jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Yang Han | Fuheng Qu | Yong Yang | Xiaofeng Li | Ping Wang | Sike Guo | Lu Wang
Source: Sensors (article, 2024-07-17)
Field: Chemistry — Analytical Chemistry

Study on the Determination of Flavor Value of Rice Based on Grid Iterative Search Swarm Optimization Support Vector Machine Model and Hyperspectral Imaging In the field of rice processing and cultivation, it is crucial to adopt efficient, rapid and user-friendly techniques to detect the flavor values of various rice varieties. The conventional methods for flavor value assessment mainly rely on chemical analysis and technical evaluation, which not only deplete the rice resources but also incur significant time and labor costs. In this study, hyperspectral imaging technology was utilized in combination with an improved Particle Swarm Optimization Support Vector Machine (PSO-SVM) algorithm, i.e., the Grid Iterative Search Particle Swarm Optimization Support Vector Machine (GISPSO-SVM) algorithm, introducing a new non-destructive technique to determine the flavor value of rice. The method captures the hyperspectral feature data of different rice varieties through image acquisition, preprocessing and feature extraction, and then uses these features to train a model using an optimized machine learning algorithm. The results show that the introduction of GIS algorithms in a PSO-optimized SVM is very effective and can improve the parameter finding ability. In terms of flavor value prediction accuracy, the Principal Component Analysis (PCA) combined with the GISPSO-SVM algorithm achieved 96% accuracy, which was higher than the 93% of the Competitive Adaptive Weighted Sampling (CARS) algorithm. And the introduction of the GIS algorithm in different feature selection can improve the accuracy to different degrees. This novel approach helps to evaluate the flavor values of new rice varieties non-destructively and provides a new perspective for future rice flavor value detection methods. Hyperspectral imaging ; Support vector machine ; Particle swarm optimization ; Swarm behaviour ; Computer science ; Pattern recognition (psychology) ; Hyperparameter optimization ; Preprocessor ; Artificial intelligence ; Grid ; Feature (linguistics) ; Flavor ; Feature selection ; Data mining ; Algorithm ; Mathematics Spectroscopy and Chemometric Analyses ; GABA and Rice Research ; Advanced Chemical Sensor Technologies
Authors: Xiang Xu | Xi Zhang | Erik Bitzek | Siegfried Schmauder | Blazej Grabowski
Source: arXiv (Cornell University) (preprint, 2024-06-07)
Field: Materials Science — Materials Chemistry

Origin of the yield stress anomaly in L12 intermetallics unveiled with physically-informed machine-learning potentials The yield stress anomaly of L12 intermetallics such as Ni3Al or Ni3Ga is controlled by the so-called Kear-Wilsdorf lock (KWL), of which the formation and unlocking are governed by dislocation cross-slip. Despite the importance of L12 intermetallics for strengthening Ni-based superalloys, microscopic understanding of the KWL is limited. Here, molecular dynamics simulations are conducted by employing a dedicated machine-learning interatomic potential derived via physically-informed active-learning. The potential facilitates modelling of the dislocation behavior in Ni3Al with near ab initio accuracy. KWL formation and unlocking are observed and analyzed. The unlocking stress demonstrates a pronounced temperature dependence, contradicting the assumptions of existing analytical models. A phenomenological model is proposed to effectively describe the atomistic unlocking stresses and extrapolate them to the macroscopic scale. The model is general and applicable to other L12 intermetallics. The acquired knowledge of KWLs provides a deeper understanding on the origin of the yield stress anomaly. Yield (engineering) ; Anomaly (physics) ; Intermetallic ; Stress (linguistics) ; Psychology ; Artificial intelligence ; Materials science ; Philosophy ; Condensed matter physics ; Computer science ; Physics ; Metallurgy Machine Learning in Materials Science ; Advanced Materials Characterization Techniques ; Intermetallics and Advanced Alloy Properties
Authors: Anton Golovin | Ilya Pozhidaev | Ivan V. Baskakov | Vladimir Orobinsky | Arkady Khimchenko
Source: SCIENCE IN THE CENTRAL RUSSIA (article, 2024-02-29)
Field: Chemistry — Analytical Chemistry

THEORETICAL PREREQUISITES FOR DETERMINING THE SHAPE AND SIZE OF THE OZONE DISTRIBUTION CENTER IN A GRAIN HEAP It is known that at the present stage of development of the agro-industrial complex, the ozonation process is increasingly being used. This is an environmentally friendly method of processing agricultural raw materials in order to disinfect, deodorize, disinsect, increase the growth qualities of seeds, etc. It is worth noting that, despite such an active use of ozone, the question remains not fully understood how the gas spreads in the grain during its processing. The article presents the results of research to determine the shape and size of the source of the ozone-air mixture divergence in the grain heap. It has been experimentally established that the ozone in the grain diverges in a shape close to an ellipsoid. The size of the lesion depends on the saturation time and, as a result, the received dose of ozone treatment. As the duration of the experiment increases, the radius of the ellipsoid increases more than its height. Based on the results of experimental studies in the Matlab program, a model of the behavior of ozone in grain is constructed depending on its processing modes. Using regressions based on Gauss processes, the shape and size of the distribution center of the ozone-air mixture in the grain were predicted with a change in the ozonation time. The selection of model parameters is based on Bayesian optimization in the Regression Learner application from the Statistics and Machine Learning Toolbox of the Matlab application software package. The electronic program allows you to optimize hyperparameters and obtain confidence intervals for predicted values. The research results make it possible to predict the behavior of ozone in grain, which is necessary to determine the number of gas supply nozzles inside a granary of any volume. Ozone ; MATLAB ; Ozonide ; Ellipsoid ; Grain size ; Mathematics ; Process engineering ; Environmental science ; Statistics ; Computer science ; Biological system ; Engineering ; Materials science ; Meteorology ; Physics Spectroscopy and Chemometric Analyses ; Animal Nutrition and Health ; Advanced Scientific Research Methods
Authors: Ahmet Gürkan Yüksek | Sabit Horoz | İlkay Demir | İsmail Altuntaş | Ebru Şenadım Tüzemen
Source: Physica B Condensed Matter (article, 2024-09-27)
Field: Materials Science — Materials Chemistry

Characterization of SiNx grown at different nitrogen flow and prediction of refractive index using artificial neural networks Artificial neural network ; Characterization (materials science) ; Refractive index ; Index (typography) ; Materials science ; Flow (mathematics) ; Nitrogen ; Biological system ; Computer science ; Artificial intelligence ; Nanotechnology ; Optoelectronics ; Mechanics ; Physics Silicon Nanostructures and Photoluminescence ; Thin-Film Transistor Technologies ; Surface Roughness and Optical Measurements
Authors: Houssam Chouaib | V. Dimastrodonato | Anderson Chou | Agostino Cangianoa | Andrew Cross | Derrick Shaughnessy | Zhengquan Tan | Daniel Schmidt | Curtis Durfee | S. Pancharatnam | Julien Frougier | Andrew S. Greene | Mary Breton
Source:  (article, 2024-04-10)
Field: Materials Science — Surfaces, Coatings and Films

Novel ellipsometry metrology-based machine learning technique for low sensitivity characterization of critical dimensions within gate-all-around transistors The semiconductor industry has witnessed a fast progression of spectroscopic ellipsometry (SE) techniques aimed at resolving a plethora of complex device characterizations on a nanometric scale. The Mueller Matrix (MM) methodology coupled with rigorous coupled-wave analysis (RCWA) has offered an unprecedented power of investigation and analysis of diverse critical dimensions (CDs), especially when applied to gate-all-around (GAA) structures, as it helps increase the useful spectral signals of the often geometrically buried CDs. However, the sensitivity to the CDs can be often screened by other parameters, hampering the precision and accuracy of the measurement. Combining the most sensitive MM elements has therefore become a critical step of scatterometry critical dimension (SCD) metrology. Driven by the rapid developments of Machine Learning (ML) algorithms, we propose a versatile ellipsometry methodology that overcomes poor sensitivity and increases accuracy through a novel principal component analysis (PCA) method of the ML training algorithm with RCWA assistance. Furthermore, our methodology introduces a new ML training concept based on reference data statistics, rather than raw reference. Our approach has been validated with reference data and proved successful in monitoring GAA sheet-specific indent. The proposed methodology paves the way to measuring low sensitivity CDs with highly accurate, noise-reduced and robust ML-based physical SCD models for any logic and memory application. Critical dimension ; Metrology ; Sensitivity (control systems) ; Computer science ; Dimensionality reduction ; Transistor ; Ellipsometry ; Dimension (graph theory) ; Principal component analysis ; Algorithm ; Electronic engineering ; Materials science ; Machine learning ; Artificial intelligence ; Nanotechnology ; Optics ; Physics ; Engineering ; Electrical engineering ; Mathematics ; Thin film Optical Coatings and Gratings ; Near-Field Optical Microscopy ; Surface Roughness and Optical Measurements
Authors: Changtai Li | Xu Han | Chao Yao | Xiaojuan Ban
Source: arXiv (Cornell University) (preprint, 2024-01-11)
Field: Materials Science — Materials Chemistry

MatSAM: Efficient Extraction of Microstructures of Materials via Visual Large Model Efficient and accurate extraction of microstructures in micrographs of materials is essential in process optimization and the exploration of structure-property relationships. Deep learning-based image segmentation techniques that rely on manual annotation are laborious and time-consuming and hardly meet the demand for model transferability and generalization on various source images. Segment Anything Model (SAM), a large visual model with powerful deep feature representation and zero-shot generalization capabilities, has provided new solutions for image segmentation. In this paper, we propose MatSAM, a general and efficient microstructure extraction solution based on SAM. A simple yet effective point-based prompt generation strategy is designed, grounded on the distribution and shape of microstructures. Specifically, in an unsupervised and training-free way, it adaptively generates prompt points for different microscopy images, fuses the centroid points of the coarsely extracted region of interest (ROI) and native grid points, and integrates corresponding post-processing operations for quantitative characterization of microstructures of materials. For common microstructures including grain boundary and multiple phases, MatSAM achieves superior zero-shot segmentation performance to conventional rule-based methods and is even preferable to supervised learning methods evaluated on 16 microscopy datasets whose micrographs are imaged by the optical microscope (OM) and scanning electron microscope (SEM). Especially, on 4 public datasets, MatSAM shows unexpected competitive segmentation performance against their specialist models. We believe that, without the need for human labeling, MatSAM can significantly reduce the cost of quantitative characterization and statistical analysis of extensive microstructures of materials, and thus accelerate the design of new materials. Artificial intelligence ; Computer science ; Segmentation ; Pattern recognition (psychology) ; Centroid ; Generalization ; Computer vision ; Mathematics Machine Learning in Materials Science ; Image Processing Techniques and Applications ; Industrial Vision Systems and Defect Detection
Authors: Yiran Li | Zhonghe Jiang | Guicheng Zhao | Xuanxin Tian | Shigang Ai
Source: Ceramics International (article, 2024-12-17)
Field: Materials Science — Ceramics and Composites

X-ray tomography-assisted modeling of unidirectional mini SiCf/SiC composites considering microstructure defects Materials science ; Microstructure ; Composite material ; Tomography ; X-ray ; Optics Advanced ceramic materials synthesis ; Aluminum Alloys Composites Properties ; Advanced Surface Polishing Techniques
Authors: Steven M. E. Demers | Christopher Sobecki | Larry Deschaine
Source: Nanomaterials (article, 2024-10-30)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Optimization and Multimachine Learning Algorithms to Predict Nanometal Surface Area Transfer Parameters for Gold and Silver Nanoparticles Interactions between gold metallic nanoparticles and molecular dyes have been well described by the nanometal surface energy transfer (NSET) mechanism. However, the expansion and testing of this model for nanoparticles of different metal composition is needed to develop a greater variety of nanosensors for medical and commercial applications. In this study, the NSET formula was slightly modified in the size-dependent dampening constant and skin depth terms to allow for modeling of different metals as well as testing the quenching effects created by variously sized gold, silver, copper, and platinum nanoparticles. Overall, the metal nanoparticles followed more closely the NSET prediction than for Förster resonance energy transfer, though scattering effects began to occur at 20 nm in the nanoparticle diameter. To further improve the NSET theoretical equation, an attempt was made to set a best-fit line of the NSET theoretical equation curve onto the Au and Ag data points. An exhaustive grid search optimizer was applied in the ranges for two variables, 0.1≤C≤2.0 and 0≤α≤4, representing the metal dampening constant and the orientation of donor to the metal surface, respectively. Three different grid searches, starting from coarse (entire range) to finer (narrower range), resulted in more than one million total calculations with values C=2.0 and α=0.0736. The results improved the calculation, but further analysis needed to be conducted in order to find any additional missing physics. With that motivation, two artificial intelligence/machine learning (AI/ML) algorithms, multilayer perception and least absolute shrinkage and selection operator regression, gave a correlation coefficient, R2, greater than 0.97, indicating that the small dataset was not overfitting and was method-independent. This analysis indicates that an investigation is warranted to focus on deeper physics informed machine learning for the NSET equations. Nanoparticle ; Materials science ; Nanosensor ; Colloidal gold ; Silver nanoparticle ; Artificial neural network ; Computer science ; Algorithm ; Nanotechnology ; Biological system ; Machine learning Gold and Silver Nanoparticles Synthesis and Applications ; Quantum Dots Synthesis And Properties ; Plasmonic and Surface Plasmon Research
Authors: Yu Morikawa | Kilho Shin | Masataka Kubouchi | Hiroaki Ohshima
Source: The Journal of Supercomputing (article, 2024-02-27)
Field: Chemistry — Inorganic Chemistry

Prediction of specific surface area of metal–organic frameworks by graph kernels Computer science ; Kernel (algebra) ; Graph ; Similarity (geometry) ; Metal-organic framework ; Porosity ; Surface (topology) ; Artificial intelligence ; Materials science ; Theoretical computer science ; Mathematics ; Chemistry Metal-Organic Frameworks: Synthesis and Applications ; Machine Learning in Materials Science ; X-ray Diffraction in Crystallography
Authors: Ke Xu | Shuo Jin | Guang-Hong Lü
Source: Nuclear Materials and Energy (article, 2024-03-12)
Field: Materials Science — Metals and Alloys

Predicting hydrogen segregation energy distributions in strained regions of tungsten using artificial neural network Employing the smooth overlap of atomic position (SOAP) descriptors, we established an artificial neural network (ANN) model with the ability to effectively and accurately predict the segregation energy Eseg distributions of hydrogen (H) atoms in various strained regions of tungsten (W). The model is verified to have comparable accuracy with the molecular statics (MS) relaxations, as quantitively evidenced by the root mean square error (RMSE) values of 0.018 eV for the homogeneously strained regions, averagely 0.025 eV for regions near different dislocations, and 0.032 eV for regions surrounding an interstitial H cluster. Besides, the predictions yield no systematic bias at both high and low Eseg extremes, which is a common issue in the elastic theory (ET) calculations. Moreover, Eseg values are precisely predicted with the RMSE value of 0.041 eV surrounding an interstitial H cluster that is not encountered during the learning phase of the model, demonstrating the model’s robust generalization capability. Considering its markedly enhanced computation speed, this accurate ANN model holds promising prospects in circumstances that require massive Eseg calculations. Tungsten ; Mean squared error ; Artificial neural network ; Materials science ; Cluster (spacecraft) ; Computation ; Hydrogen ; Root mean square ; Position (finance) ; Yield (engineering) ; Statistical physics ; Energy (signal processing) ; Physics ; Algorithm ; Computer science ; Mathematics ; Artificial intelligence ; Statistics ; Metallurgy Hydrogen embrittlement and corrosion behaviors in metals ; Machine Learning in Materials Science ; Nuclear Materials and Properties
Authors: Ion Anghel | Cătălin Lisa | Silvia Curteanu | Dana Maria Preda | Ioana‐Emilia Şofran | Monica Baia | Malvina Stroe | Mirela Paraschiv | M. Baibarac | Virginia Danciu | Liviu Cosmin Coteț | Lucian Baia
Source: Journal of Thermal Analysis and Calorimetry (article, 2024-02-03)
Field: Materials Science — Polymers and Plastics

The influence of the functionalization of polystyrene and graphene oxide composites on the flammability characteristics: modeling with artificial intelligence tools Flammability ; Polystyrene ; Surface modification ; Materials science ; Graphene ; Oxide ; Composite material ; Nanotechnology ; Chemical engineering ; Polymer ; Engineering Flame retardant materials and properties ; Fire dynamics and safety research ; Environmental and Industrial Safety
Authors: Soraya Moghadamifar | S. Hajir Bahrami | Vahabodin Goodarzi
Source: Journal of the Textile Institute (article, 2024-06-24)
Field: Materials Science — Biomaterials

Optimization and investigation of Physical, chemical, and biological properties of nanofibers produced from (poly glycerol Sebacate-co-caprolactone)/gelatin/peptide and scaffold’s cellular behavior Recently, peptide and Poly(Glycerol Sebacat) PGS-based materials have garnered significant attention in tissue engineering. In this study, we synthesized two novel nanofibrous webs, namely Poly (glycerol sebacate)/gelatin/peptide (PGS/Gel/PT) and Poly (glycerol sebacate co caprolactone)/gelatin/peptide (P(GScoCL)/Gel/PT), utilizing the electrospinning technique with acetic acid as a cost-effective and eco-friendly solvent. To optimize the diameter of the electrospun nanofibrous samples, we employed response surface methodology and artificial neural network, investigating the impact of various electrospinning parameters. Nanofibrous webs with a diameter of 232 nm under the optimized conditions. Nanofibers were characterized using FTIR, DSC, contact angle measurement, and mechanical testing. The biological properties including biodegradability, cell adhesion, MTT assay, and antibacterial efficacy were performed. Nanofibrous samples antibacterial activity, displaying 99% effectiveness against both Gram-positive and Gramnegative bacteria. Moreover, in-vitro studies on the cellular proliferation of human Mesenchymal Stem Cells (hMSCs) nanofiber scaffold demonstrated normal cell morphology and significantly enhanced cell growth after 4 days. Gelatin ; Nanofiber ; Caprolactone ; Scaffold ; Glycerol ; Peptide ; Materials science ; Chemical engineering ; Chemistry ; Nanotechnology ; Organic chemistry ; Biomedical engineering ; Polymer ; Polymerization ; Biochemistry Electrospun Nanofibers in Biomedical Applications ; Silk-based biomaterials and applications ; Bone Tissue Engineering Materials
Authors: Antonios-Dimitrios Stefanou | Xanthippi Zianni
Source: Journal of Physics Condensed Matter (article, 2024-03-08)
Field: Materials Science — Materials Chemistry

Physics mechanisms underlying the optimization of coherent heat transfer across width-modulated nanowaveguides with calculations and machine learning Optimization of heat transfer at the nanoscale is necessary for efficient modern technology applications in nanoelectronics, energy conversion, and quantum technologies. In such applications, phonons dominate thermal transport and optimal performance requires minimum phonon conduction. Coherent phonon conduction is minimized by maximum disorder in the aperiodic modulation profile of width-modulated nanowaveguides, according to a physics rule. It is minimized for moderate disorder against physics intuition in composite nanostructures. Such counter behaviors call for a better understanding of the optimization of phonon transport in non-uniform nanostructures. We have explored mechanisms underlying the optimization of width-modulated nanowaveguides with calculations and machine learning, and we report on generic behavior. We show that the distribution of the thermal conductance among the aperiodic width-modulation configurations is controlled by the modulation degree irrespective of choices of constituent material, width-modulation-geometry, and composition constraints. The efficiency of Bayesian optimization is evaluated against increasing temperature and sample size. It is found that it decreases with increasing temperature due to thermal broadening of the thermal conductance distribution. It shows weak dependence on temperature in samples with high discreteness in the distribution spectrum. Our work provides new physics insight and indicates research pathways to optimize heat transfer in non-uniform nanostructures. Phonon ; Thermal conduction ; Aperiodic graph ; Condensed matter physics ; Modulation (music) ; Physics ; Heat transfer ; Statistical physics ; Full width at half maximum ; Materials science ; Quantum mechanics ; Mathematics Thermal properties of materials ; Advanced Thermoelectric Materials and Devices ; Thermal Radiation and Cooling Technologies