jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Lei Li | Xianmin Ma | Wei Guo
Source: Security and Communication Networks (article, 2022-06-07)
Field: Materials Science — Materials Chemistry

Aging Law and Life Evaluation Model of Cable Insulation Based on WOA-SVR Cables are mainly composed of wires and are used to transmit signals or electrical energy. It can be seen everywhere in real life. However, due to the changeable environment in which it is located, it often leads to the problem of insulation aging. Therefore, it is particularly important to study the law of cable insulation aging and to evaluate its life. Humpback whale hunting behavior is simulated by WOA, a swarming intelligence algorithm. It can intelligently identify the relationship between various data. SVR is a linear regression model with a special computational loss. The aim of this paper is to investigate a WOA-SVR model to evaluate the aging law and life of cable insulation. This paper analyzes a variety of detection models. Finally, the partial discharge detection model and the depolarization detection model are selected for comparative testing with the WOA-SVR model studied in this paper. The test method is to count the aging of cable insulation under different temperature, humidity, and electric field strength environments. The test results show that the evaluation accuracy of the WOA-SVR model in this paper is 92%, 97%, and 98%, respectively, under different temperatures, humidity, and electric field strengths. The average accuracy of its evaluation is higher than the other two models. Therefore, the WOA-SVR model is more accurate and reliable for the cable insulation aging law and life evaluation. Computer science ; Electric field ; Reliability engineering ; Engineering High voltage insulation and dielectric phenomena ; Power Transformer Diagnostics and Insulation ; Electrical Fault Detection and Protection
Authors: Mississippi Missouri Bhunia | Paramita Chattopadhyay | Kalyan Kumar Chattopadhyay
Source: ChemRxiv (preprint, 2022-03-03)
Field: Materials Science — Materials Chemistry

Transformer Oil Nanofluids by Two Dimensional hexagonal Boron Nitride Nanofillers This paper explores the fascinating properties of two-dimensional (2D) nanofillers based transformer oil (TO) nanofluids. Nanofluids of 2D hexagonal boron nitride (h-BN) nanosheets in TO demonstrate stable dispersion with improved dielectric breakdown strength and superior thermo physical properties like thermal conductivity, viscosity and stability. An appreciable augmentation in AC breakdown voltage (BDV) is observed compared to the state-of-the-art boron nitride (BN) particles. This enhancement in BDV is elucidated by the role of the greater surface area of Maxwell-Garnet ‘oil-sheet’ interfacial region of the 2D morphology in charge trapping perspective. The faster rate of heating and cooling along with noteworthy enhancement in thermal conductivity is due to the interfacial heat transfer via 2D nanoadditives prompting good phonon transport which agrees with Maxwell's forecasts. Addition of 2D nanofiller at diluted concentration exhibits better stability and high thermal efficiency compared to its particle counterpart. Hence, 2D nanofillers are better choices for next generation transformer oil nanofluids, due to their high surface area, lower filler fraction and better stability. Nanofluid ; Materials science ; Thermal conductivity ; Boron nitride ; Composite material ; Transformer oil ; Dielectric ; Thermal conduction ; Dielectric strength ; Hexagonal boron nitride ; Nanoparticle ; Nanotechnology ; Transformer ; Voltage ; Optoelectronics ; Graphene High voltage insulation and dielectric phenomena ; Power Transformer Diagnostics and Insulation ; Silicon Carbide Semiconductor Technologies
Authors: Chengcheng Zhang | Sen Wang | Hong Zhao | Xuan Wang
Source: Energies (article, 2022-12-27)
Field: Materials Science — Materials Chemistry

Influence of Bismaleimide HVA-2 Grafting on the Direct Current Dielectric Properties of XLPE In this paper, N, N’-m-phenylene dimaleimide (HVA-2) grafted crosslinked polyethylene (XLPE) insulation materials with different HVA-2 contents were prepared. The grafting, crosslinking, and crystalline structure were characterized by Fourier-transform infrared spectroscopy (FTIR), Soxhlet extraction, and differential scanning calorimetry (DSC), respectively. The space charge distribution, direct current (DC) breakdown strength, and DC conduction current density were tested and the electronic structure was calculated from first-principles. HVA-2 grafting modification can significantly reduce the accumulation of space charges and the conduction current density of XLPE, but have a negative effect on DC breakdown strength. The polar groups of the grafted HVA-2 anchored on XLPE by the grafting reaction can introduce deep traps densely and evenly in XLPE, which would capture and scatter charge carriers, thus reducing the carrier concentration and mobility and further improving the space charge distribution and reducing conduction current density. However, the grafting of HVA-2 can increase the crosslinking extent of XLPE to make the crystallinity decrease and the crystallization inhomogeous, leading to a certain decrease in the breakdown strength of the grafted XLPE. Crystallinity ; Materials science ; Space charge ; Grafting ; Low-density polyethylene ; Fourier transform infrared spectroscopy ; Differential scanning calorimetry ; Polyethylene ; Dielectric strength ; Polymer chemistry ; Crystallization ; Dielectric ; Composite material ; Analytical Chemistry (journal) ; Chemical engineering ; Polymer ; Chemistry ; Organic chemistry ; Optoelectronics High voltage insulation and dielectric phenomena ; Dielectric materials and actuators ; Advanced Sensor and Energy Harvesting Materials
Authors: Dário Passos | Puneet Mishra
Source: NIR news (article, 2022-11-01)
Field: Chemistry — Analytical Chemistry

Perspectives on deep learning for near-infrared spectral data modelling Deep learning for near-infrared spectral data is a recent topic of interest for near-infrared practitioners. In recent years, applications of deep learning are flourishing from analyses of point spectrometer data to hyperspectral image analysis. However, there are also some cases where simple partial least-squares based models are sufficient. This paper provides a concise view of the state of the art of deep learning for near-infrared data modelling, particularly discussing when deep learning is useful. Discussion is also provided on what is already achieved and what ideas would be interesting to pursue regarding deep learning modelling of near-infrared data. Deep learning ; Hyperspectral imaging ; Infrared ; Computer science ; Artificial intelligence ; Flourishing ; Point (geometry) ; Data science ; Remote sensing ; Machine learning ; Geology ; Mathematics ; Physics ; Optics ; Psychology Spectroscopy and Chemometric Analyses ; Remote-Sensing Image Classification ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: Eric R. Carlson | J. R. Smith
Source:  (preprint, 2022-08-08)
Field: Materials Science — Materials Chemistry

A ±0 &lt;p&gt;This paper presents a step-up DC-DC converter that uses a stepwise gate-drive technique to reduce the power FET gate-drive energy by 82%, allowing positive efficiency down to an input voltage of ±0.5 mV—the lowest input voltage ever achieved for a DC-DC converter as far as we know. Below ±0.5 mV the converter automatically hibernates, reducing quiescent power consumption to just 255 pW. The converter has an efficiency of 63% at ±1 mV and 84% at ±6 mV. The input impedance is programmable from 1 Ω to 600 Ω to achieve maximum power extraction. A novel delay line circuit controls the stepwise gate-drive timing, programmable input impedance, and hibernation behavior. Bipolar input voltage is supported by using a flyback converter topology with two secondary windings. A generated power good signal enables the load when the output voltage has charged above 2.7 V and disables when the output voltage has discharged below 2.5 V. The DC-DC converter was used in a thermoelectric energy harvesting system that effectively harvests energy from small indoor temperature fluctuations of less than 1°C. Also, an analytical model with unprecedented accuracy of the stepwise gate-drive energy is presented. &lt;/p&gt; Flyback converter ; Electrical engineering ; Boost converter ; Forward converter ; Ćuk converter ; Buck–boost converter ; Voltage ; Engineering ; Control theory (sociology) ; Topology (electrical circuits) ; Electronic engineering ; Computer science Advanced Thermoelectric Materials and Devices ; Innovative Energy Harvesting Technologies ; Energy Harvesting in Wireless Networks
Authors: Seda Kül | S. S. Tezcan | Hüseyin Duysak | Seyit Alperen Çeltek
Source: Tehnicki vjesnik - Technical Gazette (article, 2022-09-03)
Field: Materials Science — Electronic, Optical and Magnetic Materials

FEM-based Modeling and Optimization of Dry-Type Transformers with Metaheuristic Algorithms Transformer optimization is a programming problem with an objective function for calculating the characteristics of a transformer in detail according to user requirements. Especially in recent years, efficient and optimum design of the transformer has become increasingly important. This study presents a comparative analysis of the application of metaheuristic optimization algorithms in transformer design for maximum efficiency of a three-phase dry-type transformer. In addition, transformer modeling was done using FEM analysis, and its magnetic characteristics were shown. The main contribution of this paper is to optimize the determined basic design parameters with optimization methods that have not been used in transformer optimization before and to maximize efficiency by reducing losses. During this process, using the loss constants of the materials in the basic loss equation for core loss, this process was tried to be carried out without any change in the method in applications to be made with different materials. This situation also strengthens the accuracy of the applied method. Crow Search (CSA), Moth-Flame Optimization (MFO), Vortex Optimization (VOA), Particle Swarm Optimization (PSO), and Social Learning-Particle Swarm Optimization (SL-PSO) algorithms were used in the study. The loss values were obtained by performing loaded and unloaded FEM analysis with the ANSYS/Maxwell program. As a result, the best result was obtained with SL-PSO as max 99,05%. Metaheuristic ; Finite element method ; Transformer ; Algorithm ; Computer science ; Engineering ; Structural engineering ; Electrical engineering ; Voltage Magnetic Properties and Applications ; Welding Techniques and Residual Stresses ; Non-Destructive Testing Techniques
Authors: Yeji Kim | Yoonho Jeong | Jihoo Kim | Eok Kyun Lee | Won June Kim | Insung S. Choi
Source: arXiv (Cornell University) (preprint, 2022-02-01)
Field: Materials Science — Materials Chemistry

MolNet: A Chemically Intuitive Graph Neural Network for Prediction of Molecular Properties The graph neural network (GNN) has been a powerful deep-learning tool in chemistry domain, due to its close connection with molecular graphs. Most GNN models collect and update atom and molecule features from the fed atom (and, in some cases, bond) features, which are basically based on the two-dimensional (2D) graph representation of 3D molecules. Correspondingly, the adjacency matrix, containing the information on covalent bonds, or equivalent data structures (e.g., lists) have been the main core in the feature-updating processes, such as graph convolution. However, the 2D-based models do not faithfully represent 3D molecules and their physicochemical properties, exemplified by the overlooked field effect that is a "through-space" effect, not a "through-bond" effect. The GNN model proposed herein, denoted as MolNet, is chemically intuitive, accommodating the 3D non-bond information in a molecule, with a noncovalent adjacency matrix $\bf{\bar A}$, and also bond-strength information from a weighted bond matrix $\bf{B}$. The noncovalent atoms, not directly bonded to a given atom in a molecule, are identified within 5 $\unicode{x212B}$ of cut-off range for the construction of $\bf{\bar A}$, and $\bf{B}$ has edge weights of 1, 1.5, 2, and 3 for single, aromatic, double, and triple bonds, respectively. Comparative studies show that MolNet outperforms various baseline GNN models and gives a state-of-the-art performance in the classification task of BACE dataset and regression task of ESOL dataset. This work suggests a future direction of deep-learning chemistry in the construction of deep-learning models that are chemically intuitive and comparable with the existing chemistry concepts and tools. Adjacency matrix ; Covalent bond ; Molecular graph ; Molecule ; Graph energy ; Artificial neural network ; Computer science ; Algorithm ; Graph ; Chemistry ; Artificial intelligence ; Theoretical computer science ; Line graph ; Graph power Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Various Chemistry Research Topics
Authors: Roshan Patel | Carlos H. Borca | Michael A. Webb
Source: ChemRxiv (preprint, 2022-02-16)
Field: Materials Science — Materials Chemistry

Featurization Strategies for Polymer Sequence or Composition Design by Machine Learning The emergence of data-intensive scientific discovery and machine learning has dramatically changed the way in which scientists and engineers approach materials design. Nevertheless, for designing macromolecules or polymers, one limitation is the lack of appropriate methods or standards for converting systems into chemically informed, machine-readable representations. This featurization process is critical to building predictive models that can guide polymer discovery. Although standard molecular featurization techniques have been deployed on homopolymers, such approaches capture neither the multiscale nature nor topological complexity of copolymers, and they have limited application to systems that cannot be characterized by a single repeat unit. Herein, we present, evaluate, and analyze a series of featurization strategies suitable for copolymer systems. These strategies are systematically examined in diverse prediction tasks sourced from four distinct datasets that enable understanding of how featurization can impact copolymer property prediction. Based on this comparative analysis, we suggest directly encoding polymer size in polymer representations when possible, adopting topological descriptors or convolutional neural networks when the precise polymer sequence is known, and using chemically informed unit representations when developing extrapolative models. These results provide guidance and future directions regarding polymer featurization for copolymer design by machine learning. Computer science ; Copolymer ; Sequence (biology) ; Polymer ; Process (computing) ; Artificial intelligence ; Machine learning ; Convolutional neural network ; Materials science ; Chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Analytical Chemistry and Chromatography
Authors: Claudio Ronchetti | Marco Puccini | Sergio Ferlito | Simone Giusepponi | Filippo Palombi | Francesco Buonocore | Massimo Celino
Source:  (article, 2022-10-03)
Field: Materials Science — Materials Chemistry

Machine learning techniques for data analysis in materials science This work applies Graph Neural Networks (GNNs), a class of deep learning methods, to predict physical properties and obtain optimal cathode materials for batteries. Two GNNs are selected: Crystal Graph Convolutional Neural Networks (CGCNN) and the more recent Geometric-Information-Enhanced Crystal Graph Network (GeoCGNN). Both networks are trained on a selected open-source ab initio Density Functional Theory (DFT) dataset for solid-state materials to predict the formation energy and then calculate the redox potential. Numerical results show the inference of the best trained model ran on combinatorial space of interest to discovery the optimal one via multi-objectives method. This approach allows to detect the optimum faster than physics-based computational approaches. Computer science ; Inference ; Graph ; Convolutional neural network ; Graph theory ; Theoretical computer science ; Artificial intelligence ; Deep learning ; Artificial neural network ; Machine learning ; Mathematics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; X-ray Diffraction in Crystallography
Authors: Shuhao Zhang | Małgorzata Z. Makoś | Ryan B. Jadrich | Elfi Kraka | Kipton Barros | Benjamin Nebgen | Sergei Tretiak | Olexandr Isayev | Nicholas Lubbers | Richard A. Messerly | Justin S. Smith
Source: ChemRxiv (preprint, 2022-11-30)
Field: Materials Science — Materials Chemistry

Exploring the frontiers of chemistry with a generalreactive machine learning potential Reactive chemistry atomistic simulation has a broad range of applications from drug design to energy to materials discovery. Machine learning interatomic potentials (MLIP) have become an efficient alternative to computationally expensive quantum chemistry simulations. In practice, reactive MLIPs require refitting to extensive datasets for each new application, and prior knowledge of reaction networks is required to generate fitting data. In this work, we develop a general reactive MLIP through unbiased active learning with a nanoreactor molecular dynamics inspired sampler. The resulting potential (ANI-nr) is then applied to study five distinct condensed phase reactive chemistry problems: carbon solid-phase nucleation, graphene ring formation from acetylene, biofuel additives, combustion of methane and the spontaneous formation of glycine from early earth small molecules. In all studies, ANI-nr closely matches experiment and/or previous studies using traditional model chemistry methods, without needing to be refit for each application, which enables high-throughput in silico reactive chemistry experimentation. Reactive intermediate ; Nanoreactor ; Chemistry ; Quantum chemistry ; Molecular dynamics ; Molecular machine ; Nanotechnology ; Computer science ; Biochemical engineering ; Computational chemistry ; Molecule ; Organic chemistry ; Materials science Machine Learning in Materials Science ; Protein Structure and Dynamics ; Software Engineering Research
Authors: Hassaan Malik | Muhammad Umar Chaudhry | Michał Jasiński
Source: Energies (article, 2022-12-09)
Field: Materials Science — Materials Chemistry

Deep Learning for Molecular Thermodynamics The methods used in chemical engineering are strongly reliant on having a solid grasp of the thermodynamic features of complex systems. It is difficult to define the behavior of ions and molecules in complex systems and to make reliable predictions about the thermodynamic features of complex systems across a wide range. Deep learning (DL), which can provide explanations for intricate interactions that are beyond the scope of traditional mathematical functions, would appear to be an effective solution to this problem. In this brief Perspective, we provide an overview of DL and review several of its possible applications within the realm of chemical engineering. DL approaches to anticipate the molecular thermodynamic characteristics of a broad range of systems based on the data that are already available are also described, with numerous cases serving as illustrations. Scope (computer science) ; GRASP ; Computer science ; Range (aeronautics) ; Complex system ; Perspective (graphical) ; Artificial intelligence ; Engineering ; Aerospace engineering Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Various Chemistry Research Topics
Authors: Guojing Cong | Talia Ben-Naim | Victor Fung | Anshul Gupta | Rodrigo Neumann Barros Ferreira | M. Steiner
Source: 2022 IEEE International Conference on Data Mining Workshops (ICDMW) (article, 2022-11-01)
Field: Materials Science — Materials Chemistry

Extensive Attention Mechanisms in Graph Neural Networks for Materials Discovery We present our research where attention mechanism is extensively applied to various aspects of graph neural net- works for predicting materials properties. As a result, surrogate models can not only replace costly simulations for materials screening but also formulate hypotheses and insights to guide further design exploration. We predict formation energy of the Materials Project and gas adsorption of crystalline adsorbents, and demonstrate the superior performance of our graph neural networks. Moreover, attention reveals important substructures that the machine learning models deem important for a material to achieve desired target properties. Our model is based solely on standard structural input files containing atomistic descriptions of the adsorbent material candidates. We construct novel methodological extensions to match the prediction accuracy of state-of-the-art models some of which were built with hundreds of features at much higher computational cost. We show that sophisticated neural networks can obviate the need for elaborate feature engineering. Our approach can be more broadly applied to optimize gas capture processes at industrial scale. Computer science ; Artificial neural network ; Artificial intelligence ; Graph ; Machine learning ; Construct (python library) ; Feature (linguistics) ; Theoretical computer science Machine Learning in Materials Science ; Advanced Graph Neural Networks ; Computational Drug Discovery Methods
Authors: Oleh Yasniy | Vladyslav Demchyk | Nadiia Lutsyk
Source: Scientific journal of the Ternopil national technical university (article, 2022-01-01)
Field: Materials Science — Materials Chemistry

Modelling of functional properties of shape-memory alloys by machine learning methods Shape-memory alloys are used in various areas of science and industry due to their unique shape memory effect and superelasticity, caused by martensite and reverse transformations. In this study, it is proposed to model the functional properties of shape memory alloys, namely, the dissipated energy range, strain range and stress range using the methods of machine learning. The modeling is carried ou in the specialized data mining software environment called Orange. There were built five models for each dataset by means of method of neural networks, random forest, gradient boosting, AdaBoost and kNN. The respective regression dependencies are obtained and K fold cross-validation with K=5 is performed. The errors and coefficient for R2 determination are calculated as the results of modeling by means of the above mentioned machine learning methods for the range of dissipated energy, stresses and strains on the number of loading cycles. For each physical quantity, the best results in terms of method error are obtained for k-nearest neighbors method. Shape-memory alloy ; Artificial neural network ; Pseudoelasticity ; Computer science ; Range (aeronautics) ; Boosting (machine learning) ; AdaBoost ; Random forest ; Artificial intelligence ; Machine learning ; Gradient boosting ; Materials science ; Martensite ; Support vector machine ; Metallurgy Shape Memory Alloy Transformations ; Machine Learning in Materials Science ; Thermography and Photoacoustic Techniques