jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Moutusi Paul | Palash Mishra | Pabbati Vinod | R. Sarathi | Mithun Mondal
Source: Polymer Composites (article, 2022-11-10)
Field: Materials Science — Materials Chemistry

Performance evaluation of gamma‐ray irradiated silicone rubber nano‐micro composites using electrical, thermal, physiochemical and deep learning techniques Abstract Silicone rubber insulation used in nuclear power stations is inevitably exposed to highly energetic gamma‐ray irradiations which may lead to premature failure. Therefore, it is mandatory to develop silicone rubber composites capable of withstanding the stress posed by gamma‐ray irradiation. The present research involves the development of micro aluminum tri hydrate (ATH) and nano silica (SiO 2 ) doped/co‐doped silicone rubber nanocomposites best suited for use in nuclear power stations. The samples were exposed to gamma‐ray irradiation of 500 kGy. Surface changes incurred were assessed using atomic force microscopy (AFM), fourier transform infrared (FTIR) spectroscopy, contact angle, surface potential/impedance, water droplet‐initiated corona inception voltage (CIV) measurements thermogravimetric analysis (TGA) was adopted to characterize the thermal stability of the specimens. Further moisture absorption behavior of the test specimens under strong electrolytic NaCl solution has been investigated. The erosion resistance of the virgin and gamma‐ray irradiated specimens has been classified using GoogLeNet convolution neural network. Materials science ; Silicone rubber ; Composite material ; Thermogravimetric analysis ; Irradiation ; Fourier transform infrared spectroscopy ; Nanocomposite ; Natural rubber ; Silicone ; Thermal stability ; Gamma ray ; Chemical engineering High voltage insulation and dielectric phenomena ; Electrical Fault Detection and Protection ; Infrastructure Maintenance and Monitoring
Authors: Massimiliano Lupo Pasini | Zhang, Pei | Samuel Temple Reeve | Jong Youl Choi
Source: arXiv (Cornell University) (preprint, 2022-02-04)
Field: Materials Science — Materials Chemistry

Multi-task graph neural networks for simultaneous prediction of global and atomic properties in ferromagnetic systems We introduce a multi-tasking graph convolutional neural network, HydraGNN, to simultaneously predict both global and atomic physical properties and demonstrate with ferromagnetic materials. We train HydraGNN on an open-source ab initio density functional theory (DFT) dataset for iron-platinum (FePt) with a fixed body centered tetragonal (BCT) lattice structure and fixed volume to simultaneously predict the mixing enthalpy (a global feature of the system), the atomic charge transfer, and the atomic magnetic moment across configurations that span the entire compositional range. By taking advantage of underlying physical correlations between material properties, multi-task learning (MTL) with HydraGNN provides effective training even with modest amounts of data. Moreover, this is achieved with just one architecture instead of three, as required by single-task learning (STL). The first convolutional layers of the HydraGNN architecture are shared by all learning tasks and extract features common to all material properties. The following layers discriminate the features of the different properties, the results of which are fed to the separate heads of the final layer to produce predictions. Numerical results show that HydraGNN effectively captures the relation between the configurational entropy and the material properties over the entire compositional range. Overall, the accuracy of simultaneous MTL predictions is comparable to the accuracy of the STL predictions. In addition, the computational cost of training HydraGNN for MTL is much lower than the original DFT calculations and also lower than training separate STL models for each property. Convolutional neural network ; Computer science ; Tetragonal crystal system ; Artificial neural network ; Ferromagnetism ; Artificial intelligence ; Algorithm ; Materials science ; Machine learning ; Chemistry ; Physics ; Condensed matter physics Machine Learning in Materials Science ; Magnetic properties of thin films ; Heusler alloys: electronic and magnetic properties
Authors: Shi-Yu Wang | Yuanqi Du | Xiaojie Guo | Bo Pan | Zhaohui Qin | Liang Zhao
Source: arXiv (Cornell University) (review, 2022-07-19)
Field: Materials Science — Materials Chemistry

Controllable Data Generation by Deep Learning: A Review Designing and generating new data under targeted properties has been attracting various critical applications such as molecule design, image editing and speech synthesis. Traditional hand-crafted approaches heavily rely on expertise experience and intensive human efforts, yet still suffer from the insufficiency of scientific knowledge and low throughput to support effective and efficient data generation. Recently, the advancement of deep learning has created the opportunity for expressive methods to learn the underlying representation and properties of data. Such capability provides new ways of determining the mutual relationship between the structural patterns and functional properties of the data and leveraging such relationships to generate structural data, given the desired properties. This article is a systematic review that explains this promising research area, commonly known as controllable deep data generation. First, the article raises the potential challenges and provides preliminaries. Then the article formally defines controllable deep data generation, proposes a taxonomy on various techniques and summarizes the evaluation metrics in this specific domain. After that, the article introduces exciting applications of controllable deep data generation, experimentally analyzes and compares existing works. Finally, this article highlights the promising future directions of controllable deep data generation and identifies five potential challenges. Deep learning ; Computer science ; Data science ; Artificial intelligence ; Representation (politics) ; Domain (mathematical analysis) ; Machine learning Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Innovative Microfluidic and Catalytic Techniques Innovation
Authors: Hartmut Schlenz | Stefan Baumann | Wilhelm Albert Meulenberg | Olivier Guillon
Source: Crystals (article, 2022-07-05)
Field: Materials Science — Materials Chemistry

The Development of New Perovskite-Type Oxygen Transport Membranes Using Machine Learning The aim of this work is to predict suitable chemical compositions for the development of new ceramic oxygen gas separation membranes, avoiding doping with toxic cobalt or expensive rare earths. For this purpose, we have chosen the system Sr1−xBax(Ti1−y−zVyFez)O3−δ (cubic perovskite-type phases). We have evaluated available experimental data, determined missing crystallographic information using bond-valence modeling and programmed a Python code to be able to generate training data sets for property predictions using machine learning. Indeed, suitable compositions of cubic perovskite-type phases can be predicted in this way, allowing for larger electronic conductivities of up to σe = 1.6 S/cm and oxygen conductivities of up to σi = 0.008 S/cm at T = 1173 K and an oxygen partial pressure pO2 = 10−15 bar, thus enabling practical applications. Perovskite (structure) ; Valence (chemistry) ; Oxygen ; Ceramic ; Python (programming language) ; Partial pressure ; Materials science ; Doping ; Membrane ; Computer science ; Chemistry ; Crystallography ; Optoelectronics ; Metallurgy Advancements in Solid Oxide Fuel Cells ; Fuel Cells and Related Materials ; Electrocatalysts for Energy Conversion
Authors: Xiaohua Li | Jian Wen | Yanhua Fu | Yachun Mao | Wang Cao | Jiaqi Huang | Zhanguo Zhao | Ge Yu
Source: Infrared Physics & Technology (article, 2022-04-06)
Field: Chemistry — Analytical Chemistry

Visible-NIR spectral characteristics and grade inversion model of skarn-type iron ore Hyperspectral imaging ; Iron ore ; Inversion (geology) ; Principal component analysis ; Skarn ; Data pre-processing ; Computer science ; Preprocessor ; Pattern recognition (psychology) ; Algorithm ; Artificial intelligence ; Materials science ; Geology Spectroscopy and Chemometric Analyses ; Mineral Processing and Grinding ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: Sihoon Choi | David S. Sholl | Andrew J. Medford
Source: The Journal of Chemical Physics (article, 2022-05-16)
Field: Chemistry — Inorganic Chemistry

Gaussian approximation of dispersion potentials for efficient featurization and machine-learning predictions of metal–organic frameworks Energy-related descriptors in machine learning are a promising strategy to predict adsorption properties of metal-organic frameworks (MOFs) in the low-pressure regime. Interactions between hosts and guests in these systems are typically expressed as a sum of dispersion and electrostatic potentials. The energy landscape of dispersion potentials plays a crucial role in defining Henry's constants for simple probe molecules in MOFs. To incorporate more information about this energy landscape, we introduce the Gaussian-approximated Lennard-Jones (GALJ) potential, which fits pairwise Lennard-Jones potentials with multiple Gaussians by varying their heights and widths. The GALJ approach is capable of replicating information that can be obtained from the original LJ potentials and enables efficient development of Gaussian integral (GI) descriptors that account for spatial correlations in the dispersion energy environment. GI descriptors would be computationally inconvenient to compute using the usual direct evaluation of the dispersion potential energy surface. We show that these new GI descriptors lead to improvement in ML predictions of Henry's constants for a diverse set of adsorbates in MOFs compared to previous approaches to this task. Dispersion (optics) ; Gaussian ; Energy landscape ; Statistical physics ; Energy (signal processing) ; Pairwise comparison ; Potential energy ; Biological system ; Computer science ; Chemical physics ; Chemistry ; Physics ; Artificial intelligence ; Computational chemistry ; Quantum mechanics ; Thermodynamics Metal-Organic Frameworks: Synthesis and Applications ; Machine Learning in Materials Science ; Crystallography and molecular interactions
Authors: Kennedy C. Onyelowe | Jamshid Shakeri | Hasel Amini Khoshalan | Thompson Usungedo | Mohammadreza Alimoradi-Jazi
Source: Advances in Materials Science and Engineering (article, 2022-02-27)
Field: Materials Science — Polymers and Plastics

Computational Modeling of Desiccation Properties (CW, LS, and VS) of Waste-Based Activated Ash-Treated Black Cotton Soil for Sustainable Subgrade Using Artificial Neural Network, Gray-Wolf, and Moth-Flame Optimization Techniques Artificial neural network (ANN), gray-wolf, and moth-flame optimization (GWO and MFO) techniques have been used in this research work to predict the effect of activated sawdust ash (ASDA) on the crack width (CW), linear shrinkage (LS), and volumetric shrinkage (VS) of a black cotton soil utilized as a subgrade material. Problematic soils or black cotton soils are not good pavement foundation materials except that they are pretreated in order to meet the basic strength characteristics required for roads in Nigeria. Due to this reason, there has been ongoing research to evaluate the best practices in which black cotton soils can be favorably utilized in earthwork construction. On the other hand, there is a huge concern on the solid waste management system in the wood processing environment and the recycling of sawdust into ash and its reuse as an alternative binder has offered a sustainable disposal system. The work tries to use AI-based techniques to predict the crack and shrinkage behaviors of BCS treated with saw dust ash activated with alkali materials. There was appreciable improvement in the shrinkage and crack parameters over the 30-day drying period due to the addition of ASDA. The intelligent model results showed that the three techniques successfully predicted the CW, LS, and VS with a performance accuracy above 90%, while ANN produced the minimal error in performance outperforming the other techniques. Sensitivity study showed that the drying time (T) was the most influential of the studied parameter. Hence, soil stabilization has shown its potential system of waste management in the wood processing industry. Shrinkage ; Subgrade ; Materials science ; Soil water ; Sawdust ; Black liquor ; Environmental science ; Pulp and paper industry ; Waste management ; Composite material ; Geotechnical engineering ; Soil science ; Engineering Natural Fiber Reinforced Composites ; Innovations in Concrete and Construction Materials ; Coal Combustion and Slurry Processing
Authors: Elisa Marenzi | Emanuele Torti | Giovanni Danese | Francesco Leporati
Source: 2022 11th Mediterranean Conference on Embedded Computing (MECO) (article, 2022-06-07)
Field: Chemistry — Analytical Chemistry

FPGA High Level Synthesis for the classification of skin tumors with hyperspectral images Cancer is the main cause of premature death in the world, with 18 million diagnoses in 2018, 3.9 million of which in Europe. In particular, according to studies conducted by the American Academy of Dermatology, skin cancer is the most prevalent type in the US. Diagnostic tools are generally invasive, hence research focuses on emerging technologies, like hyperspectral images, since they are non-invasive, contactless and non-ionizing. A hyperspectral image acquisition system has been used to produce a database of 49 images from 36 patients, used to validate an innovative machine learning algorithm. Starting from our original serial implementation, a novel version has been developed with modern technologies of High Level Synthesis (HLS) using FPGA, to verify the feasibility of a portable instrument. Differences in implementation, HLS optimizations and latency times have been compared and evaluated. The algorithm has been tested on different FPGAs, to identify the optimal device for the purpose. Finally, the proposed hardware architecture processes hyperspectral images dissipating less energy than state-of-the-art GPU solutions. Hyperspectral imaging ; Field-programmable gate array ; Computer science ; Medical diagnosis ; Embedded system ; Latency (audio) ; Architecture ; Computer architecture ; Artificial intelligence ; Medicine ; Telecommunications Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Optical Coherence Tomography Applications
Authors: Ziyi Chu | Fengmei Li | Dongwei WANG | Shusheng Xu | Chunfeng GAO | Haoran Bai
Source: Food Science and Technology (article, 2022-01-01)
Field: Chemistry — Analytical Chemistry

Research on identification method of tangerine peel year based on deep learning Tangerine Peel has rich medicinal value, known as ' one kilogram of tangerine peel, one kilogram of gold '. However, the value of tangerine peels in different years is different, and there is no significant difference in the appearance of tangerine peels in different years. Identifying their authenticity has brought trouble to the industry. Generally speaking, the characteristics of tangerine peel can be identified through the texture, color and oil parcel points on the surface of tangerine peel. However, compared with the feature recognition of other Chinese medicinal materials, there is no significant difference in the shape of tangerine peel in different years, and the color is similar. Therefore, the feature extraction of tangerine peel is more complicated and the recognition is more difficult. The existing deep learning algorithms face great challenges in efficient and high accuracy recognition. In response to this challenge, this paper builds a new lightweight tangerine peel recognition algorithm TPRA (Tangerine Peel Recognition Algorithm) based on ResNet50. This algorithm uses a variety of methods to optimize the generalization ability of the model and improve the recognition accuracy. Firstly, TPRA adopts mixed data enhancement, including traditional data enhancement, deep convolution generation confrontation network DCGAN, and Mosaic data enhancement to enhance the richness of sample images in the dataset, reduced the data of each batch regularization (Batch Normal), and enhanced the performance of algorithm identification. Secondly, TPRA introduced the attention mechanism module CBAM (Convolutional Block Attention Module) combined with the cross stage partial network CSPNet (Cross Stage Partial Network) to propose an improved ResNet50 model, which adjusts the position of the maximum pooling layer and disassembles the large convolution kernel to effectively avoid overfitting. The experimental results showed that the accuracy of the algorithm can reach 98.8%, and the effect was better than that of Alexnet, VGG16 and Resnet50. TPRA provided a new method for the identification of peel years. Artificial intelligence ; Pattern recognition (psychology) ; Chemistry ; Computer science ; Mathematics Spectroscopy and Chemometric Analyses ; Microbial Metabolism and Applications ; Cultural Heritage Materials Analysis
Authors: Chitra Boobalan | Sathish Kumar Kannaiyan
Source: International Journal of Energy Research (article, 2022-02-28)
Field: Materials Science — Materials Chemistry

A correlation to predict the thermal conductivity of MXene‐silicone oil based nano‐fluids and data driven modeling using artificial neural networks In this article, MXene nano-material is used with silicone oil to improve the thermal conductivity of nano-fluids and it further increases the heat transfer. MXene nano-material-silicone oil-based nano-fluids have the capacity to be employed as heat transfer fluids in various thermal applications since it has excellent thermal properties, higher heat resistance and silicone oil have the ability to operate upto a temperature of 400°C. This paper investigates the thermal conductivity values for different concentrations and temperature of MXene nano-fluids. The thermal conductivity of the prepared MXene nano-fluids is strongly dependent on temperature and concentration of MXene nano-particles. The values of thermal conductivity are predicted by developing multilayer perceptron (MLP) Artificial Neural Network (ANN) model. The temperature and nano-particles concentration are taken as inputs and thermal conductivity is taken as output to the model. Mean square error (MSE) and correlation coefficient (R) are used to assess the efficacy of the developed model using trial and error method. The values of MSE and correlation coefficient for the optimum model are found to be 2.6 × 10−07 and 0.99687, respectively. A correlation for thermal conductivity as a function of temperature and concentration is also propounded using the experimental results. The developed optimum ANN model show satisfactory performance, and the predicted thermal conductivity values matches well with experimental thermal conductivity values. Thermal conductivity ; Materials science ; Silicone oil ; Nano- ; Nanofluid ; Heat transfer ; Thermodynamics ; Silicone ; Heat transfer coefficient ; Correlation coefficient ; Thermal resistance ; Composite material ; Machine learning ; Computer science ; Physics MXene and MAX Phase Materials ; Thermal properties of materials ; Phase Change Materials Research
Authors: Lechuan Hu | Lanxin Ma | Chengchao Wang | Linhua Liu
Source: Optics Express (article, 2022-07-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Machine learning-assisted design of polarization-controlled dynamically switchable full-color metasurfaces Dynamic color tuning has significant application prospects in the fields of color display, steganography, and information encryption. However, most methods for color switching require external stimuli, which increases the structural complexity and hinders the applicability of front-end dynamic display technology. In this study, we propose polarization-controlled hybrid metal-dielectric metasurfaces to realize full-color display and dynamic color tuning by altering the polarization angle of incident light without changing the structure and properties of the material. A bidirectional neural network is trained to predict the colors of mixed metasurfaces and inversely design the geometric parameters for the desired colors, which is less dependent on design experience and reduces the computational cost. According to the color recognition ability of human eyes, the accuracy of color prediction realized in our study is 93.18% and that of inverse parameter design is 92.37%. This study presents a simple method for dynamic structural color tuning and accelerating the design of full-color metasurfaces, which can offer further insight into the design of color filters and promote photonics research. Computer science ; Polarization (electrochemistry) ; Optics ; Color filter array ; Structural coloration ; Autostereoscopy ; Photonics ; Artificial intelligence ; Color gel ; Computer vision ; Materials science ; Photonic crystal ; Physics ; Nanotechnology Metamaterials and Metasurfaces Applications ; Polydiacetylene-based materials and applications ; Photonic Crystals and Applications
Authors: Aziz Saaidia | Ahmed Belaadi | A. Haddad
Source: Journal of Natural Fibers (article, 2022-05-10)
Field: Materials Science — Polymers and Plastics

Moisture Absorption of cork-based Biosandwich Material Extracted from <i>Quercussuber L</i>. Plant: ANN and Fick’s Modelling The building sector is one of the most dynamic in terms of energy consumption, consuming about 40% of the world’s energy. This same sector is also responsible for about 1/3 of the world’s greenhouse gas emissions. In recent years, the adoption of composite materials, particularly those strengthened through the use of natural fibers is growing in all areas. This increase is the direct result of the important performances offered by these materials and that includes lightness, thermal, and acoustic insulation along with respect for the environment. This led to the integration of materials, such as bio composites or bio sandwiches, into various building structures constructions relating to civil engineering. However, numerous researches related to bio composites showed the need to explore them further particularly concerning the issue of moisture absorption as the presence of water affects the behavior of plant fibers both in terms of swelling and degradation. It is within this context that the present study focuses on modeling the water absorption behavior of bio sandwich materials having an agglomerated cork core associated with fibers extracted from the plant Quercussuber L. Fick’s law and Artificial Neural Network (ANN) are applied to model the experimental results pertaining to this absorption behavior. The experimental investigation starts by placing the original samples in tank filled with distilled water at an ambient temperature of 25°C. Mass samples are later and periodically taken on specimen with cork core having different thicknesses (5, 10, and 20 mm) as well as on laminated skin sandwiches made of short flax fibers until saturation that lasted around 25 days. The two Fick’s diffusion characteristic parameters represented by the mass gain at saturation (Mm) and the diffusion coefficient (D) were determined analytically and water absorption kinetics behavior was recorded and later compared to the curves predicted by Fick’s laws. Statistical processing of the results was carried out through the application of the analysis of variance ANOVA. Cork ; Materials science ; Composite material ; Context (archaeology) ; Absorption of water ; Moisture ; Absorption (acoustics) ; Water content ; Environmental science ; Composite number ; Engineering ; Geotechnical engineering Natural Fiber Reinforced Composites ; Plant Surface Properties and Treatments ; Bamboo properties and applications