jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Yuhan Liu | Cuiling Liu | Caijin Ling | Qiaoyi Zhou | Xiaorong Sun | Di Wu
Source:  (article, 2024-09-06)
Field: Chemistry — Analytical Chemistry

Nondestructive Classification of Black Tea Grades Based on Hyperspectral Imaging and Machine Learning Tea grade classification is a crucial standard in assessing tea quality, allowing for a rapid evaluation of the general quality of tea. This paper proposes a method for black tea grade classification based on hyperspectral imaging technology, chemometric algorithms, and machine learning. Standard Normal Variate (SNV) was used to preprocess the hyperspectral data. Then, correlation analysis was conducted between tea grades and chemical values to identify the chemical values that directly reflect tea grades. To reduce redundant wavelengths and improve model accuracy, Successive Projections Algorithm (SPA) and Competitive Adaptive Reweighted Sampling (CARS) were employed for secondary feature wavelength selection. Support Vector Machine (SVM) was used to establish classification models for single and multiple fused chemical values. The results show that combining hyperspectral imaging technology with machine learning can achieve excellent results in tea grade classification, with an accuracy of $\mathbf{8 8 \%}$. This method provides a theoretical basis for the rapid and non-destructive online classification of black tea grades. Hyperspectral imaging ; Artificial intelligence ; Computer science ; Black tea ; Nondestructive testing ; Contextual image classification ; Pattern recognition (psychology) ; Machine learning ; Remote sensing ; Computer vision ; Geology ; Image (mathematics) ; Chemistry ; Physics Spectroscopy and Chemometric Analyses ; Remote Sensing and Land Use
Authors: Bin Wang | Wenqing Wang | Jianzhong Wang
Source: SHILAP Revista de lepidopterología (article, 2024-11-01)
Field: Chemistry — Analytical Chemistry

XRD and SEM Characterization and Investigation of Effective Agents of Medicinal Plants and Nanoparticles: Machine Learning (ML) Analysis This study focuses on the characterization and investigation of effective agents in medicinal plants and nanoparticles, aiming to understand their potential applications. X-Ray Diffraction (XRD) and Scanning Electron Microscopy (SEM) techniques were employed to analyze the structural and morphological properties of the samples. XRD provided valuable information on crystalline phases, crystal structure, and lattice parameters, while SEM revealed surface morphology, particle size distribution, and aggregation behavior. These techniques facilitated a comprehensive understanding of the physical and chemical properties, crucial for effective utilization. Machine Learning (ML) analysis was employed to uncover patterns and correlations within the data. ML algorithms were used to identify significant features, establish predictive models, and gain insights into the relationships between sample properties and effective agents. This enhanced understanding of the factors influencing efficacy, paving the way for targeted applications. The study encompassed two main research areas. Firstly, a ML was developed to estimate Z, P>|Z|, and the 95% confidence interval by manipulating coefficients (COEF) and robust standard errors (ROBUST STD.ERR) in wider intervals compared to the experimental samples. The study revealed a direct relationship between coefficients and robust standard errors, with increasing coefficients leading to higher robust standard errors and an expanded 95% confidence interval. Additionally, the study emphasized the significance of income from Chinese medicinal materials in the financing process for growers, as income variations impacted their willingness to finance technology adoption. By exploring the connection between technology adoption and financing, the research aimed to enhance understanding and logical linkage, contributing to more effective and sustainable agricultural development. Characterization (materials science) ; Confidence interval ; Scanning electron microscope ; Process (computing) ; Supervised learning ; Classifier (UML) ; Sample (material) ; Principal component analysis Heavy Metals in Plants ; Phytochemistry and biological activities of Ficus species ; Spectroscopy and Chemometric Analyses
Authors: Jae Joon Kim | Eung-Seon Kim | Hyangsuk Seong | Ho Jin Ryu
Source: SSRN Electronic Journal (preprint, 2024-01-01)
Field: Materials Science — Materials Chemistry

Diffusion Coefficients Calculations of 110mag in Zrc at Very High Temperature Using Machine-Learning Interatomic Potential Diffusion ; Materials science ; Interatomic potential ; Thermodynamics ; Condensed matter physics ; Statistical physics ; Physics ; Chemistry ; Computational chemistry ; Molecular dynamics Nuclear Materials and Properties ; Fusion materials and technologies ; Thermodynamic and Structural Properties of Metals and Alloys
Authors: Zied Abdelkefi | Dalia Fendri | Soulaimen Smaoui | Hassen Cheffi | Bassem Ben Hamed | Mourad Fakhfakh
Source:  (article, 2024-05-16)
Field: Materials Science — Materials Chemistry

Predicting Adiabatic Temperature Changes in Electrocaloric Ceramics for Cooling Electronic Devices Through Machine Learning Materials with the Electrocaloric effect constitute a promising candidate for new solid-state refrigeration techniques. This effect refers to the adiabatic temperature change (ΔT), which can play an important role in cooling electronic devices. Typically, to estimate ΔT of new materials without characterization, Hamiltonian approaches or Landau theory can be employed. However, these methods are computationally intensive and require many parameters, prompting the use of artificial intelligence techniques as an alternative approach. In this regard, we employ a data-driven machine learning approach to predict the adiabatic temperature change (ΔT) in BaTiO3-based ceramics. The data, extracted from a survey of published electrocaloric measurements in previous studies, represents the evolution of ΔT as a function of chemical composition, temperature, applied electric field, and other features. Several models have been investigated to predict ΔT, and computational results suggest that the Decision Trees model is slightly better than the Support Vector Machines model and significantly outperforms the Ridge Regression model. Adiabatic process ; Materials science ; Ceramic ; Electrocaloric effect ; Optoelectronics ; Engineering physics ; Thermodynamics ; Composite material ; Engineering ; Physics ; Dielectric ; Ferroelectricity Ferroelectric and Piezoelectric Materials ; Multiferroics and related materials ; Magnetic and transport properties of perovskites and related materials
Authors: Junfeng Gao | Luneng Zhao | Hongsheng Liu | Yuan Chang | Xiaoran Shi | Jijun Zhao | Feng Ding
Source: Research Square (preprint, 2024-12-04)
Field: Materials Science — Materials Chemistry

Intermediates of Forming Transition Metal Dichalcogenides Heterostructures Revealed by Machine Learning Simulations Transition metal ; Heterojunction ; Materials science ; Schottky barrier ; Chemical vapor deposition ; Wafer ; Metal ; Atom (system on chip) ; Nanotechnology ; Optoelectronics ; Chemistry ; Metallurgy ; Computer science ; Diode ; Catalysis 2D Materials and Applications ; Machine Learning in Materials Science ; Chalcogenide Semiconductor Thin Films
Authors: Haoran Cui | Iyyappa Rajan Panneerselvam | Pranay Chakraborty | Qiong Nian | Yiliang Liao | Yan Wang
Source: arXiv (Cornell University) (preprint, 2024-08-30)
Field: Materials Science — Materials Chemistry

Significantly Enhanced Interfacial Thermal Transport between Single-layer Graphene and Water Through Basal-plane Oxidation Heat transfer between graphene and water is pivotal for various applications, including solarthermal vapor generation and the advanced manufacturing of graphene-based hierarchical structures in solution. In this study, we employ a deep-neural network potential derived from ab initio molecular dynamics to conduct extensive simulations of single-layer graphenewater systems with different levels of oxidation (carbon/oxygen ratio) of the graphene layer. Remarkably, our findings reveal a one-order-of-magnitude enhancement in heat transfer upon oxidizing graphene with hydroxyl or epoxide groups at the graphene surface, underscoring the significant tunability of heat transfer within this system. Given the same oxidation ratio, more dispersed locations of functional groups on graphene surface leads to faster heat dissipation to water. Basal plane ; Graphene ; Basal (medicine) ; Layer (electronics) ; Materials science ; Thermal oxidation ; Thermal ; Nanotechnology ; Chemical engineering ; Chemistry ; Crystallography ; Physics ; Thermodynamics ; Internal medicine ; Engineering ; Medicine Graphene research and applications ; Thermal properties of materials ; Solar-Powered Water Purification Methods
Authors: Jan‐Hendrik Pöhls | Chun-Wan Timothy Lo | Marissa MacIver | Yu‐Chih Tseng | Yurij Mozharivskyj
Source: arXiv (Cornell University) (preprint, 2024-12-06)
Field: Materials Science — Materials Chemistry

Driving Thermoelectric Optimization in AgSbTe2 via Design of Experiments and Machine Learning Systemic optimization of thermoelectric materials is arduous due to their conflicting electrical and thermal properties. A strategy based on Design of Experiments and machine learning is developed to optimize the thermoelectric efficiency of AgSb1+xTe2+y, an established thermoelectric. From eight experiments, high thermoelectric performance in AgSb1.021Te2.04 is revealed with a peak and average thermoelectric figure of merit of 1.61 +/- 0.24 at 600 K and 1.18 +/- 0.18 (300 - 623 K), respectively, which is over 30% higher than the best literature values for AgSb1+xTe2+y. Ag-deficiency and suppression of secondary phases in AgSb1.021Te2.04 improves the electrical properties and reduces the thermal conductivity (~0.4 W m-1 K-1). Our strategy is implemented into an open-source graphical user interface, and it can be used to optimize the methodologies, properties, and processes across different scientific fields. Thermoelectric effect ; Computer science ; Industrial engineering ; Artificial intelligence ; Engineering ; Physics ; Thermodynamics Machine Learning in Materials Science ; Advanced Thermoelectric Materials and Devices ; Innovation Diffusion and Forecasting
Authors: Sayanjit Singha Roy | Ashish Paramane | Jiwanjot Singh
Source:  (article, 2024-08-04)
Field: Materials Science — Materials Chemistry

Automated Space Charge Detection Inside XLPE HVDC Cable Insulation Employing Customized Convolutional Neural Network Space charge accumulations taking place inside the high-voltage direct current (HVDC) polymeric insulation causes significant distortion in electrical field distribution i.e., homo-charges increases the field value past uniform measure and the hetero-charges decrease it. Effect of such unbalanced electrical fields reflect in severe insulation degradation, resulting into reduced service life of the cable and ultimately breakdown if not detected early. Hence, space charge identification is extremely crucial to maintain safe and reliable operations of HVDC cables as well as to increase their service lifespan. Keeping this in mind, primary purpose of the proposed method is to fabricate an indigenous deep learning framework for automated space charge detection and categorization inside HVDC cable insulations. To this end, space charge accumulations inside cross-linked polyethylene (XLPE) insulation were measured and the acquired red-green-blue (RGB)-colored space charge image data were subsequently augmented in size and dimensionally pre-processed. The automated space charge recognition task was accomplished utilizing a light-weight convolutional neural network (CNN) model, whose architecture was custom-designed for the particular space charge classification problem in hand, keeping in mind the objective to perceive optimum accuracy engaging the least computation time. Comparative survey against different classical CNN models reveals persuasive potential of the proposed customized CNN-based methodology for automated space charge detection and categorization purpose, which substantiate its presumable application in real-time HVDC insulation diagnostic scenarios Convolutional neural network ; Space charge ; Electrical engineering ; Computer science ; Engineering ; Electronic engineering ; Artificial intelligence ; Physics High voltage insulation and dielectric phenomena ; Thermal Analysis in Power Transmission ; Electrical Fault Detection and Protection
Authors: Monday Omotioma | Okechukwu Dominic Onukwuli | Obiora-Okafo Ifeoma Amaoge | Friday Nwankwo Archibong | Peace Ugochinyerem Nlemedim
Source: Research Square (preprint, 2024-01-02)
Field: Materials Science — Materials Chemistry

Modeling inhibition efficiency of mango leaf extract for corrosion control of mild steel in HCl solution: Response Surface Methodology and Artificial Neural Network Abstract This study advanced the establishment of natural plant-based inhibitors for corrosion prevention procedures. It entails modelling the efficiency of leaf extract for mild steel corrosion control in HCl solution. The mango leaf extract are characterize to ascertain its molecules/molecular structures using gas chromatography-mass spectrophotometer (GCMS). The efficiency undergo modeling using response surface methodology (RSM) and artificial neural network (ANN). Critical phenomena of the inhibitor’s bio-molecules in the HCl solution and interfacial transition between the molecules and mild steel’s surface are examine using Langmuir, Frumkin, Temkin and Flory-Huggins adsorption isotherms. The results showed that 2-hydroxycyclopentadecanone (C 15 H 28 O 2 ), 4-hepten-3-one (C 8 H 14 O), benzenemethanol (C 9 H 12 O), and 2,7-dimethyloct-7-en-5-yn-4-yl ester (C 14 H 20 O 4 are the predominant molecular constituents (of higher inhibitive properties) in the mango leaf extract. The highest efficiency of 91.42% is obtain at an inhibition concentration of 0.6 g/L, temperature of 318 K and immersion time of 16 hours. Efficiency of the extract are model by optimization tools of RSM and ANN. Based on statistical analyses (correlation coefficient, RMSE and standard error of prediction), ANN performed better than RSM in the prediction of inhibition efficiency of the extract. Interfacial transition between the extract’s molecules and the mild steel surface established. The bio-molecular constituents inhibited the corrosion by process of adsorption. Adsorption ; Response surface methodology ; Corrosion ; Langmuir adsorption model ; Molecule ; Correlation coefficient ; Chemistry ; Artificial neural network ; Sorption isotherm ; Langmuir ; Materials science ; Chromatography ; Nuclear chemistry ; Metallurgy ; Organic chemistry ; Computer science ; Machine learning Corrosion Behavior and Inhibition ; Concrete Corrosion and Durability ; Concrete and Cement Materials Research
Authors: Sulaiman T. Ureiga | Nasser Hajri | Todd J. Green | Muhammad Imran Javed
Source: International Petroleum Technology Conference (article, 2024-02-12)
Field: Materials Science — Materials Chemistry

The Future of Acid Corrosion Inhibitor Testing: A Machine Learning Solution Abstract The paper aims to present a machine learning virtual laboratory as an alternative to autoclave testing, which is typically utilized in determining corrosion inhibitor performance. The virtual lab aids in the optimization of acid corrosion inhibitor loading for stimulation jobs. The lowest acceptable/safe chemical dosage of the inhibitor is the optimal acid corrosion inhibitor loading. Hundreds of physical autoclave test results from a variety of settings were used to develop and test the virtual laboratory's underlying machine learning models. Metal coupons are tested with acid recipes in a laboratory autoclave under simulated wellbore conditions. The weight loss per unit area is measured in the autoclave lab test. The weight loss per unit area is measured in the autoclave lab test. Expected acid exposure period, acid strength or concentration percentage, downhole temperature, corrosion inhibitor type, corrosion inhibitor loading, and steel type are among the parameters that affects the testing. The results of the laboratory tests are categorized into two groups. One set includes 80% of the total autoclave tests which is used to train the machine learning model. The remaining 20% is used in a completely different set designated as the testing set. The testing set is used to validate that machine learning model predictions are accurate. For training and testing, several machine learning models are applied to the same data sets. Field engineers can utilize the machine learning model with the best confidence to design acid stimulation recipes with acceptable corrosion metal loss and low cost. The model can be dynamic, with updated tests being uploaded to the model on a regular basis to train it and improve prediction confidence. Eight machine learning models were trained and then tested through their tests. Each machine learning model tested the sensitivity of testing parameters using four distinct input cases to determine the best machine learning model. The outputs of two machine learning models have a 97.90 percent confidence level. The virtual laboratory will assist oilfield operators in lowering the costs of acidizing treatments while maintaining safe corrosion inhibition limits. In addition, the virtual lab would reduce the requirement for laboratory tests to determine acid formulas for each field job. Corrosion ; Corrosion inhibitor ; Computer science ; Materials science ; Metallurgy Corrosion Behavior and Inhibition ; Hydrogen embrittlement and corrosion behaviors in metals ; Structural Integrity and Reliability Analysis
Authors: Peter B. R. Hartog | Fabian Krüger | Samuel Genheden | Igor V. Tetko
Source: ChemRxiv (preprint, 2024-01-03)
Field: Materials Science — Materials Chemistry

Using test-time augmentation to investigate explainable AI: inconsistencies between method, model and human intuition Stakeholders of machine learning models desire explainable artificial intelligence (XAI) to produce human-understandable and consistent interpretations. In computational toxicity, augmentation of text-based molecular representations has been used successfully for transfer learning on downstream tasks. Augmentations of molecular representations can also be used at inference to compare differences between multiple representations of the same ground-truth. In this study, we investigate the robustness of eight XAI methods using test-time augmentation for a molecular-representation model in the field of computational toxicity prediction. We report significant differences between explanations for different representations of the same ground-truth, and show that randomized models have similar variance. We hypothesize that text-based molecular representations in this and past research reflect tokenization more than learned parameters. Furthermore, we see a greater variance between in-domain predictions than out-of-domain predictions, indicating XAI measures something other than learned parameters. Finally, we investigate the relative importance given to expert-derived structural alerts and find similar importance given irregardless of applicability domain, randomization and varying training procedures. We therefore caution future research to validate their methods using a similar comparison to human intuition without further investigation. Intuition ; Computer science ; Inference ; Artificial intelligence ; Machine learning ; Robustness (evolution) ; Lexical analysis ; Variance (accounting) ; Ground truth ; Natural language processing ; Psychology ; Cognitive science Machine Learning in Materials Science ; Explainable Artificial Intelligence (XAI) ; Computational Drug Discovery Methods
Authors: Shengluo Ma | Yongchao Rao | Xiang Huang | Shenghong Ju
Source: arXiv (Cornell University) (preprint, 2024-04-30)
Field: Materials Science — Materials Chemistry

High-throughput discovery of metal oxides with high thermoelectric performance via interpretable feature engineering on small data In this work, we have proposed a data-driven screening framework combining the interpretable machine learning with high-throughput calculations to identify a series of metal oxides that exhibit both high-temperature tolerance and high power factors. Aiming at the problem of weak generalization ability of small data with power factors at high temperatures, we employ symbolic regression for feature creation which enhances the robustness of the model while preserving the physical meaning of features. 33 candidate metal oxides are finally targeted for high-temperature thermoelectric applications from a pool of 48,694 compounds in the Materials Project database. The Boltzmann transport theory is utilized to perform electrical transport properties calculations at 1,000 K. The relaxation time is approximated by employing constant electron-phonon coupling based on the deformation potential theory. Considering band degeneracy, the electron group velocity is obtained using the momentum matrix element method, yielding 28 materials with power factors greater than 50 $μW cm^{-1} K^{-2} $. The high-throughput framework we proposed is instrumental in the selection of metal oxides for high-temperature thermoelectric applications. Furthermore, our data-driven analysis and transport calculation suggest that metal oxides rich in elements such as cerium (Ce), tin (Sn), and lead (Pb) tend to exhibit high power factors at high temperatures. Throughput ; Feature (linguistics) ; Thermoelectric effect ; Computer science ; Materials science ; Data mining ; Physics ; Telecommunications Machine Learning in Materials Science
Authors: Guanlan Liu | François Ayello | Oleg Shabarchin | Gabriel Langlois-Rahme | Dongliang Lu
Source:  (article, 2024-09-23)
Field: Materials Science — Materials Chemistry

Multiscale Multidisciplinary Machine Learning Modeling for Pipeline Risk Assessment Abstract Ensuring the safety and reliability of pipeline systems across diverse components, materials and operating conditions necessitates the integration of information from various fields of expertise. Machine learning can be a powerful tool to analyze multiscale and multidisciplinary data, enhancing our ability to make informed decisions and manage pipeline risks effectively. Also, machine learning technology, when applied to pipeline risk assessment, can be used to organize valuable pipeline information, and uncover hidden correlations. However, machine learning faces challenges like overfitting and identifying false correlations without a real causal impact. Understanding the causes of pipeline failures is essential for taking appropriate actions. Since data can be imperfect with measurement error, a probabilistic risk prediction model is necessary to incorporate uncertainty considerations. This publication uses a previously published Bayesian network model for Stress Corrosion Cracking (SCC) pipeline risk assessment to demonstrate how Bayesian networks can be used to aggregate data at multiscale and from multidisciplinary factors. The model is reviewed with field data to share knowledge with pipeline engineers using machine learning for pipeline risk assessment. Multidisciplinary approach ; Pipeline (software) ; Computer science ; Artificial intelligence ; Machine learning Material Properties and Failure Mechanisms ; Oil and Gas Production Techniques ; Engineering Diagnostics and Reliability