jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Amine Berrag | Lakhdar Madani | S. Belkhiat
Source: Engineering Technology & Applied Science Research (article, 2023-04-02)
Field: Materials Science — Materials Chemistry

An Experimental Study of the Dielectric Parameters of PVC Nano-Composites under Corona Conditions Poly-Vinyl Chloride (PVC) is a commonly used material used in cable insulation sheaths, but its dielectric properties can be negatively impacted by electric aging. This study investigates the use of nano-fillers, specifically alumina (Al2O3), titanium dioxide (TiO2), calcium carbonate (CaCO3), and barium titanate (BaTiO), in order to improve the dielectric properties of PVC. Films of PVC were doped with nano-fillers and were then exposed to an alternating voltage of 15kV for various time periods (1, 2, and 3 hours). The dielectric properties of PVC were measured using an impedance analyzer, and the results indicated that the use of these nano-fillers had a positive effect on the dielectric characteristics of PVC. Materials science ; Composite material ; Dielectric ; Barium titanate ; Titanium dioxide ; Nano- ; Dielectric spectroscopy ; Vinyl chloride ; Polyvinyl chloride ; Ceramic ; Electrode ; Polymer ; Electrochemistry ; Chemistry High voltage insulation and dielectric phenomena ; Dielectric materials and actuators ; Polymer Nanocomposite Synthesis and Irradiation
Authors: Latai Ga | Yuqi Zhang | Daochun Xu | Wenbin Li
Source: Forests (article, 2023-02-22)
Field: Materials Science — Materials Chemistry

Design and Experimental Investigation of a Thermoelectric Conversion Device with Power Management for Forest Fire Monitoring Forest fires have long been a significant global problem. How to reliably accomplish the early warning and real-time monitoring of forest fires has become a pressing issue in order to limit the damage caused by forest fires. A novel technological approach for forest fire monitoring has been made possible by the quick development of wireless sensor network (WSN) technology. Currently, batteries are the primary source of power for WSNs used in forests, but frequent battery replacement will compromise the network for monitoring. As a result, the power supply is the key limit to its application in forest areas. This paper puts forward the thermoelectric conversion based on the Seebeck effect. Its notable feature is to convert heat energy into electric energy through the temperature difference between shallow soil and air. In the process of testing the device, the maximum voltage was 803.36 mV. At the same time, a power management system (PMS) for a thermoelectric conversion device was designed. The main feature of this system is that there is no need for an external control module. In the laboratory test, the minimum input power of this system was 200 mV. When the load resistance was 8 KΩ, the output power was 0.55 mW, and the maximum efficiency could reach 65.38% when the input was 500 mV, which fulfills the requirements of low cost and high reliability, providing a feasible solution for solving the energy limitation problem of WSNs in a forest area. Battery (electricity) ; Environmental science ; Computer science ; Electric power ; Thermoelectric effect ; Automotive engineering ; Reliability (semiconductor) ; Power management ; Power (physics) ; Voltage ; Limit (mathematics) ; Reliability engineering ; Electrical engineering ; Engineering Advanced Thermoelectric Materials and Devices ; Fire Detection and Safety Systems ; Thermal Radiation and Cooling Technologies
Authors: B. Q. Li | Yunlong Han | Jiaxiang Li | Tongfei Shi
Source: Physics of Fluids (article, 2023-12-01)
Field: Materials Science — Materials Chemistry

Dynamics of Janus vesicles passing through a narrow pore Janus vesicles have garnered significant attention due to their versatility and potential applications. Investigating the perforation dynamics of Janus vesicles represents a fundamental physical inquiry in this distinctive structure and helps to understand their promising applications in drug delivery, biomedicine, and others. However, the perforation kinetic behavior of Janus vesicles has yet to be elaborated. Here, we employ the finite element method to explore the dynamics of Janus vesicles passing through a narrow pore in a microchannel. Results demonstrate that Janus vesicles, characterized by their asymmetric membrane structure, have a non-negligible effect on vesicle deformation while crossing the pore. Specifically, the Janus property strengthens membrane deformation on the low modulus side while weakening it on the high modulus side. Additionally, Janus vesicles exhibit an increased perforation speed. Results further determine that the non-axially positioned Janus vesicles experience a bigger rotation angle than uniform modulus vesicles by constructing the rotation angle θ. This research provides valuable insight into the movement and deformation of Janus vesicles during their passage through a pore, which can guide optimization and design of Janus vesicles for drug carriers, catalysis, and more. Vesicle ; Janus ; Deformation (meteorology) ; Nanotechnology ; Perforation ; Materials science ; Membrane ; Chemistry ; Composite material Pickering emulsions and particle stabilization ; Micro and Nano Robotics ; Lipid Membrane Structure and Behavior
Authors: Jessica Narku-Tetteh | Ebenezer Mensah | Pailin Muchan | Teeradet Supap | Supranee Lisawadi | Raphael Idem
Source: Heliyon (article, 2023-11-01)
Field: Materials Science — Materials Chemistry

Development of experimental error-Driven model for prediction of corrosion rates of amines based on their chemical structures This work investigated the relationships between amine corrosion rates and their chemical structural properties for application in the development of a Gaussian Process Regression (GPR) model for chemical structure-based prediction of corrosion rate of any amine. The GPR model accounted for experimental errors, which widened its scope to accurately predict the true corrosion rates, being restricted only to error associated with the trained model. The Average Absolute Deviation (AAD) between experimental corrosion rates and model predicted rates was 4.26 % for the test data, and 5.32 % for two test data unknown to the model. This showed that the model is generalizable and its predictions are accurate. This work also developed a user-friendly Graphical-User Interface, which allows a user to define any amine's structure to provide needed information to calculate its surface tension and steric effects for use as input variables to the model in predicting the corrosion rate of the amine. Corrosion ; Kriging ; Amine gas treating ; Experimental data ; Gaussian ; Work (physics) ; Materials science ; Computer science ; Biological system ; Chemistry ; Mathematics ; Engineering ; Metallurgy ; Statistics ; Computational chemistry ; Machine learning ; Mechanical engineering Machine Learning in Materials Science ; Corrosion Behavior and Inhibition ; Fuel Cells and Related Materials
Authors: Piotr Gnaciński | Marcin Pepliński | Аdаm Muc | Damian Hallmann
Source: Energies (article, 2023-12-22)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Line-Start Permanent Magnet Synchronous Motor Supplied with Voltage Containing Negative-Sequence Subharmonics In some power systems, the voltage waveform contains frequency components less than fundamental, called subharmonics or subsynchronous interharmonics. Voltage subharmonics can be both positive- and negative-sequence, independent of their frequency (order). Subharmonics exert harmful effects on sundry electrical equipment, especially on rotating machinery; they cause various noxious phenomena, such as a local saturation of the magnetic circuit, increases in power losses and windings temperature, and torque pulsations leading to vibration of unacceptable severity. Notably, previous works reported excessive vibration of rotating machinery only under no-load, while under full load, rather moderate vibration occurred. This study deals with vibration analysis of a line-start permanent magnet synchronous motor (LSPMSM) supplied with the voltage containing negative-sentence subharmonics. Experimental investigations were conducted for a 3 kW, four-pole production LSPMSM for subharmonics of various values and frequencies. Voltage subharmonics of values significantly less than reported in real power systems were found to cause unacceptable vibration, especially under full load. Vibration ; Subharmonic ; Waveform ; Voltage ; Control theory (sociology) ; Power (physics) ; Acoustics ; Physics ; Engineering ; Electrical engineering ; Computer science ; Nonlinear system Magnetic Properties and Applications ; Vibration and Dynamic Analysis ; Power Quality and Harmonics
Authors: Jiawei Li | Zhengxin Chen | Jiang Wu | Jia Lin | Ping He | Rui Zhu | Cheng Peng | Hai Zhang | Wenhao Li | Fang Xu | Hongtao Shen
Source: Materials Today Communications (article, 2023-05-24)
Field: Materials Science — Materials Chemistry

Constructing a link between multivariate titanium-based semiconductor band gaps and chemical formulae based on machine learning Photocatalysis ; Band gap ; Semiconductor ; Materials science ; Energy (signal processing) ; Component (thermodynamics) ; Machine learning ; Electronic band structure ; Artificial intelligence ; Computer science ; Optoelectronics ; Physics ; Catalysis ; Mathematics ; Statistics ; Thermodynamics ; Chemistry Machine Learning in Materials Science ; Advanced Photocatalysis Techniques ; X-ray Diffraction in Crystallography
Authors: Ayana Ghosh | Dennis Trujillo | Subhashis Hazarika | Elizabeth Schiesser | M. J. Swamynathan | S. Ghosh | Jian‐Xin Zhu | Serge Nakhmanson
Source: APL Machine Learning (article, 2023-12-01)
Field: Materials Science — Materials Chemistry

Identification of novel organic polar materials: A machine learning study with importance sampling Recent advances in the synthesis of polar molecular materials have produced practical alternatives to ferroelectric ceramics, opening up exciting new avenues for their incorporation into modern electronic devices. However, in order to realize the full potential of polar polymer and molecular crystals for modern technological applications, it is paramount to assemble and evaluate all the available data for such compounds, identifying descriptors that could be associated with an emergence of ferroelectricity. In this paper, we utilized data-driven approaches to judiciously shortlist candidate materials from a wide chemical space that could possess ferroelectric functionalities. A machine learning study with importance sampling was employed to address the challenge of having a limited amount of available data on already-known organic ferroelectrics. Sets of molecular- and crystal-level descriptors were combined with a Random Forest Regression algorithm in order to predict the spontaneous polarization of the shortlisted compounds. First-principles simulations were performed to further validate the predictions obtained from the machine learning model. Ferroelectricity ; Polar ; Machine learning ; Computer science ; Polarization (electrochemistry) ; Artificial intelligence ; Chemical space ; Identification (biology) ; Sampling (signal processing) ; Nanotechnology ; Materials science ; Chemistry ; Physics Machine Learning in Materials Science ; Solid-state spectroscopy and crystallography ; X-ray Diffraction in Crystallography
Authors: Alexandra Jakab-Nácsa | Attila Garami | Béla Fiser | László Farkas | Béla Viskolcz
Source: International Journal of Molecular Sciences (article, 2023-07-14)
Field: Chemistry — Organic Chemistry

Towards Machine Learning in Heterogeneous Catalysis—A Case Study of 2,4-Dinitrotoluene Hydrogenation Utilization of multivariate data analysis in catalysis research has extraordinary importance. The aim of the MIRA21 (MIskolc RAnking 21) model is to characterize heterogeneous catalysts with bias-free quantifiable data from 15 different variables to standardize catalyst characterization and provide an easy tool to compare, rank, and classify catalysts. The present work introduces and mathematically validates the MIRA21 model by identifying fundamentals affecting catalyst comparison and provides support for catalyst design. Literature data of 2,4-dinitrotoluene hydrogenation catalysts for toluene diamine synthesis were analyzed by using the descriptor system of MIRA21. In this study, exploratory data analysis (EDA) has been used to understand the relationships between individual variables such as catalyst performance, reaction conditions, catalyst compositions, and sustainable parameters. The results will be applicable in catalyst design, and using machine learning tools will also be possible. Catalysis ; Ranking (information retrieval) ; Computer science ; Heterogeneous catalysis ; Chemistry ; Machine learning ; Organic chemistry Nanomaterials for catalytic reactions ; Machine Learning in Materials Science ; Asymmetric Hydrogenation and Catalysis
Authors: Anwen Hu | Yongdi Huang | Qionghai Chen | Wanhui Huang | Xiaohui Wu | Lihong Cui | Yining Dong | Jun Liu
Source: APL Machine Learning (article, 2023-05-04)
Field: Materials Science — Materials Chemistry

Glass transition of amorphous polymeric materials informed by machine learning The glass transition temperature (Tg) is used to determine thermophysical properties of polymer materials and is often considered one of the most important descriptors. Methods for predicting various physical properties of materials based on machine learning algorithms and key molecular descriptors are efficient and accurate. However, it still needs improvements because an overly complex model is less practical and difficult to generalize. In addition, obtaining a large number of samples to achieve accurate predictions remains a challenge due to the complex and lengthy experimental process. In this work, based on Tg of 100 polymers, we use a feature selection algorithm combining FeatureWiz and the least absolute shrinkage and selection operator to quickly select molecular descriptors that are minimally redundant and maximally relevant to Tg. The processed dataset is interpolated from the original dataset using the nearest neighbor interpolation algorithm to solve the data deficiency problem. Finally, the synthetic minority oversampling technique algorithm is used to solve the data imbalance problem. The augmented dataset is used to construct the extreme gradient boosting prediction model to achieve good prediction accuracy. The experimental results demonstrate the robustness of the proposed model and the accuracy of its prediction results. Robustness (evolution) ; Computer science ; Feature selection ; Glass transition ; Artificial intelligence ; Machine learning ; Algorithm ; Oversampling ; Interpolation (computer graphics) ; Boosting (machine learning) ; Overfitting ; Shrinkage ; Polymer ; Materials science ; Artificial neural network ; Chemistry ; Motion (physics) Machine Learning in Materials Science ; Injection Molding Process and Properties ; Cultural Heritage Materials Analysis
Authors: R. Sato | Kentaro Kutsukake | Shunta Harada | Miho Tagawa | Toru Ujihara
Source: Advanced Theory and Simulations (article, 2023-06-15)
Field: Materials Science — Materials Chemistry

Machine Learning for Semiconductor Process Simulation Described by Coupled Partial Differential Equations Abstract Technology computer‐aided design (TCAD) simulation is an important tool for the development of semiconductor devices. Based on coupled partial differential equations (PDEs) for behaviors, TCAD can calculate objects such as impurities, point defects, and electronic carriers in semiconductors. However, over recent years semiconductor devices have become increasingly miniaturized and complicated, resulting in much longer calculation times for TCAD. Machine learning is one technology that may be used to overcome this simulation cost problem. In this study, a neural network architecture is proposed that considers the structure of the coupled PDEs. Features representing each concentration distribution of the calculation objects are extracted by convolution operations and their reaction is modeled by channel attention. The performance of the proposed architecture and of conventional neural network models is evaluated using a simulation dataset generated by 1D coupled PDEs that models the diffusion and reaction of vacancies and interstitial atoms. In addition, the advantage of the method is discussed through the analyses of error correlations of the two predictions and attention coefficients. The machine learning method developed in this study will be applicable to other physics described by coupled PDEs and is expected to speed up the computation of simulations in various fields. Partial differential equation ; Computer science ; Artificial neural network ; Computation ; Convolution (computer science) ; Technology CAD ; Process (computing) ; Semiconductor device ; Semiconductor device modeling ; Semiconductor ; Electronic engineering ; Computational science ; Differential equation ; Diffusion ; Computer engineering ; Artificial intelligence ; Algorithm ; Layer (electronics) ; CMOS ; Mathematics ; Engineering ; Electrical engineering ; Materials science ; CAD ; Physics ; Engineering drawing ; Mathematical analysis ; Nanotechnology Machine Learning in Materials Science ; Advancements in Semiconductor Devices and Circuit Design ; Silicon and Solar Cell Technologies
Authors: Longze Li | Jiang Chang | Aleksandar Vakanski | Yachun Wang | Tiankai Yao | Min Xian
Source: arXiv (Cornell University) (preprint, 2023-11-04)
Field: Materials Science — Materials Chemistry

Uncertainty Quantification in Multivariable Regression for Material Property Prediction with Bayesian Neural Networks With the increased use of data-driven approaches and machine learning-based methods in material science, the importance of reliable uncertainty quantification (UQ) of the predicted variables for informed decision-making cannot be overstated. UQ in material property prediction poses unique challenges, including the multi-scale and multi-physics nature of advanced materials, intricate interactions between numerous factors, limited availability of large curated datasets for model training, etc. Recently, Bayesian Neural Networks (BNNs) have emerged as a promising approach for UQ, offering a probabilistic framework for capturing uncertainties within neural networks. In this work, we introduce an approach for UQ within physics-informed BNNs, which integrates knowledge from governing laws in material modeling to guide the models toward physically consistent predictions. To evaluate the effectiveness of this approach, we present case studies for predicting the creep rupture life of steel alloys. Experimental validation with three datasets of collected measurements from creep tests demonstrates the ability of BNNs to produce accurate point and uncertainty estimates that are competitive or exceed the performance of the conventional method of Gaussian Process Regression. Similarly, we evaluated the suitability of BNNs for UQ in an active learning application and reported competitive performance. The most promising framework for creep life prediction is BNNs based on Markov Chain Monte Carlo approximation of the posterior distribution of network parameters, as it provided more reliable results in comparison to BNNs based on variational inference approximation or related NNs with probabilistic outputs. The codes are available at: https://github.com/avakanski/Creep-uncertainty-quantification. Uncertainty quantification ; Probabilistic logic ; Machine learning ; Computer science ; Inference ; Artificial intelligence ; Artificial neural network ; Bayesian inference ; Gaussian process ; Kriging ; Markov chain Monte Carlo ; Bayesian probability ; Gaussian Machine Learning in Materials Science ; Nuclear Materials and Properties ; Model Reduction and Neural Networks
Authors: Alex K. Chew | Matthew Sender | Zachary Kaplan | Anand Chandrasekaran | Andrea Browning | H. Shaun Kwak | Mathew D. Halls | Mohammad Atif Faiz Afzal
Source: ChemRxiv (preprint, 2023-06-08)
Field: Materials Science — Materials Chemistry

Advancing Material Property Prediction: Using Physics-Informed Machine Learning Models for Viscosity In materials science, accurately computing properties like viscosity, melting point, and glass transition temperatures solely through physics-based models is challenging. Data-driven machine learning (ML) also poses challenges in constructing ML models, especially in the material science domain where data is limited. To address this, we integrate physics-informed descriptors from molecular dynamics (MD) simulations to enhance the accuracy and interpretability of ML models. Our current study focuses on accurately predicting viscosity in liquid systems using MD descriptors. In this work, we curated a comprehensive dataset of over 4,000 small organic molecules’ viscosities from scientific literature, publications, and online databases. This dataset enabled us to develop quantitative structure–property relationships (QSPR) consisting of descriptor-based and graph neural network models to predict temperature-dependent viscosities for a wide range of viscosities with considerable accuracy. The QSPR models reveal that including MD descriptors improves prediction accuracies of experimental viscosities, particularly at the small data set scale of fewer than a thousand data points. Furthermore, feature importance tools reveal that intermolecular interactions captured by MD descriptors are most important for accurate viscosity predictions. Finally, the QSPR models can accurately capture the inverse relationship between viscosity and temperature for six battery-relevant solvents, some of which were not included in the original data set. Our research highlights the effectiveness of incorporating MD descriptors into QSPR models, which leads to improved accuracy for properties that are difficult to predict when using physics-based models alone or when limited data is available. Interpretability ; Quantitative structure–activity relationship ; Applicability domain ; Molecular descriptor ; Machine learning ; Artificial intelligence ; Experimental data ; Viscosity ; Computer science ; Predictive modelling ; Cheminformatics ; Artificial neural network ; Set (abstract data type) ; Data mining ; Mathematics ; Thermodynamics ; Chemistry ; Computational chemistry ; Physics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Various Chemistry Research Topics
Authors: Wanying Zhang | Xiaoyuan Wang | Yibo Ai | Weidong Zhang
Source: Materials Today Communications (article, 2023-11-19)
Field: Materials Science — Materials Chemistry

Selection of mechanical properties of uranium and uranium alloys after corrosion based on machine learning Uranium ; Materials science ; Ultimate tensile strength ; Corrosion ; Metallurgy ; Alloy Nuclear Materials and Properties ; Hydrogen embrittlement and corrosion behaviors in metals ; Nuclear reactor physics and engineering