kuzmina
Source: ИННОВАЦИИ (article, 2020-12-26)
Field: Chemistry — Analytical Chemistry

Модель нейронной сети оценки показателей качества бумажной промышленности Исследования зависимости качества бумажного полотна от условий производства и свойств сырья дают значительный статистический разброс, вследствие чего невозможно точно прогнозировать результат. Этот факт стал предпосылкой применения технологии нейросетевого моделирования при разработке интеллектуальной системы мониторинга качества бумажного полотна. Рассмотрена методика определения оценок неоднородности структуры бумажного полотна на завершающей стадии его производства. Предложено расширить классификацию образцов готовой продукции посредством использования нейронечеткой интерполяции лингвистических значений таких показателей, что позволит повысить эффективность производственного процесса Studies of the dependence of the quality of the paper web on the production conditions and properties of raw materials give a significant statistical spread, as a result of which it is impossible to accurately predict the result. This fact became a prerequisite for the use of neural network modeling technology in the development of an intelligent system for monitoring the quality of the paper web. The methodology for determining the estimates of the heterogeneity of the structure of the paper web at the final stage of its production is considered. It is proposed to expand the classification of finished product samples through the use of neurofuzzy interpolation of the linguistic values of such indicators, which will improve the efficiency of the production process Computer science Spectroscopy and Chemometric Analyses ; Stonefly species taxonomy and ecology ; Advanced Data Processing Techniques
Authors: Sorood Zahedi Abghari | Ali Asghar Pasban | َAliakbar Miranbeigi
Source:  (article, 2020-01-14)
Field: Chemistry — Analytical Chemistry

Development of applicable and suitable Methods for Control and Monitoring of Wax Formation by determination of WAT for Crude Oil Transport in Pipelines Determination of wax appearance temperature is a crucial specification in transportation and storage of crude oil or heavy refinery products same as fuel oils. In this work, to determine wax appearance temperature different applicable methods were tested and developed. At first, the microscopic experiments were applied for different samples. Then the variation of viscosity versus temperature were monitored. To determine a feasible method to be applied in the fields, the variation of viscosity versus temperature was monitored. It was declared that at the wax appearance temperature a deep change in viscosity was happened. Eventually, the wax appearance temperature of different crude oil including Iranian or heavy crude oil can be determined by viscosimetry method. In the related experiments, the negative effect of water sediments and wax crystals were eliminated. It was also declared that the wax appearance temperature could be varied by variation of density, wax content, pour point and asphalthenes of the samples. To correlate between input and output variables three different modeling methods were tested and implemented. The Artificial neural networks, Neuro-fuzzy and regression models were test to derive the most accurate correlation. The statistical studies approved that the regression models have suitable accuracy. The results of regression model declared that the interaction effect of density and pour point and the effect of asphalthenes as one of the main input variable have the greatest impact on the wax appearance temperature. Wax ; Refinery ; Viscosity ; Pour point ; Linear regression ; Chemistry ; Crude oil ; Petroleum engineering ; Regression analysis ; Environmental science ; Chromatography ; Materials science ; Mathematics ; Statistics ; Engineering ; Composite material ; Organic chemistry Petroleum Processing and Analysis ; Structural Integrity and Reliability Analysis
Authors: Marco Arrigoni | Georg K. H. Madsen
Source: arXiv (Cornell University) (preprint, 2020-11-13)
Field: Materials Science — Materials Chemistry

Evolutionary computing and machine learning for the discovering of low-energy defect configurations Density functional theory (DFT) has become a standard tool for the study of point defects in materials. However, finding the most stable defective structures remains a very challenging task as it involves the solution of a multimodal optimization problem with a high-dimensional objective function. Hitherto, the approaches most commonly used to tackle this problem have been mostly empirical, heuristic and/or based on domain knowledge. In this contribution, we describe an approach for exploring the potential energy surface based on the covariance matrix adaption evolution strategy (CMA-ES) and supervised and unsupervised machine learning models. We show how the original CMA-ES can be modified to suit the specific problem of DFT studies of point defects in the dilute limit. The resulting algorithm depends only on a limited set of physically interpretable hyperparameters. The approach offers a robust and systematic way for finding low-energy configurations of point defects in solids. We demonstrate the applicability and moderate computational cost on the intrinsic defects in silicon. We also apply the methodology to the neutral oxygen vacancy oxygen vacancy in TiO$_2$ anatase and reproduce the known defect structures. Furthermore, a new defect structure, stable at the level of hybrid density functional theory and characterized by a delocalized electronic structure, is found for this system. Computer science ; Density functional theory ; Artificial intelligence ; Heuristic ; Machine learning ; Algorithm ; Mathematical optimization ; Mathematics ; Computational chemistry ; Chemistry Machine Learning in Materials Science