jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Yanqiu Yang | Paul Heinz Heinemann | Kittiphum Pawikhum | Kari Anne Peter
Source:  (article, 2024-07-28)
Field: Chemistry — Analytical Chemistry

Presymptomatic Detection of Fire Blight in Apple Orchards Using Portable Diffuse Reflectance Spectroscopy: A Machine Learning Approach <b><sc>Abstract.</sc></b> Fire blight, caused by the bacterium Erwinia amylovora, poses a severe threat to apple orchards worldwide, leading to significant production losses and potential tree death. Traditional detection methods are labor-intensive and often imprecise, while molecular techniques, though accurate, require specialized equipment and expertise. This study explores the use of smartphone-assisted spectroscopy combined with machine learning for the early detection of fire blight in apple orchards. Utilizing the K-means clustering algorithm, three distinct treatment groups were identified. Subsequent classification using random forest models achieved high accuracy, 86.89% for the control group, 89.71% for the antibiotic group, and 90.35% for the non-antibiotic group. Feature importance analysis highlighted key spectral wavelengths crucial for the infection status classification. The results demonstrate the potential of spectroscopic techniques for rapid, non-invasive detection of fire blight, suggesting a promising tool for enhancing disease management in agriculture. Future research should focus on optimizing chemical treatments, analyzing data point relationships, and comparing various machine learning models to further improve detection accuracy. Reflectivity ; Diffuse reflectance infrared fourier transform ; Fire blight ; Spectroscopy ; Diffuse reflection ; Remote sensing ; Environmental science ; Computer science ; Artificial intelligence ; Materials science ; Optics ; Chemistry ; Geology ; Physics Spectroscopy and Chemometric Analyses
Authors: I. G Emel yanov | A. N Kislov
Source: PNRPU Mechanics Bulletin (article, 2024-10-03)
Field: Materials Science — Materials Chemistry

THE LIMITING STATE OF A STEEL STRUCTURE UNDER EXTREME THERMOMECHANICAL LOADINGS Using numerical methods, the problem of determining the strength and limiting state of a steel shell structure under thermomechanical loading is solved. The operating stresses are determined by solving a physically nonlinear boundary value problem for a shell of revolution. The classical theory of shells, based on the Kirchhoff – Love hypotheses, and the method of integrating shell equations with discrete S.K. Godunov orthogonalization are used. By integrating a system of ordinary differential equations at each point of the shell, the meridional and circumferential stresses and the corresponding deformations are calculated. When taking into account the plastic deformation of the material, the boundary value problem becomes nonlinear. The relationship between stress and strain is linearized by the method of additional strains. A limiting state criterion for thin-walled structures is proposed. In the absence of the necessary parameters for the material of construction, interpolation and extrapolation of the experimental data based on neural networks is used. The method uses the example of a muffle, which is a revolution shell structure loaded with an internal excess pressure of a hydrogen-containing gas and a non-stationary thermal field. The muffle is designed for high-temperature annealing of the electrolytic steel, and is made of non-heat-resistant St3 steel, its mechanical properties have not been sufficiently studied at temperatures above 500 °C. However, the operating temperature of the muffle can reach more than 1000 °C. Under the influence of such a thermal load, noticeable residual deformations are formed in the muffle structure and the muffle may lose its load-bearing capacity. For thermomechanical loads, a maximum temperature of 1000 °C is determined at which the limit state occurs and the operation of the muffle is not permissible. A satisfactory agreement was obtained with the actual muffle temperature during operations of 1100 °C, at which the muffle loses its load-bearing capacity. Limiting ; Materials science ; Structural engineering ; Composite material ; Forensic engineering ; Engineering ; Mechanical engineering Material Properties and Failure Mechanisms ; High-Velocity Impact and Material Behavior ; Metallurgy and Material Forming
Authors: Nitesh Dhar Badgayan | Santosh Kumar Sahu | Avisek Kundu | Seeboli Ghosh Kundu
Source: Data in Brief (article, 2024-10-09)
Field: Materials Science — Materials Chemistry

Dataset on graphite nanoplatelet enhanced HDPE composites: An ensemble machine learning approach estimating the tensile modulus, toughness, and hardness thus creating a roadmap for new product development This article presents a dataset examining the impact of Graphite Nanoplatelets (GNP), injection pressure, and injection temperature on the mechanical properties of High-Density Polyethylene (HDPE) composites. The study uses an ensemble machine learning approach to build a predictive model for response variables: tensile modulus, toughness, and hardness. The model used was a Random Forest regressor, which was robust to outliers and data distribution types. Grid Search CV and Random Search CV were used for hyperparameter tuning, and predictive analysis of model parameters was conducted using both. The response variables were divided into high, medium, and low classes based on the quartile distributions. A Decision Tree classifier was used to derive rules for each class. These methods and the comprehensive dataset and analysis offer a robust foundation for future research in polymer composites. The insights can be leveraged with further research to develop the highest grade of polymer composite products. This might include new applications, improved performance, or cost reduction by optimizing the resources. This research may help in developing a detailed concept for the viable ideas and testing of a new polymer-based product thus bringing a significant impact in the polymer industry as a whole. Materials science ; Composite material ; Ultimate tensile strength ; High-density polyethylene ; Toughness ; Graphite ; Modulus ; Fracture toughness ; Young's modulus ; Research article ; Computer science ; Polyethylene Machine Learning in Materials Science ; Fiber-reinforced polymer composites ; Material Selection and Properties
Authors: J. John Shiny | S Haranya | A Sameeha | P Y Sivanithi
Source:  (article, 2024-12-11)
Field: Chemistry — Analytical Chemistry

Food Adulterant Detection Using Hyperspectral Imaging System in Okra The adulteration of food products in relation to food safety and quality assurance for the project results in serious consequences with regards to consumer health and market integrity. This work attempts to achieve a reliable and rapid means of detecting adulteration in food by use of hyperspectral imaging (HSI) system. Given its potential in capturing and analyzing a wide spectrum of light outside the visible range, hyperspectral imaging becomes a very great tool in identifying and quantifying adulterants not easily detectable with visual or chemical methods. A collection of comprehensive set of hyperspectral images taken from both pure and adulterated okra (lady's finger) samples were processed in the data extraction process of spectral signatures and other important features which will differentiate the adulterated okra from the non-adulterated okra. Advanced image processing techniques such as Principal Component Analysis (PCA) was applied to extract the features, augment by applying noise injection and rotation techniques. In addition to this, superior machine learning algorithms like Conventional Neural Network (CNN) were used for hyperspectral data analysis and classification of the okra samples. The main focus is to develop computational models that could classify the level of okra adulteration with better accuracy and quantify the same. Three CNN models (1D CNN, 2D CNN and 3D CNN) were applied to the samples. It was inferred that 1D CNN model classified the okra samples more accurately with a test accuracy of 0.81 when compared to other models 2D and 3D. Adulterant ; Hyperspectral imaging ; Computer science ; Remote sensing ; Artificial intelligence ; Pattern recognition (psychology) ; Computer vision ; Geology ; Chemistry ; Chromatography Spectroscopy and Chemometric Analyses ; Agricultural Practices and Plant Genetics
Authors: Watanabe Natsuki | Hori Yuta | Sugisawa Hiroki | Ida Tomonori | Shoji Mitsuo | Shigeta Yasuteru
Source: Institutional Repositories DataBase (IRDB) (article, 2024-01-01)
Field: Materials Science — Materials Chemistry

A machine learning potential construction based on radial distribution function sampling Sampling reference data is crucial in machine learning potential (MLP) construction. Inadequate coverage of local configurations in reference data may lead to unphysical behaviors in MLP-based molecular dynamics (MLP-MD) simulations. To address this problem, this study proposes a new on-the-fly reference data sampling method called radial distribution function (RDF)-based data sampling for MLP construction. This method detects and extracts anomalous structures from the trajectories of MLP-MD simulations by focusing on the shapes of RDFs. The detected structures are added to the reference data to improve the accuracy of the MLP. This method allows us to realize a reasonable MLP construction for liquid water with minimal additional data. We prepare data from an H2O molecular cluster system and verify whether the constructed MLPs are practical for bulk water systems. MLP-MD simulations without RDF-based data sampling show unphysical behaviors, such as atomic collisions. In contrast, after applying this method, we obtain MLP-MD trajectories with features, such as RDF shapes and angle distributions, that are comparable to those of ab initio MD simulations. Our simulation results demonstrate that the RDF-based data sampling approach is useful for constructing MLPs that are robust to extrapolations from molecular cluster systems to bulk systems without any specialized know-how. The accuracy of machine learning potentials (MLPs) depends on the quality of training data. MLPs constructed with insufficient data cause unphysical behavior. However, the indiscriminate expansion of the training data is an unwelcome approach from the standpoint of computational cost. This work proposed new training data sampling method, radial distribution function (RDF)-based data sampling, which improves the accuracy of MLPs. The incorporation of new training data via our sampling method led to a notable improvement in the RDFs and other physical properties. Our results demonstrate that this method is useful for constructing MLPs which are robust to extrapolation from molecular cluster system to periodic system. image Sampling (signal processing) ; Function (biology) ; Sampling distribution ; Reference data ; Radial basis function ; Cluster (spacecraft) ; Importance sampling ; Distribution (mathematics) Machine Learning in Materials Science ; Block Copolymer Self-Assembly ; Protein Structure and Dynamics
Authors: Mark H. Griep | Chris Rinderspacher | Daniel Shreiber
Source:  (article, 2024-11-13)
Field: Chemistry — Organic Chemistry

Neural network driven optimization of targeted metasurface colors (Conference Presentation) Tailored sub-wavelength structures of select materials, such as plasmonic metasurfaces and polymeric bragg reflectors, enable strong electromagnetic (EM) interactions that can be tuned to select frequencies across the spectrum. While such systems can be independently tailored towards precise colorimetric outputs in both static and dynamic configurations, new opportunities emerge in hybridized forms that enable more precise spectral control. Optimization of the nanophotonic response was achieved through the use of gaussian process regressions (GPRs) and artificial neural networks (ANNs) to predict colors and spectra of nanoscale metasurface patterns. In addition to the forward model, the inverse functions were also approximated for use in direct design prediction. Spectra were measured, as well as simulated with Lumerical.software Machine-learned models were trained to approximate each respectively. Corresponding experiments were performed to validate and improve the models, isolating metasurface parameters to targeted colorimetric response. Presentation (obstetrics) ; Computer science ; Artificial neural network ; Metamaterial ; Artificial intelligence ; Optoelectronics ; Materials science Polydiacetylene-based materials and applications ; Advanced Computational Techniques in Science and Engineering ; Digital Media and Visual Art
Authors: Oliver N. F. King | Karl Levik | James Sandy | Mark Basham
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2024-05-23)
Field: Materials Science — Materials Chemistry

CHiMP: Deep Learning Tools Trained on Protein Crystallisation Micrographs to Enable Automation of Experiments A group of three deep learning tools, referred to collectively as CHiMP (Crystal Hits in My Plate) were created for analysis of micrographs of protein crystallisation experiments at the Diamond Light Source (DLS) synchrotron, UK. The first tool, a classification network, assigns images into categories relating to experimental outcomes. The other two tools are networks that perform both object detection and instance segmentation, resulting in masks of individual crystals in the first case, and masks of crystallisation droplets in addition to crystals in the second case, allowing positions and sizes of these entities to be recorded. Creation of these tools used transfer learning, where weights from a pre-trained deep learning network were used as a starting point and re-purposed by further training on a relatively small set of data. Two of the tools are now integrated at the VMXi macromolecular crystallography beamline at DLS where they absolve the need for any user input both for monitoring crystallisation experiments and for triggering in situ data collections. The third is being integrated into the XChem fragment-based drug discovery screening platform, also at DLS, to allow automatic targeting of acoustic compound dispensing into crystallisation droplets. Automation ; Artificial intelligence ; Computer science ; Engineering drawing ; Engineering ; Mechanical engineering Enzyme Structure and Function ; Crystallization and Solubility Studies ; Protein Structure and Dynamics
Authors: Meesun Yang | William Basener
Source:  (article, 2024-12-09)
Field: Chemistry — Analytical Chemistry

Analysis of Vegetation Chemistry Using Feature Selection and Learning Methods on Hyperspectral Images The spectrum provided for each pixel in a hyperspectral image provides information about the chemical composition present in the material. The presence of some constituents can be estimated from simple computations from values at specific wavelengths, for example using NDVI to estimate chlorophyll. There are many other molecular and elemental constituents that are essential for understanding vegetation type, health, and function, including water, nitrogen, and carbon, which do not have analogous known wavelength values and formulae. In this paper, we provide a machine learning framework for choosing sets of wavelengths and models for estimating the amounts of these constituents. Using data from the publicly available ECOSTRESS spectral library, 40 regression models, including both linear and nonlinear methods, are evaluated for estimating the constituent concentrations in vegetation. We test each model in conjunction with several feature selection methods, and combined with regularization within some of the models, identify specific wavelengths that are the most useful for quantifying the constituent concentrations, thereby reducing the high dimensionality of this data to a core set of the most critical wavelengths (top bands). Furthermore, our models test both the aggregated vegetation data and data subset by species, and show that while the concentrations can be estimated with accuracy in general on aggregated data, focusing the analysis on individual species can greatly improve accuracy. We also compare results from data collected using an ASD spectrometer with data collected using a Nicolet spectrometer. Implications of these findings are discussed. Hyperspectral imaging ; Feature selection ; Computer science ; Vegetation (pathology) ; Selection (genetic algorithm) ; Feature (linguistics) ; Pattern recognition (psychology) ; Artificial intelligence ; Remote sensing ; Geology Spectroscopy and Chemometric Analyses
Authors: Hugh M J Dorward | David Knowles | Mahmoud Mostafavi | MJ Peel
Source: Bristol Research (University of Bristol) (article, 2024-10-09)
Field: Materials Science — Materials Chemistry

DEVELOPING A REDUCED-ORDER REPRESENTATION OF CRYSTALLOGRAPHIC TEXTURE FOR APPLICATION IN SURROGATE MODELLING AND UNCERTAINTY QUANTIFICATION OF CRYSTAL PLASTICITY MODELS Crystal plasticity modelling is a powerful method for understanding the behaviour of materials at the mesoscale. These models allow prediction of behaviour including deformation, fatigue, damage and creep based on microstructural information about a material including crystallographic texture and grain morphology. This provides numerous opportunities for application in the structural integrity of nuclear plant components. However, the length scale of these models and the computational expense of individual simulations has limited incorporation into macroscopic level component assessment.<br/><br/>Reduced-order modelling is a useful tool which can incorporate machine learning regression algorithms and statistical methods to emulate the response of a complex model. Typically, these models are orders of magnitude faster than the complex models they emulate making tasks involving many model evaluations such as optimisation, sensitivity analysis and uncertainty quantification tractable.<br/><br/>In this study, two reduced-order representations of crystallographic texture are investigated to represent the bulk response of a polycrystal volume element using an approach based on the Taylor factor of a polycrystal and by using principal component analysis (PCA) on the orientation distribution function of the polycrystal. These representations are used as inputs to a Gaussian process regression (GPR) model which is used to predict the macroscopic stress-strain response of the polycrystal for different crystallographic textures. The GPR acts as a surrogate model of the underlying crystal plasticity model and allows an inherent quantification of the model epistemic uncertainty and the uncertainty related to unobserved effects not captured by the texture parameterization.<br/><br/>The GPR model developed also allows uncertainties from the experimental measurement of texture to be propagated through the model which would be prohibitively computationally demanding for a crystal plasticity finite element model. The texture parameterisations investigated can additionally be incorporated into incremental finite element user subroutines providing a data driven tool for bridging material length scales and structure-property linkages Texture (cosmology) ; Uncertainty quantification ; Representation (politics) ; Orientation (vector space) ; Principal component analysis ; Surrogate model ; Component (thermodynamics) ; Gaussian ; Statistical model ; Multiscale modeling Machine Learning in Materials Science ; Model Reduction and Neural Networks ; High Temperature Alloys and Creep
Authors: Daming Sun | Morten M. Smedskjær
Source: VBN Forskningsportal (Aalborg Universitet) (article, 2024-05-20)
Field: Materials Science — Ceramics and Composites

Computational study of fracture in lithium aluminosilicate glass-ceramics Crystals in glass-ceramics can induce crack deflection and diversion to impede crack propagation and consequently improve the fracture toughness compared to that of the parent glass. However, the structural details of these phenomena are not yet fully understood. Here, we simulate the fracture behavior of lithium aluminosilicate glass-ceramics with different crystallinity using molecular dynamics. We analyze the fracture data using a classification-based machine learning method to compute the so-called 'softness' metric. We find that the tendency of Al atoms to undergo bond switching events increases with the increase in the softness of Al atoms, which is manifested by frequent bond switching and rearrangements of Al ions during the fracture process. Cracks tend to expand along the path of the 'soft' Al atom distribution. These results can help guide the future design of optimized microstructures of glass-ceramics with high fracture resistant.<br/> Aluminosilicate ; Lithium (medication) ; Glass-ceramic ; Materials science ; Fracture (geology) ; Composite material ; Ceramic ; Mineralogy ; Chemical engineering ; Geology ; Chemistry ; Medicine ; Organic chemistry ; Engineering ; Internal medicine Glass properties and applications ; Material Science and Thermodynamics ; Recycling and utilization of industrial and municipal waste in materials production
Authors: KY M Diego | Darwin Barayang Putungan | Alexandra B. Santos-Putungan
Source: Journal of Physics Conference Series (article, 2024-07-01)
Field: Materials Science — Materials Chemistry

Predicting the minimum energy pathway of 1H to 1T phase transition of select 2D transition metal dichalcogenides via density functional theory and machine learning approach Abstract This study predicts the minimum energy pathways (MEPs) and transition barriers of two-dimensional transition metal dichalcogenides (TMDs) during their 1H to 1T structural phase transitions. The investigation utilizes density functional theory (DFT) calculations and machine learning algorithms to predict the MEPs and transition barriers. Six TMDs, namely NbSe 2 , ScS 2 , ScSe 2 , TiTe 2 , VS 2 , and VSe 2 , are selected for analysis. The DFT calculations provide reference values for comparison with the machine learning predictions. The transition barriers obtained through DFT calculations range from 0.13 eV to 0.72 eV. The machine learning models, including Linear Regression, Random Forest Regression, K-Nearest Neighbor Regression, and Gaussian Process Regression, demonstrate varying degrees of accuracy in predicting the transition barriers. Gaussian Process Regression achieved the highest accuracy among all ML models, with the absolute mean error spanning all systems being 0.0093. It has been shown that the features that were identified in the study are sufficient for the prediction of the MEPs and transition barriers of TMDs using machine learning. Overall, this study contributes to the understanding of TMD phase transitions and provides insights into the integration of machine learning in materials science research. Density functional theory ; Transition metal ; Phase transition ; Energy transition ; Materials science ; Energy (signal processing) ; Computer science ; Condensed matter physics ; Chemistry ; Computational chemistry ; Physics ; Quantum mechanics 2D Materials and Applications ; Machine Learning in Materials Science ; Graphene research and applications