jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Tao Wan | Wen Qi | Kangqi Cheng
Source: Highlights in Science Engineering and Technology (article, 2023-02-21)
Field: Chemistry — Inorganic Chemistry

Analysis and Prediction of Chemical Composition of Ancient Glass Relics Glass was introduced into China long ago via the Silk Road. After the ancient glass was buried, its internal elements would change under the influence of the environment. It is of great significance for archaeological work to study the changes of these chemical components. In this paper, the chemical composition of ancient glass products is analyzed and predicted by using R-type clustering, BP neural network, multiple linear regression and other models.First, after using SPSS statistical data, Pearson chi square test was conducted to draw a conclusion that the category, decoration and color were not related to whether the surface of the cultural relics was weathered. Then, descriptive statistics are made on the data. Finally, the multiple linear regression model is used to predict the unknown type of cultural relics. The results show that after the weathering of cultural relics, the content of potassium oxide, calcium oxide, aluminum oxide and magnesium oxide decreases compared with that before weathering, while the content of iron oxide increases.The main chemical components of high potassium glass relics and lead barium glass relics are calculated by entropy method. Compared with silicon dioxide and aluminum oxide, they have less influence on other chemical components. Therefore, these two chemical components are eliminated to classify cultural relics. Then, the R-type clustering model is established to classify the cultural relics, and the high potassium weathering type is obtained: 22 cultural relics sampling points form a category, and the other cultural relics form a category. Weathering ; Mineralogy ; Chemical composition ; Oxide ; Linear regression ; Materials science ; Archaeology ; Geology ; Metallurgy ; Mathematics ; Geography ; Geochemistry ; Statistics ; Physics ; Thermodynamics Pigment Synthesis and Properties ; Cultural Heritage Materials Analysis
Authors: Pezhman Abbasi | Ehsan Bahrami Moghadam
Source: SSRN Electronic Journal (preprint, 2023-01-01)
Field: Chemistry — Electrochemistry

Electrochemical Degradation of Ciprofloxacin from Water: Modeling and Prediction Using Ann and Lssvm Degradation (telecommunications) ; Environmental science ; Artificial neural network ; Computer science ; Soil science ; Artificial intelligence ; Telecommunications Electrochemical Analysis and Applications
Authors: Andrew Pannone | Jackson Robbins | Akshay Wali | Dipanjan Sen | Michael Felins | Saptarshi Das
Source: Research Square (preprint, 2023-01-09)
Field: Materials Science — Materials Chemistry

Machine Learning-Aided Non-Functionalized Graphene Chemitransistors for Highly Selective Chemisensing in Liquid Media Abstract The development of highly selective and miniaturized chemisensors for liquid media can enable newer discoveries and empower a wide range of disciplines from biology to human health, marine ecology to climate change, and industrial process monitoring to food safety. While the two-terminal chemiresistor architecture has been remarkably successful in this regard, they rely on appropriate surface functionalization to achieve selectivity for a specific target analyte. Furthermore, sensor drift and sensor-to-sensor variation in a multiplexed chemisensors array can severely impact their reliability and obscure the response of individual sensors to the target analytes. In this article, we overcome these challenges by introducing a new sensing paradigm that integrates an array of three-terminal chemitransistors based on monolayer graphene with an ensemble of machine learning (ML) algorithms. The extraordinary transport properties of graphene combined with its electrochemically inert basal plane allow a wide range of chemical species to electrostatically control the channel conductivity in a liquid medium through voltage gating. This in turn expands the parameter space for the sensing elements which enables the integration of various unsupervised and supervised ML algorithms to eliminate the need for surface functionalization, eradicate the need for sensor calibration, and minimize the impact of sensor-to-sensor variation. Our ML-aided, non-functionalized, and reusable graphene chemitransistors were found to be highly selective to de-ionized (DI) water, multiple chloride salts including NaCl, LiCl, CaCl2, and MgCl2 that are relevant to biosensing, several weak acids including acetic, propionic, and citric acid that are used in the food industry, as well as more complex solutions and processes such as decaying juices extracted from different fruits (grape, orange, watermelon, and pineapple). Graphene ; Materials science ; Nanotechnology ; Computer science Graphene research and applications ; Molecular Junctions and Nanostructures ; Electrochemical sensors and biosensors
Authors: Yinlong SHI | Shotaro HARA | Naoki Shikazono
Source: The Proceedings of Mechanical Engineering Congress Japan (article, 2023-01-01)
Field: Materials Science — Materials Chemistry

Prediction of Local Stress Distribution in SOFC Electrodes using Deep Learning In order to manufacture highly durable solid oxide fuel cell (SOFC) electrodes, a technique is required to accurately assess the thermal stress distribution within porous electrodes. However, the traditional numerical simulation methods, such as the finite element method (FEM), are often computationally expensive and time-consuming. To address this issue, we have developed an efficient deep learning model utilizing Generative Adversarial Networks (GAN), which is capable of predicting the thermal stress distribution generated in porous materials under biaxial constraints. We show that our Pix2pix GAN model predicts the stress concentration regions within the 2D cross-sectional images extracted from their 3D microstructures with high accuracy, particularly by incorporating 3D density information into the training data. Furthermore, we provide an effective method to create more realistic training data by combining the numerical simulations and the existing deep learning techniques. This method allows us to predict the stress concentration regions generated in FIB-SEM actual microstructures with higher accuracy. Environmental science Machine Learning in Materials Science ; Model Reduction and Neural Networks ; Advancements in Solid Oxide Fuel Cells
Authors: Yukun Wang | Wang Chen | Xiaomeng An | Rui Wang | Yuanyuan Li | Yinyin Xu
Source: SSRN Electronic Journal (preprint, 2023-01-01)
Field: Chemistry — Analytical Chemistry

Effectively Removal of Ppcps By Catalytic Activated Biochar Derived from Hazelnut Shell: Modeled and Predicted by Machine Learning Biochar ; Shell (structure) ; Catalysis ; Chemistry ; Activated carbon ; Environmental chemistry ; Waste management ; Environmental science ; Pulp and paper industry ; Chemical engineering ; Adsorption ; Materials science ; Engineering ; Organic chemistry ; Composite material Spectroscopy and Chemometric Analyses
Authors: BenBen Sun | Wanbing Guan | Jianxin Wang
Source:  (article, 2023-10-19)
Field: Materials Science — Materials Chemistry

Implementation of a low-code monitoring system for solid oxide fuel cells Solid oxide fuel cells (SOFCs) have the advantages of high conversion efficiency, low working noise, and environmental protection. Thus, they are widely used in large-scale power generation, combined heat power, peak load-regulating energy storage, and other fields. In the actual working process of SOFCs, the battery performance will decline sharply due to extreme conditions such as overtemperature and peroxide, and the service life will be terminated. Remote monitoring of fuel cells can detect problems in time and restore the battery’s performance by restoring the battery, thereby extending its service life. The data generated by fuel cells during operation have a strong temporal correlation. To discover the future state of fuel cells in time and solve the problem that a large number of codes are needed for monitoring, this paper designs a remote monitoring system of fuel cells with low-code based on Node-RED and Ali Cloud. The output current of fuel cells is predicted by using the improved back propagation neural network based on Levenberg-Marguardt (LM-BPNN) algorithm. The test result of LM-BPNN shows that the test samples have a root mean square error of 0.172, a mean absolute percentage error of 3.85%, and a coefficient of determination of 0.998. Computer science ; Code (set theory) ; Embedded system ; Operating system ; Programming language Advancements in Solid Oxide Fuel Cells
Authors: Jiayu Peng | James Damewood | Rafael Gómez‐Bombarelli
Source: arXiv (Cornell University) (preprint, 2023-05-02)
Field: Materials Science — Materials Chemistry

Data-Driven, Physics-Informed Descriptors of Cation Ordering in Multicomponent Oxides The structural tunability and compositional diversity of multicomponent perovskite oxides have enabled their various applications, including catalysis and electronics. The cation ordering in these oxides, ranging from disordered (i.e., high-entropy) to ordered (e.g., rocksalt), profoundly influences their properties. While computational design tools can typically predict properties associated with a particular ordering, inferring which ordering -- if any -- will be observed in synthesized oxides remains challenging. Here, we leveraged first-principles simulations and machine learning to develop data-driven, physics-informed descriptors of experimental ordering in multicomponent perovskites and compared them with traditional physicochemical descriptors, e.g., ionic radii and oxidation states. The fitted low-dimensional classification models correctly rank up to 93% of compositions in an experimental dataset of 190 perovskites between cation-ordered and disordered, offering a rigorous benchmark between theory and experiments. Furthermore, these descriptors accelerate high-throughput virtual screening of multicomponent oxides by predicting their dominant ordering to avoid costly, exhaustive simulations of cation arrangements. Perovskite (structure) ; Benchmark (surveying) ; Materials science ; Ionic bonding ; Chemical physics ; Computer science ; Statistical physics ; Ion ; Nanotechnology ; Chemistry ; Physics ; Crystallography Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Electronic and Structural Properties of Oxides
Authors: Rong‐Jun Xie
Source: ECS Meeting Abstracts (article, 2023-08-28)
Field: Materials Science — Materials Chemistry

(Digital Presentation) Data-Driven Discovery of Luminescent Materials Luminescent materials play an important roles in lighting, display, plant growth, anti-counterfeit, medical and bio-technologies. The search for luminescent materials with desired properties never stops, but relies mostly on the trial-and-error approach, which is time-consuming and labor-intensive. Several methods have been proposed to accelerate the discovery of new luminescent materials, among them the data-driven one attracts much attention. In this presentation, two types of luminescent materials for different applications will be reported. Firstly, we build an emission-prediction model based on machine learning, and using this model found five Eu2+-doped nitride phosphors with highly efficient near-infrared (NIR) emissions. Secondly, we propose selection rules to discover laser phosphors and mechanoluminescent materials based on the structure-property relations, respectively. The applications of these phosphors in NIR detectors, laser lighting and stress mapping will also demonstrated. Phosphor ; Luminescence ; Computer science ; Materials science ; Laser ; Process engineering ; Optoelectronics ; Engineering ; Optics ; Physics Machine Learning in Materials Science ; Luminescence Properties of Advanced Materials ; Inorganic Chemistry and Materials
Authors: Shaymaa A. Qenawy | Loai Nasrat | Nesreen. M. Samir
Source:  (article, 2023-12-19)
Field: Materials Science — Materials Chemistry

Dielectric and Mechanical Properties Improvement of High Voltage XLPE Using Micro and Nano Aluminum Oxide Filler Recently, researchers have become increasingly interested in blends and polymer composites for dielectric applications usage. The industrial sector approved the tailoring of polymer features for objectives. This work describes the adjustments of the electromechanical properties bundle for cross linked polyethylene (XLPE) compounded to nano and micro scale Aluminum oxide (XLPE/Al<inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf>O<inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</inf>) to generate electrical cables insulating material with superior performance. By using a process called melt intercalation, composites with different filler loading weight percentages were created. The electrical lab test included tests for dielectric strength, permittivity, and loss angle, while the mechanical test pack included elongation and tensile strength. Another test on the nano composites was done using a scanning electron microscope (SEM). For the dielectric strength estimation of the composites with typical contents but with varying filler loading or affected by other medium salinity degrees, a machine learning algorithm was developed and trained using acquired practical findings. The research results demonstrated that XLPE compounded to surface characterized nano Al<inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf>O<inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</inf> is superior to the pristine host and micro composite in most electrical and mechanical properties, with the best results obtained with modest percentages of filler loading of almost 1.5 wt%. Materials science ; Filler (materials) ; Aluminum oxide ; Dielectric ; Nano- ; Composite material ; Aluminium ; Oxide ; Voltage ; Electrical engineering ; Optoelectronics ; Metallurgy ; Engineering High voltage insulation and dielectric phenomena ; Dielectric materials and actuators ; Power Transformer Diagnostics and Insulation
Authors: Nuo Shi
Source:  (article, 2023-12-08)
Field: Materials Science — Materials Chemistry

Phase-change materials-integrated MRR-type 2×2 optical switch based on slot-ridge waveguide structure All-optical computing has attracted attention for its faster computation speed and lower energy consumption compared to conventional electrical computing methods. Optical switches are the key basic units of optical computing devices. However, conventional optical switches suffer from the disadvantages of consuming external energy to maintain the switching state, high extinction ratio, and poor optical field modulation, which greatly limit the realization of large-scale photonic integrated circuits and optical pulse neural networks. In this paper, we utilize a Lumerical simulation platform to design a nonvolatile MRR-type 2 × 2 optical switch based on the phase-change materials Ge2Sb2Te5 (GST) using a slot-ridge waveguide, which possesses high extinction ratios of 25.31 dB at the THROUGH port and 8.97 dB at the DROP port. Changing the phase state of the GST modulates the transmission of the optical switch with a higher extinction ratio than previous optical switches. The absence of static power consumption in maintaining the phase state renders it very suitable for utilization in optical switch matrices and reconfigurable optical pulse neural networks, therefore presenting exciting prospects for these applications. Ridge ; Materials science ; Waveguide ; Optics ; Integrated optics ; Optoelectronics ; Phase (matter) ; Geology ; Physics Phase-change materials and chalcogenides ; Liquid Crystal Research Advancements ; Nonlinear Optical Materials Research
Authors: Sam Martino | João Morado | Chenghao Li | Zhenghao Lu | Edina Rosta
Source: arXiv (Cornell University) (preprint, 2023-12-22)
Field: Materials Science — Materials Chemistry

Large Scale Training of Graph Neural Networks for Optimal Markov-Chain Partitioning Using the Kemeny Constant Traditional clustering algorithms often struggle to capture the complex relationships within graphs and generalise to arbitrary clustering criteria. The emergence of graph neural networks (GNNs) as a powerful framework for learning representations of graph data provides new approaches to solving the problem. Previous work has shown GNNs to be capable of proposing partitionings using a variety of criteria, however, these approaches have not yet been extended to work on Markov chains or kinetic networks. These arise frequently in the study of molecular systems and are of particular interest to the biochemical modelling community. In this work, we propose several GNN-based architectures to tackle the graph partitioning problem for Markov Chains described as kinetic networks. This approach aims to minimize how much a proposed partitioning changes the Kemeny constant. We propose using an encoder-decoder architecture and show how simple GraphSAGE-based GNNs with linear layers can outperform much larger and more expressive attention-based models in this context. As a proof of concept, we first demonstrate the method's ability to cluster randomly connected graphs. We also use a linear chain architecture corresponding to a 1D free energy profile as our kinetic network. Subsequently, we demonstrate the effectiveness of our method through experiments on a data set derived from molecular dynamics. We compare the performance of our method to other partitioning techniques such as PCCA+. We explore the importance of feature and hyperparameter selection and propose a general strategy for large-scale parallel training of GNNs for discovering optimal graph partitionings. Computer science ; Cluster analysis ; Graph partition ; Theoretical computer science ; Markov chain ; Graph ; Hyperparameter ; Machine learning ; Artificial intelligence Machine Learning in Materials Science ; Advanced Graph Neural Networks ; Graph Theory and Algorithms
Authors: Ziyu Zhou | Sixiang Ye | Yiming Huang | Jiayue Zhang
Source:  (article, 2023-06-26)
Field: Materials Science — Materials Chemistry

ChemCLS: a framework for applying supervised and weakly supervised machine learning to better interpretable cultural relic classification Machine learning algorithms advance the classification of cultural relics in recent studies. A part of the studies adopts image processing methods as a solution. We believe that the blurring of the image limits this approach. On the other hand, we notice the studies conducted from the perspective of chemical composition, which avoids the problem of blurring images and shows interpretability. In this paper, we follow the direction of classifying by chemical composition and aim to provide our method with better interpretability. Hence, we propose a strategy that combined both supervised and weakly supervised methods as the solution. It visualizes the decision tree of classifying the main classes of cultural relics at first and extracts the chemical composition of the subclasses with the pseudo-labeling according to limited subclass labels next. Besides interpretability, it also performs accurately in subclasses classification by using the radial basis function network and random forest. Our results indicate that our weakly supervised method achieves the closest reduction to a finer classification. Interpretability ; Artificial intelligence ; Decision tree ; Machine learning ; Computer science ; Pattern recognition (psychology) ; Random forest ; Supervised learning ; Perspective (graphical) ; Contextual image classification ; Notice ; Image (mathematics) ; Basis (linear algebra) ; Mathematics ; Artificial neural network Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Cultural Heritage Materials Analysis
Authors: Qingyang Fan | Nan Wu | Yingbo Zhao | Yanxing Song | Xinhai Yu | Sining Yun
Source: SSRN Electronic Journal (preprint, 2023-01-01)
Field: Materials Science — Materials Chemistry

Study of the Elastic Modulus of Boron Nitride Polymorphs: First-Principles Calculations and Machine Learning Boron nitride ; Materials science ; Elastic modulus ; Boron ; Modulus ; Nitride ; Thermodynamics ; Composite material ; Chemistry ; Physics ; Organic chemistry Boron and Carbon Nanomaterials Research ; Machine Learning in Materials Science ; Boron Compounds in Chemistry
Authors: Ricardo Gomez Gomez | Daniel Whisler
Source:  (article, 2023-09-18)
Field: Materials Science — Materials Chemistry

IMPROVING UNIAXIAL DYNAMIC TENSION TESTING OF COMPOSITE SPECIMENS Validation of composite specimens on the order of a few millimeters in length in tension can be examined dynamically via uniaxial test rigs and split Hopkinson pressure bars. For compression testing, hydraulic/gravity drop towers and the Hopkinson bar are permissible for larger specimens (on the order of centimeters in length) and up to 1,000 1/s. Uniaxial tension test methods for composite specimens enabling both configurations, i.e., several centimeters in length and at strain rates up to 1,000 1/s, is much more limited. The goal of this research investigation is to test precisely in this regime by optimizing the composite coupon specimen for dynamic material characterization. For this project, a tension setup using a single Hopkins on bar was adapted to enable up to 6 cm long specimens tested at the requisite strain rates. Coupon specimens were then optimized using machine learning to promote stress in the gage region thereby minimizing testing failures if the specimen were to break near the grips. Using these two systems, we may have a proof of concept for high strain rate tension testing of composites that is optimized for validation studies using machine learning based geometries. Split-Hopkinson pressure bar ; Tension (geology) ; Composite number ; Materials science ; Bar (unit) ; Composite material ; Universal testing machine ; Strain gauge ; Strain rate ; Structural engineering ; Compression (physics) ; Dynamic testing ; Drop test ; Engineering ; Geology ; Ultimate tensile strength High-Velocity Impact and Material Behavior ; Electromagnetic Launch and Propulsion Technology ; Transportation Safety and Impact Analysis