jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Yuchen Guo | Xiangyang Yu | Weibin Hong | Yefan Cai | Wanbang Xu | Hongyu Gu
Source: Journal of Near Infrared Spectroscopy (article, 2023-08-12)
Field: Chemistry — Analytical Chemistry

On-site rapid detection of aging of Pericarpium Citri Reticulatae using multispectral imaging Pericarpium Citri Reticulatae is a traditional Chinese medicine with high medicinal value, and its storage age has a great impact on its ethno-pharmaceutical relevance. At present, there is a situation in the market place where Pericarpium Citri Reticulatae with short storage age is fraudulently sold as Pericarpium Citri Reticulatae with long storage age, and some unaged orange peels dyed with tea are sold as Pericarpium Citri Reticulatae at a high price. In this study, a rapid, on-site method for identifying the storage age of Xinhui Pericarpium Citri Reticulatae based on spectral imaging technology was described. The image features and spectral features were extracted respectively from the surface reflection spectral images of Pericarpium Citri Reticulatae, and a machine learning model was established to identify the storage age. This study explored the classification effect of the combination of different spectral pre-processing methods and machine learning models, and finally selected the combination of standard normal variate and random forest models, to achieve 95% accuracy on the test dataset, showing excellent generalization performance. The result shows that the spectral imaging technology can rapidly identify the storage age of Xinhui Pericarpium Citri Reticulatae in real time, which has a great application prospect in the detection of the properties of medicinal materials. Multispectral image ; Spectral imaging ; Artificial intelligence ; Computer science ; Pattern recognition (psychology) ; Mathematics ; Remote sensing ; Geography Spectroscopy and Chemometric Analyses ; Remote-Sensing Image Classification ; Remote Sensing and Land Use
Authors: Odunayo T. Ore | F. M. Adebiyi
Source: Industrial & Engineering Chemistry Research (article, 2023-08-29)
Field: Chemistry — Analytical Chemistry

Pyrolysis of Oil Sand Bitumen Using a Fixed-Bed Reactor: Process Modeling and Compositional Analysis In the present work, a comprehensive study was carried out on the pyrolysis of oil sand bitumen using a fixed-bed reactor with a view to estimating the optimal conditions for maximum yields of oil, char, and gas. The laboratory scale experiments were designed and optimized using the response surface methodology and artificial neural network. The operational pyrolysis parameters adopted in the current study were sample dosage, reaction temperature, and reaction time, while the pyrolysis products were characterized using Fourier transform infrared and gas chromatography. The analysis of variance based on the response surface methodology showed that R2 and adjusted R2 values were >0.97 for oil, char, and gas yields, indicating that the model is compatible and adequate for estimating the respective interactions of process parameters on target variables. However, the artificial neural network showed that R2 was >0.99 for training, validation, and testing, indicating that it is a relatively better model predictor of oil sand bitumen pyrolysis. Optimization results showed that temperature > 400 °C led to more production of gases, while low temperature favored the maximum production of chars. More oil yields were obtained using increased sample dosage at moderate temperatures. Functional group analysis showed the principal presence of C-H (CH2) and C-H (CH3) groups, indicating that aliphatic compounds are extant in the product oils. Compositional analysis showed that the pyrolytic oils contained organic compounds in the gasoline, kerosene, diesel, and residual fuel oil ranges. Methane was the predominant gas produced in the pyrolysis process. The study demonstrated the generative potential of oils and gases from oil sand bitumen. Pyrolysis ; Diesel fuel ; Gasoline ; Kerosene ; Char ; Asphalt ; Response surface methodology ; Fuel oil ; Coal ; Gas chromatography ; Pulp and paper industry ; Chemistry ; Materials science ; Chemical engineering ; Chromatography ; Waste management ; Organic chemistry ; Composite material Petroleum Processing and Analysis ; Thermochemical Biomass Conversion Processes ; Hydrocarbon exploration and reservoir analysis
Authors: James Pepper | Oliver Levano Blanch | Ben Thomas | Martin Jackson
Source: Journal of Materials Science (article, 2023-09-01)
Field: Materials Science — Materials Chemistry

Channelling electric current during the field-assisted sintering technique (FAST) to control microstructural evolution in Ti-6Al-4V Abstract Perhaps the most defining feature of field-assisted sintering technology (FAST) is the application of an electric current, in addition to the uniaxial pressure, to create resistive heating in and around the sample region. However, with a few exceptions, most research takes this as an unchangeable part of the process. Here, this current flow has been directed to specific regions within the toolset, using boron nitride as electrically insulating material. This caused the heating to occur in differing regions within the Ti-6Al-4V sample and mould over four insulating configurations, with the shift in current density resulting in an extreme disparity in the final microstructures. The samples were imaged and analysed with deep learning in MIPAR, alongside comparisons with finite element analysis (FEA) models for 20 s and 5 min dwell times, to provide the technique with predictive capabilities for grain size and microstructure. The results gathered imply significant potential for this concept to improve the flexibility of FAST, and reduce negative effects such as undesirable temperature profiles in size scaling sintering for industry. Materials science ; Sintering ; Microstructure ; Current (fluid) ; Electric field ; Electromigration ; Current density ; Boron nitride ; Dwell time ; Resistive touchscreen ; Electric current ; Engineering physics ; Composite material ; Computer science ; Electrical engineering Titanium Alloys Microstructure and Properties ; Advanced materials and composites ; Intermetallics and Advanced Alloy Properties
Authors: Qi Wang | Chen Wang | Cheng Chi | Niuchang Ouyang | Ruiqiang Guo | Nuo Yang | Yue Chen
Source: Physical review. B./Physical review. B (article, 2023-09-28)
Field: Materials Science — Materials Chemistry

Phonon transport in freestanding <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:msub><mml:mtext>SrTiO</mml:mtext><mml:mn>3</mml:mn></mml:msub></mml:math> down to the monolayer limit Perovskites down to the monolayer limit have emerged and have attracted increased interest due to their two-dimensional nature with potentially novel physical properties. Here, we investigate the phonon transport in the oxide perovskite ${\mathrm{SrTiO}}_{3}$ with thicknesses from the monolayer limit to 10 nm by constructing an accurate first-principles machine-learning potential and combining it with the Boltzmann transport equation and homogeneous nonequilibrium molecular dynamics simulations. Compared to its bulk counterpart, the phonon dispersion relation of monolayer ${\mathrm{SrTiO}}_{3}$ is insensitive to temperature, and the calculated in-plane thermal conductivity of monolayer ${\mathrm{SrTiO}}_{3}$ is much larger than that of bulk ${\mathrm{SrTiO}}_{3}$, which mainly results from the unique out-of-plane atomic vibrations in monolayer ${\mathrm{SrTiO}}_{3}$. The thermal conductivity of ${\mathrm{SrTiO}}_{3}$ thin film first decreases and then approaches the bulk value as thickness increases from the monolayer limit to 10 nm. The hardening of the out-of-plane acoustic phonon branch and the transition of low-frequency optical phonons can explain the observed trend in thermal conductivity transitions. Our study demonstrates that monolayer ${\mathrm{SrTiO}}_{3}$ has a higher thermal conductivity than its bulk counterpart with covalent bonds at the first-principles level of accuracy, and dimension reduction has a weak inhibition on phonon transport in freestanding atomically smooth ${\mathrm{SrTiO}}_{3}$ thin films, which furthers the understanding of phonon transport in two-dimensional perovskite thin films. Phonon ; Thermal conductivity ; Monolayer ; Materials science ; Condensed matter physics ; Boltzmann equation ; Perovskite (structure) ; Physics ; Thermodynamics ; Nanotechnology ; Crystallography ; Chemistry ; Composite material Thermal properties of materials ; Electronic and Structural Properties of Oxides ; Ferroelectric and Piezoelectric Materials
Authors: Hongliang Zhang | Ranran Su | Benoît Queylat | Tae‐Ho Kim | G. Lucadamo | William S. Howland | Adrien Couet
Source: Corrosion Science (article, 2023-10-31)
Field: Materials Science — Materials Chemistry

3D reconstruction and interconnectivity quantification of the nano-porosity in the oxide layer of corroded Zr alloys Corrosion ; Interconnectivity ; Porosity ; Materials science ; Nano- ; Layer (electronics) ; Metallurgy ; Oxide ; Composite material ; Computer science Nuclear Materials and Properties ; Advanced materials and composites ; Metal and Thin Film Mechanics
Authors: Xiaobing Shen | Wilmar Martínez
Source:  (article, 2023-09-04)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Machine Learning Model for High-Frequency Magnetic Loss Predictions Based on Loss Map by a Measurement Kit Accurately forecasting the losses in high-frequency magnetic materials is a significant challenge when optimizing the design of high-frequency (HF) magnetic components. Existing models do not adequately consider the intricate interactions among geometry, and temperature factors, which have distinct and substantial impacts on core losses. A new method is introduced, which utilizes a Deep Neural Network (DNN) model to construct parameterized models for high-frequency magnetic core loss based on measurement data. The DNN employs the Gaussian Error Linear Unit (GELU) activation function and Huber loss function, and its performance is compared to that of a conventional Rectified Linear Unit (ReLU) activation and Mean Squared Error (MSE) loss function. The proposed DNN demonstrates significantly higher accuracy and improved robustness. Robustness (evolution) ; Mean squared error ; Artificial neural network ; Parameterized complexity ; Computer science ; Gaussian ; Gaussian function ; Algorithm ; Artificial intelligence ; Mathematics ; Statistics ; Physics Magnetic Properties and Applications ; Induction Heating and Inverter Technology ; Non-Destructive Testing Techniques
Authors: Ashwin Ravichandran | Shreyas Honrao | Stephen Xie | Eric Fonseca | John W. Lawson
Source: The Journal of Physical Chemistry Letters (article, 2023-12-26)
Field: Materials Science — Materials Chemistry

Computational Design of Low Melting Eutectics of Molten Salts: A Combined Machine Learning and Thermodynamic Modeling Approach We develop a computational framework combining thermodynamic and machine learning models to predict the melting temperatures of molten salt eutectic mixtures (<i>T</i><sub>eut</sub>). The model shows an accuracy of ∼6% (mean absolute percentage error) over the entire data set. Using this approach, we screen millions of combinatorial eutectics ranging from binary to hexanary, predict new mixtures, and propose design rules that lead to low <i>T</i><sub>eut</sub>. We show that heterogeneity in molecular sizes, quantified by the molecular volume of the components, and mixture configurational entropy, quantified by the number of mixture components, are important factors that can be exploited to design low <i>T</i><sub>eut</sub> mixtures. While predicting eutectic composition with existing techniques had proved challenging, we provide some preliminary models for estimating the compositions. The high-throughput screening technique presented here is essential to design novel mixtures for target applications and efficiently navigate the vast design space of the eutectic mixtures. Eutectic system ; Binary number ; Computer science ; Molten salt ; Volume (thermodynamics) ; Entropy (arrow of time) ; Materials science ; Thermodynamics ; Biological system ; Mathematics Crystallization and Solubility Studies ; Ionic liquids properties and applications ; Phase Equilibria and Thermodynamics
Authors: Ahmed Belaadi | Messaouda Boumaaza | Hassan Alshahrani | Mohammad K. A. Khan | Mostefa Bourchak
Source: The International Journal of Advanced Manufacturing Technology (article, 2023-06-24)
Field: Materials Science — Polymers and Plastics

Optimization of drilling process parameters of sisal/cork-reinforced epoxy biosandwich structure by multi-objective RSM and hybrid ANN-GA models Response surface methodology ; Machining ; Drill ; Delamination (geology) ; Biocomposite ; Drilling ; Materials science ; Mechanical engineering ; Abrasive ; Composite material ; Engineering ; Computer science ; Composite number ; Machine learning Natural Fiber Reinforced Composites ; Erosion and Abrasive Machining ; Advanced Machining and Optimization Techniques
Authors: Fengqing Chen | Zhen Guo | Jinhe Wang | Runhai Ouyang | Dianpu Ma | Pei Gao | Fei Pan | Peng Ding
Source: Computational Materials Science (article, 2023-09-03)
Field: Materials Science — Polymers and Plastics

Accelerated feasible screening of flame-retardant polymeric composites using data-driven multi-objective optimization Fire retardant ; Limiting oxygen index ; Limiting ; Ultimate tensile strength ; Materials science ; Composite material ; Work (physics) ; Computer science ; Mechanical engineering ; Chemical engineering ; Pyrolysis ; Engineering Flame retardant materials and properties ; Fire dynamics and safety research ; Synthesis and properties of polymers
Authors: JingChun Wang | Yao Wang | Rui–Xue Xu | GuanHua Chen | Xiao Zheng
Source: The Journal of Chemical Physics (article, 2023-04-19)
Field: Materials Science — Materials Chemistry

A semilocal machine-learning correction to density functional approximations Machine learning (ML) has demonstrated its potential usefulness for the development of density functional theory methods. In this work, we construct an ML model to correct the density functional approximations, which adopts semilocal descriptors of electron density and density derivative and is trained by accurate reference data of relative and absolute energies. The resulting ML-corrected functional is tested on a comprehensive dataset including various types of energetic properties. Particularly, the ML-corrected Becke's three parameters and the Lee-Yang-Parr correlation (B3LYP) functional achieves a substantial improvement over the original B3LYP on the prediction of total energies of atoms and molecules and atomization energies, and a marginal improvement on the prediction of ionization potentials, electron affinities, and bond dissociation energies; whereas, it preserves the same level of accuracy for isomerization energies and reaction barrier heights. The ML-corrected functional allows for fully self-consistent-field calculation with similar efficiency to the parent functional. This study highlights the progress of building an ML correction toward achieving a functional that performs uniformly better than B3LYP. Computer science ; Mathematics education ; Machine learning ; Mathematics ; Artificial intelligence ; Calculus (dental) ; Medicine Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Catalysis and Oxidation Reactions
Authors: Lukas Wiku Kuswidiyanto | Dong Eok Kim | Teng Fu | Kyoung Su Kim | Xiongzhe Han
Source: Agriculture (article, 2023-11-29)
Field: Chemistry — Analytical Chemistry

Detection of Black Spot Disease on Kimchi Cabbage Using Hyperspectral Imaging and Machine Learning Techniques The cultivation of kimchi cabbage in South Korea has always faced significant challenges due to the looming presence of Alternaria leaf spot (ALS), which is a fungal disease mainly caused by Alternaria alternata. The emergence of black spots resulting from Alternaria infection lowers the quality of the plant, rendering it inedible and unmarketable. The timely identification of this disease is crucial, as it provides essential data enabling swift intervention, thereby localizing the infection throughout the field. Hyperspectral imaging technologies excel in detecting subtle shifts in reflectance values induced by chemical differences within leaf tissues. However, research on the spectral correlation between Alternaria and kimchi cabbage remains relatively scarce. Therefore, this study aims to identify the spectral signature of Alternaria infection on kimchi cabbage and develop an automatic classifier for detecting Alternaria disease symptoms. Alternaria alternata was inoculated on various sizes of kimchi cabbage leaves and observed daily using a hyperspectral imaging system. Datasets were created based on captured hyperspectral images to train four classifier models, including support vector machine (SVM), random forest (RF), one-dimensional convolutional neural network (1D-CNN), and one-dimensional residual network (1D-ResNet). The results suggest that 1D-ResNet outperforms the other models with an overall accuracy of 0.91, whereas SVM, RF, and 1D-CNN achieved 0.80, 0.88, and 0.86, respectively. This study may lay the foundation for future research on high-throughput disease detection, frequently incorporating drones and aerial imagery. Hyperspectral imaging ; Alternaria alternata ; Alternaria ; Multispectral image ; Support vector machine ; Convolutional neural network ; Spectral signature ; Artificial intelligence ; Black spot ; Leaf spot ; Biology ; Horticulture ; Computer science ; Remote sensing Spectroscopy and Chemometric Analyses ; Essential Oils and Antimicrobial Activity ; Remote Sensing in Agriculture
Authors: Hongyi Xiao | Ge Zhang | Entao Yang | Robert Ivancic | Sean A. Ridout | Robert A. Riggleman | D. J. Durian | Andrea J. Liu
Source: Proceedings of the National Academy of Sciences (article, 2023-10-09)
Field: Materials Science — Materials Chemistry

Identifying microscopic factors that influence ductility in disordered solids There are empirical strategies for tuning the degree of strain localization in disordered solids, but they are system-specific and no theoretical framework explains their effectiveness or limitations. Here, we study three model disordered solids: a simulated atomic glass, an experimental granular packing, and a simulated polymer glass. We tune each system using a different strategy to exhibit two different degrees of strain localization. In tandem, we construct structuro-elastoplastic (StEP) models, which reduce descriptions of the systems to a few microscopic features that control strain localization, using a machine learning-based descriptor, softness, to represent the stability of the disordered local structure. The models are based on calculated correlations of softness and rearrangements. Without additional parameters, the models exhibit semiquantitative agreement with observed stress-strain curves and softness statistics for all systems studied. Moreover, the StEP models reveal that initial structure, the near-field effect of rearrangements on local structure, and rearrangement size, respectively, are responsible for the changes in ductility observed in the three systems. Thus, StEP models provide microscopic understanding of how strain localization depends on the interplay of structure, plasticity, and elasticity. Ductility (Earth science) ; Materials science ; Local structure ; Plasticity ; Polymer ; Elasticity (physics) ; Statistical physics ; Strain (injury) ; Biological system ; Chemical physics ; Physics ; Composite material Material Dynamics and Properties ; Theoretical and Computational Physics ; Building materials and conservation
Authors: Le Van Lich | Thuy-Huong Nguyen | Dang Thi Hong Hue | Tinh Quoc Bui | Trong‐Giang Nguyen | Van‐Hai Dinh | Manh‐Huong Phan
Source: Materials Research Bulletin (article, 2023-07-05)
Field: Materials Science — Materials Chemistry

The design of compositionally modulated lead-free ferroelectrics with large electromechanical response via high-throughput phase-field simulations and machine learning Compositionally graded ferroelectrics (CGFEs) have attracted great interest due to their exceptional and tunable electromechanical properties, which are anticipated to be superior to traditional ferroelectrics. However, an effective design of CGFEs with desired properties from a huge compositional space remains an enormous challenge. In this study, we present an efficient design strategy for CGFEs through a combination of high-throughput phase-field simulation and machine learning (ML) algorithm. Systematic phase-field simulations are first performed to establish a dataset of electromechanical properties for various CGFEs, which are characterized by three degrees of freedom, including compositions of two end materials and gradient index. A ML-assisted optimization process is then conducted on this dataset to yield optimal electromechanical properties, which are further analyzed by phase-field simulations. The ML-guided simulations lead us to discover that the large optimal electromechanical responses can be achieved in CGFEs with average composition near the morphotropic phase boundary yet large compositional gradient. These CGFEs accommodate a coexistence of tetragonal, orthorhombic, and cubic phases in their domain structures, which flatten the energy barrier landscape and facilitate the rotation of polarization under mechanical field. Our proposed highly efficient design strategy can be readily adapted to identify CGFEs possessing other principal elements and properties, which will accelerate the discovery of new high-performance CGFE materials. Materials science ; Phase boundary ; Tetragonal crystal system ; Field (mathematics) ; Phase (matter) ; Throughput ; Phase field models ; Polarization (electrochemistry) ; Computer science ; Nanotechnology ; Physics ; Mathematics Ferroelectric and Piezoelectric Materials ; Multiferroics and related materials ; Ferroelectric and Negative Capacitance Devices
Authors: Mehdi Shams | Zachary A. Smith | Lukas Herron | Ziyue Zou | Pratyush Tiwary
Source: arXiv (Cornell University) (review, 2023-06-15)
Field: Materials Science — Materials Chemistry

Enhanced Sampling with Machine Learning: A Review Molecular dynamics (MD) enables the study of physical systems with excellent spatiotemporal resolution but suffers from severe time-scale limitations. To address this, enhanced sampling methods have been developed to improve exploration of configurational space. However, implementing these is challenging and requires domain expertise. In recent years, integration of machine learning (ML) techniques in different domains has shown promise, prompting their adoption in enhanced sampling as well. Although ML is often employed in various fields primarily due to its data-driven nature, its integration with enhanced sampling is more natural with many common underlying synergies. This review explores the merging of ML and enhanced MD by presenting different shared viewpoints. It offers a comprehensive overview of this rapidly evolving field, which can be difficult to stay updated on. We highlight successful strategies like dimensionality reduction, reinforcement learning, and flow-based methods. Finally, we discuss open problems at the exciting ML-enhanced MD interface. Computer science ; Viewpoints ; Sampling (signal processing) ; Reinforcement learning ; Artificial intelligence ; Machine learning ; Field (mathematics) ; Dimensionality reduction ; Curse of dimensionality ; Domain (mathematical analysis) ; Interface (matter) ; Data science ; Mathematics Machine Learning in Materials Science ; Microfluidic and Capillary Electrophoresis Applications ; Fuel Cells and Related Materials