jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Anis Biswas | Ajay Kumar | Prashant Singh | Tyler Del Rose | Rajiv K. Chouhan | B. C. Margato | B.P. Alho | E.P. Nóbrega | P.J. von Ranke | P.O. Ribeiro | V.S.R. de Sousa | Yaroslav Mudryk
Source: Physical Review Materials (article, 2024-11-21)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Influence of Ga doping on magnetic properties, magnetocaloric effect, and electronic structure of pseudobinary <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:msub><mml:mtext>GdZn</mml:mtext><mml:mrow><mml:mn>1</mml:mn><mml:mo>−</mml:mo><mml:mi>x</mml:mi></mml:mrow></mml:msub><mml:msub><mml:mtext>Ga</mml:mtext><mml:mi>x</mml:mi></mml:msub></mml:mrow></mml:math> (<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:mi>x</mml:mi><mml:mo>=</mml:mo><mml:mn>0</mml:mn><mml:mtext>−</mml:mtext><mml:mn>0.1</mml:mn></mml:mrow></mml:math>) We explore the impact of introducing IIIA-group element Ga in place of IIB-group element Zn in binary intermetallic GdZn on its magnetic and magnetocaloric properties, as well as explicate the modified electronic band structure of the compound. The magnetic transition temperature of the compound decreases with the increase of Ga concentration in ${\mathrm{GdZn}}_{1\ensuremath{-}x}{\mathrm{Ga}}_{x}(x=0--0.1)$ while the crystal structure (CsCl prototype) and lattice parameters remain unchanged. Our detailed analysis of magnetization and magnetocaloric data conclusively proves that long-ranged magnetic ordering exists in the sample, despite the magnetic interaction considerably weakening with the increase of Ga. The experimental data are rationalized using both a theoretical machine-learning model and first-principle density functional theory. The electronic band structure of GdZn is manifested with some unusual complex features which gradually diminish with Ga doping and the conventional sinusoidal feature of Ruderman-Kittel-Kasuya-Yosida-type interactions also disappears. A mean-field theory model is developed and can successfully describe the overall magnetocaloric behavior of the ${\mathrm{GdZn}}_{1\ensuremath{-}x}{\mathrm{Ga}}_{x}$ series of samples. Magnetic refrigeration ; Materials science ; Doping ; Condensed matter physics ; Crystallography ; Magnetization ; Physics ; Optoelectronics ; Quantum mechanics ; Magnetic field Magnetic and transport properties of perovskites and related materials ; Thermal Expansion and Ionic Conductivity ; Rare-earth and actinide compounds
Authors: Mila Jumarlis | Ida Ida | Mirfan Mirfan | Irmawati Imawati | Mardiah Mardiah | Muhammad Faisal | Hairin Anisa
Source: IAES International Journal of Artificial Intelligence (article, 2024-07-17)
Field: Chemistry — Inorganic Chemistry

A hybrid hue saturation lightness, gray level co-occurrence matrix, and k-nearest neighbour for palm-sugar classification &lt;span lang="EN-US"&gt;In recent years, there has been an increasing demand for high-quality raw materials driven by consumers and the food industry. This study aims to build a model to predict the type of &lt;/span&gt;&lt;span lang="IN"&gt;palm&lt;/span&gt;&lt;span lang="EN-US"&gt; sugar using a hybrid method of hue&lt;/span&gt;&lt;span lang="IN"&gt;-&lt;/span&gt;&lt;span lang="EN-US"&gt;saturation&lt;/span&gt;&lt;span lang="IN"&gt;-l&lt;/span&gt;&lt;span lang="EN-US"&gt;ightness (HSL), gray level co-occurrence matrix (GLCM), and K-nearest neighbor (KNN). The price of palm sugar is determined based on the type and ingredients used. However, due to the lack of public knowledge in distinguishing the types of &lt;/span&gt;&lt;span lang="IN"&gt;palm&lt;/span&gt;&lt;span lang="EN-US"&gt; sugar, there is the potential for price manipulation that can harm the community. The accuracy rate of 9&lt;/span&gt;&lt;span lang="IN"&gt;7.6&lt;/span&gt;&lt;span lang="EN-US"&gt;% of the palm sugar type prediction results shows that the model that was built has worked very well. The results have practical implications, such as developing automated systems to classify palm species in specific industries to benefit economics and operational efficiency. Future research directions may explore the integration of advanced machine-learning techniques and real-time image processing for further improving classification performance and scalability in industrial applications&lt;/span&gt;&lt;span lang="IN"&gt;.&lt;/span&gt; Span (engineering) ; Sugar ; Hue ; Life span ; Mathematics ; Artificial intelligence ; Computer science ; Structural engineering ; Engineering ; Food science ; Biology ; Medicine ; Gerontology Coconut Research and Applications ; Agricultural and Environmental Management ; Spectroscopy and Chemometric Analyses
Authors: Camilo Augusto Fernandes Salvador | Jessica S. Rego | Thiago Lopes | Júlio Romano Meneghini | Nigel P. Brandon | Daniela Andrade Damasceno
Source: Ceramics International (article, 2024-11-30)
Field: Materials Science — Materials Chemistry

Materials selection of non-metallic glasses for planar solid-oxide fuel cell sealants Materials science ; Planar ; Oxide ; Solid oxide fuel cell ; Fuel cells ; Metal ; Selection (genetic algorithm) ; Composite material ; Metallurgy ; Chemical engineering ; Physical chemistry ; Electrode Advancements in Solid Oxide Fuel Cells ; Catalysis and Oxidation Reactions ; Catalytic Processes in Materials Science
Authors: S. Hussain | David J. Lary | Kentaro Hara | Kallol Bera | Shahid Rauf | Matthew Goeckner
Source: Journal of Vacuum Science & Technology A Vacuum Surfaces and Films (article, 2024-05-14)
Field: Chemistry — Spectroscopy

Case study in machine learning for predicting moderate pressure plasma behavior Modeling and forecasting the dynamics of complex systems, such as moderate pressure capacitively coupled plasma (CCP) systems, remains a challenge due to the interactions of physical and chemical processes across multiple scales. Historically, optimization for a given application would be accomplished via a design of experiment (DOE) study across the various external control parameters. Machine learning (ML) techniques show the potential to “forecast” process conditions not tested in a traditional DOE study and thereby allow better optimization and control of a plasma tool. In this article, we have used standard DOE as well as ML predictions to analyze I-V data in a moderate-pressure CCP system. We have demonstrated that supervised regression ML techniques can be a useful tool for extrapolating data even when a plasma system is undergoing a transition in the heating mode, in this case from the alpha to gamma mode. Classification analysis of control parameters is another possible application of ML techniques that can be deployed for system control. Here, we show that given a large set of measured data, the models can identify the gas ratio in the feed gas as well as correctly identify the operating pressure and electrode gap in almost all the cases. Design of experiments ; Process (computing) ; Computer science ; System dynamics ; Set (abstract data type) ; Machine learning ; Artificial intelligence ; Control (management) ; Mode (computer interface) ; Mathematics ; Statistics Mass Spectrometry Techniques and Applications ; Plasma Diagnostics and Applications ; Electrohydrodynamics and Fluid Dynamics
Authors: Marco Lukas | Sebastian Leineweber | Birger Reitz | Ludger Overmeyer | Alexander Aschemann | Benjamin Klie | Ulrich Giese
Source: Rubber Chemistry and Technology (article, 2024-05-08)
Field: Chemistry — Analytical Chemistry

MINIMIZING TEMPERATURE DEVIATIONS IN RUBBER MIXING PROCESS BY USING ARTIFICIAL NEURAL NETWORKS ABSTRACT Rubber mixing is a complex manufacturing process that poses challenges for process control due to the high number of control variables, including mixing parameter settings, rheological behavior, compound viscosity, and batch-dependent material variations. Already small deviations from the control variables can influence the compound properties, leading to increased scrap rates. To address these challenges, this paper introduces an artificial intelligence–based approach to enhance process control in rubber mixing by predicting mixing temperatures from input variables. The proposed method uses feedforward neural networks (FFNs) to enable early identification of batch-specific temperature deviations, thereby enabling systematic improvements with each new application. The FFN was trained on a diverse dataset encompassing various rubber recipes and batches. Post-training, the FFN demonstrated remarkable accuracy, achieving a mean absolute percentage error of 1.00% on the training dataset and 1.44% on the validation dataset, thereby showcasing its efficacy in predicting temperature fluctuations within the mixing process. Consequently, the FFN can determine the relevant input variables necessary to achieve specific mixing temperatures, providing a foundation for an automated control system in rubber mixing process. This paper outlines the system architecture of the FFN tailored for rubber mixing and provides a comprehensive overview of the experimental results. Mixing (physics) ; Natural rubber ; Artificial neural network ; Process (computing) ; Feed forward ; Computer science ; Process engineering ; Process variable ; Process control ; Scrap ; Statistical process control ; Materials science ; Biological system ; Artificial intelligence ; Engineering ; Control engineering ; Mechanical engineering ; Composite material ; Physics Petroleum Processing and Analysis ; Enhanced Oil Recovery Techniques ; Rheology and Fluid Dynamics Studies
Authors: Vipin Kurian Elavunkel | Prahallad Padhan
Source: Advanced Theory and Simulations (article, 2024-10-21)
Field: Materials Science — Materials Chemistry

Assessment of Thermoelectric Properties of Bi<sub>2</sub>Se<sub>3</sub>: Insights from Hybrid Functional Studies, Strain Engineering, and Machine Learning Methodology Abstract Thermoelectric properties in topological insulator Bi 2 Se 3 are explored with multifaceted strategies, i.e., hybrid functional with strain and artificial intelligence methodology. The assessment with the experimental band gap values recognizes the limitations of conventional functional and the effectiveness of screened hybrid functionals. A thorough investigation into the impact of biaxial and uniaxial strain on thermoelectric parameters uncovers distinctive behaviors in n‐type and p‐type Bi 2 Se 3 , providing insights into optimal strain conditions for improved performance. Furthermore, the studies on the role of topologically non‐trivial surface states (TNSS) in thermoelectric properties reveal that TNSS significantly dominate electronic transport. Dual scattering time approximation elucidates the segregation of thermoelectric transport contributions from bulk and surface states, highlighting the importance of controlling the relaxation time ratio for enhanced thermoelectric performance. Additionally, the prediction of thermoelectric properties using Random Forest and Neural Networks models showcase impressive agreement with density functional theory predictions across varying temperatures, offering a powerful tool for understanding complex temperature‐dependent trends in thermoelectric properties. In summary, this interdisciplinary study presents a unique approach to advancing the understanding and optimization of thermoelectric properties in Bi 2 Se 3 . It provides a comprehensive framework for tailoring material behavior for diverse thermoelectric applications. Thermoelectric effect ; Strain (injury) ; Materials science ; Engineering physics ; Computer science ; Engineering ; Physics ; Thermodynamics ; Medicine ; Physical therapy Advanced Thermoelectric Materials and Devices ; Machine Learning in Materials Science ; Topological Materials and Phenomena
Authors: Xinjun Hu | Jiahong Zhang | Lei Yu | Jianping Tian | Jianheng Peng | Man Chen
Source: Journal of Food Safety (article, 2024-09-19)
Field: Chemistry — Analytical Chemistry

Classification of Pesticide Residues in Sorghum Based on Hyperspectral and Gradient Boosting Decision Trees ABSTRACT To address the challenges posed by chemical methods for detecting pesticide residues in sorghum, such as complicated sample preparation and prolonged detection periods, this study presents a rapid and nondestructive detection approach based on hyperspectral imaging (HSI) technology. A group of sorghum without pesticide residues and three groups uniformly sprayed with pesticides were used in this study. Firstly, support vector machine (SVM) classification models were built using spectral data preprocessed with Savitzky–Golay (SG), discrete wavelet transform (DWT), and standard normal variate (SNV) methods, respectively, and SNV was determined to be the best preprocessing method. Secondly, the gradient boosting decision tree (GBDT) algorithm, principal component analysis (PCA), and the successive projections algorithm (SPA) were respectively used to extract feature wavelengths. Pesticide residue identification models based on full and feature wavelengths were then respectively established using backpropagation neural network (BPNN), SVM, and partial least squares discriminant analysis (PLS‐DA). The results show that the BPNN model developed using the feature wavelengths obtained from GBDT was the best for identification of pesticide residues, with an accuracy of 97.8% for both the training and testing sets. Finally, visualization of pesticide residue species in sorghum was achieved using the optimal model. This study demonstrates that utilizing HSI in conjunction with the GBDT‐BPNN model is an effective, rapid, and nondestructive method for identifying pesticide residues in sorghum. Hyperspectral imaging ; Boosting (machine learning) ; Sorghum ; Pesticide residue ; Decision tree ; Gradient boosting ; Pesticide ; Environmental science ; Artificial intelligence ; Computer science ; Biology ; Agronomy ; Random forest Spectroscopy and Chemometric Analyses ; Water Quality Monitoring and Analysis ; Pesticide Residue Analysis and Safety
Authors: Taoreed O. Owolabi | Fawaz S. Alharbi | Sami M. Ibn Shamsah | Usman Taura | Abdulrahman M. Alshaheen | Akinyemi C. Tomiwa
Source: Materials Today Sustainability (article, 2024-07-20)
Field: Materials Science — Materials Chemistry

Modeling thermoelectric performance of doped BiCuSeO oxychalcogenide ceramics using genetically hybridized support vector regression computational method Materials science ; Doping ; Ceramic ; Thermoelectric effect ; Composite material ; Optoelectronics ; Physics ; Thermodynamics Advanced Thermoelectric Materials and Devices ; Chalcogenide Semiconductor Thin Films ; Magnetic and transport properties of perovskites and related materials
Authors: Aaron D. Danilack | Callum J. Dickson | Cihan Soylu | Mike Fortunato | Stephane Rodde | Hagen Munkler | Viktor Horn̆ák | José S. Duca
Source: Journal of Computer-Aided Molecular Design (article, 2024-05-01)
Field: Chemistry — Organic Chemistry

Reactivities of acrylamide warheads toward cysteine targets: a QM/ML approach to covalent inhibitor design Covalent bond ; QM/MM ; Cysteine ; Chemistry ; Acrylamide ; Organic chemistry ; Molecule ; Enzyme ; Polymer Click Chemistry and Applications ; Peptidase Inhibition and Analysis ; Ferrocene Chemistry and Applications
Authors: Chitra Sabapathy Ranganathan | Arshadh Ariff Mohamed Abuthahir Riazulhameed | Soorya Saravanan | N. Mohankumar | M. Rajmohan | S. Murugan
Source:  (article, 2024-10-24)
Field: Materials Science — Polymers and Plastics

IoT Based Real-Time Moisture Management in Fabric Drying Processes Ensuring product quality and production efficiency in textile manufacturing operations is greatly dependent on appropriate moisture control. There have been inefficiencies and less-than-ideal results from using traditional techniques to monitor and manage textile moisture levels since they do not have real-time capabilities. It provides a new method for controlling and monitoring fabric drying processes in real-time by combining the K-Nearest Neighbors (KNN) algorithm with Internet of Things (IoT) technology. Using the IoT infrastructure, sensors implanted in the fabric can continuously and accurately monitor the amount of moisture while the fabric dries. The KNN algorithm makes use of similarities across moisture profiles to forecast the best drying settings after processing and analyzing the data. Our method improves textile moisture management via experiments and validation. With the help of the IoT, drying settings may be dynamically adjusted in reaction to changes in moisture levels in real time, which improves process control and efficiency. Reducing drying times and energy usage without sacrificing product quality is made possible by the proactive decision-making made possible by the predictive capabilities of the KNN algorithm. To optimize fabric drying processes it proposes a system that combines IoT technology and machine learning algorithms in a synergistic way. Sustainable, productive, and high-quality textile production processes may be achieved in real-time with the help of the proposed method. Moisture ; Computer science ; Internet of Things ; Environmental science ; Process engineering ; Materials science ; Embedded system ; Composite material ; Engineering Textile materials and evaluations
Authors: Young-Jae Choi | Minjae Ghim | Seung-Hoon Jhi
Source: Physical Review Materials (article, 2024-01-24)
Field: Materials Science — Materials Chemistry

Study of vacancy ordering and the boson peak in metastable cubic Ge-Sb-Te using machine learning potentials The mechanism of the vacancy ordering in metastable cubic Ge-Sb-Te (c-GST) that underlies the ultrafast phase-change dynamics and prominent thermoelectric properties remains elusive. Achieving a comprehensive understanding of the vacancy-ordering process at an atomic level is challenging because of enormous computational demands required to simulate disordered structures on large temporal and spatial scales. In this study, we investigate the vacancy ordering in c-GST by performing large-scale molecular dynamics simulations using machine learning potentials. The initial c-GST structure with randomly distributed vacancies rearranges to develop a semiordered cubic structure with layerlike ordered vacancies after annealing at 700 K for 100 ns. The vacancy ordering significantly affects the lattice dynamical properties of c-GST. In the initial structure with fully disordered vacancies, we observe a boson peak, usually associated with amorphous solids, that consists of localized modes at \ensuremath{\sim}0.575 THz. The boson peak modes are highly localized around specific atomic arrangements of straight vacancy-Te-vacancy trios. As vacancies become ordered, the boson peak disappears, and the Debye-Waller thermal $B$ factor of Te decreases substantially. This finding indicates that the c-GST undergoes a transition from amorphous-like to crystalline-like solid state by thermal annealing in low-frequency dynamics. Metastability ; Materials science ; Vacancy defect ; Condensed matter physics ; Boson ; Crystallography ; Quantum mechanics ; Physics Phase-change materials and chalcogenides ; Advanced Thermoelectric Materials and Devices ; Chalcogenide Semiconductor Thin Films
Authors: Pradeep Mulabagal | A. Kumaraswamy | Mithilesh Kumar Dewangan | Sunil Nimje
Source: International Journal on Interactive Design and Manufacturing (IJIDeM) (article, 2024-05-30)
Field: Materials Science — Materials Chemistry

A hybrid approach for predicting impact characteristics of stainless steel conical fragments: finite element simulation and artificial neural network modelling Artificial neural network ; Finite element method ; Conical surface ; Structural engineering ; Computer science ; Materials science ; Artificial intelligence ; Engineering ; Mechanical engineering High-Velocity Impact and Material Behavior ; Nuclear Materials and Properties ; Material Properties and Failure Mechanisms
Authors: Janeck J. Scott‐Fordsmand | Susana I.L. Gomes | Suman Pokhrel | Lutz Mädler | Matteo Fasano | Pietro Asinari | Kaido Tämm | Jaak Jänes | Mónica J.B. Amorim
Source: ACS Applied Materials & Interfaces (article, 2024-08-01)
Field: Chemistry — Electrochemistry

Machine Learning Allowed Interpreting Toxicity of a Fe-Doped CuO NM Library Large Data Set─An Environmental In Vivo Case Study The wide variation of nanomaterial (NM) characters (size, shape, and properties) and the related impacts on living organisms make it virtually impossible to assess their safety; the need for modeling has been urged for long. We here investigate the custom-designed 1-10% Fe-doped CuO NM library. Effects were assessed using the soil ecotoxicology model <i>Enchytraeus crypticus</i> (Oligochaeta) in the standard 21 days plus its extension (49 days). Results showed that 10%Fe-CuO was the most toxic (21 days reproduction EC50 = 650 mg NM/kg soil) and Fe<sub>3</sub>O<sub>4</sub> NM was the least toxic (no effects up to 3200 mg NM/kg soil). All other NMs caused similar effects to <i>E. crypticus</i> (21 days reproduction EC50 ranging from 875 to 1923 mg NM/kg soil, with overlapping confidence intervals). Aiming to identify the key NM characteristics responsible for the toxicity, machine learning (ML) modeling was used to analyze the large data set [9 NMs, 68 descriptors, 6 concentrations, 2 exposure times (21 and 49 days), 2 endpoints (survival and reproduction)]. ML allowed us to separate experimental related parameters (e.g., zeta potential) from particle-specific descriptors (e.g., force vectors) for the best identification of important descriptors. We observed that concentration-dependent descriptors (environmental parameters, e.g., zeta potential) were the most important under standard test duration (21 day) but not for longer exposure (closer representation of real-world conditions). In the longer exposure (49 days), the particle-specific descriptors were more important than the concentration-dependent parameters. The longer-term exposure showed that the steepness of the concentration-response decreased with an increased Fe content in the NMs. Longer-term exposure should be a requirement in the hazard assessment of NMs in addition to the standard in OECD guidelines for chemicals. The progress toward ML analysis is desirable given its need for such large data sets and significant power to link NM descriptors to effects in animals. This is beyond the current univariate and concentration-response modeling analysis. Materials science ; In vivo ; Doping ; Toxicity ; Set (abstract data type) ; Data set ; Nanotechnology ; Computer science ; Artificial intelligence ; Optoelectronics ; Biology ; Biotechnology ; Chemistry Electrochemical Analysis and Applications ; Water Quality Monitoring and Analysis
Authors: Jinfang Ma | Fengjie Xu | Xue Zhou | Hai Tao Yin | Hui Wang | Caiyun Wang | Jiaze Chen | Baiheng Xie | Fahuan Ge | Furong Huang
Source: Journal of Food Composition and Analysis (article, 2024-06-26)
Field: Chemistry — Analytical Chemistry

Quality assessment of Gastrodia elata Blume (Tianma) based on Vis-NIR spectroscopy: Discrimination of harvest times and prediction of quality indicator contents Gastrodia elata ; Quality assessment ; Quality (philosophy) ; Traditional medicine ; Food science ; Biology ; Medicine ; Business ; Physics ; Marketing Spectroscopy and Chemometric Analyses ; Biological and pharmacological studies of plants ; Spectroscopy Techniques in Biomedical and Chemical Research