jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Ethan Kane Waters | Carla Chen | Mostafa Rahimi Azghadi
Source: Computers and Electronics in Agriculture (review, 2024-12-03)
Field: Chemistry — Analytical Chemistry

Sugarcane health monitoring with satellite spectroscopy and machine learning: A review Research into large-scale crop monitoring has flourished due to increased accessibility to satellite imagery. This review delves into previously unexplored and under-explored areas in sugarcane health monitoring and disease/pest detection using satellite-based spectroscopy and Machine Learning (ML). It discusses key considerations in system development, including relevant satellites, vegetation indices, ML methods, factors influencing sugarcane reflectance, optimal growth conditions, common diseases, and traditional detection methods. Many studies highlight how factors like crop age, soil type, viewing angle, water content, recent weather patterns, and sugarcane variety can impact spectral reflectance, affecting the accuracy of health assessments via spectroscopy. However, these variables have not been fully considered in the literature. In addition, the current literature lacks comprehensive comparisons between ML techniques and vegetation indices. This review addresses these gaps and discusses that, while current findings suggest the potential for an ML-driven satellite spectroscopy system for monitoring sugarcane health, further research is essential. This paper offers a comprehensive analysis of previous research to aid in unlocking this potential and advancing the development of an effective sugarcane health monitoring system using satellite technology. • Review the use of satellite-based spectroscopy and machine learning for sugarcane health monitoring. • Discuss essential factors to consider in the development of monitoring systems using satellite-based spectroscopy. • Analyse the use of freely available satellite data for more affordable monitoring. • Provide a comparison of effective machine learning techniques and vegetation indices for sugarcane health monitoring. • Conduct a comprehensive synthesis of the existing literature, evaluating both its strengths and limitations. Satellite ; Remote sensing ; Environmental science ; Spectroscopy ; Computer science ; Engineering ; Artificial intelligence ; Aerospace engineering ; Geography ; Physics Spectroscopy and Chemometric Analyses ; Remote Sensing in Agriculture ; Smart Agriculture and AI
Authors: Weiyang Zhang | Xuecheng Peng | Guo Jing | Hanjie Guo | Shusen Cheng
Source: Journal of Analytical and Applied Pyrolysis (article, 2024-07-27)
Field: Materials Science — Materials Chemistry

Pyrolysis kinetic analysis and model constructions of different ranks of coal and validation by GA–BP neural networks Pyrolysis ; Kinetic energy ; Coal ; Artificial neural network ; Biological system ; Chemistry ; Computer science ; Artificial intelligence ; Organic chemistry ; Biology ; Physics Thermal and Kinetic Analysis ; Iron and Steelmaking Processes ; Thermochemical Biomass Conversion Processes
Authors: Ziyue Zou | Pratyush Tiwary
Source: The Journal of Physical Chemistry B (article, 2024-03-19)
Field: Materials Science — Materials Chemistry

Enhanced Sampling of Crystal Nucleation with Graph Representation Learnt Variables In this study, we present a graph neural network (GNN)-based learning approach using an autoencoder setup to derive low-dimensional variables from features observed in experimental crystal structures. These variables are then biased in enhanced sampling to observe state-to-state transitions and reliable thermodynamic weights. In our approach, we used simple convolution and pooling methods. To verify the effectiveness of our protocol, we examined the nucleation of various allotropes and polymorphs of iron and glycine in their molten states. Our graph latent variables, when biased in well-tempered metadynamics, consistently show transitions between states and achieve accurate thermodynamic rankings in agreement with experiments, both of which are indicators of dependable sampling. This underscores the strength and promise of our GNN variables for improved sampling. The protocol shown here should be applicable for other systems and other sampling methods. Latent variable ; Sampling (signal processing) ; Undersampling ; Graph ; Nucleation ; Metadynamics ; Autoencoder ; Statistical physics ; Crystal (programming language) ; Computer science ; Pooling ; Biological system ; Artificial intelligence ; Molecular dynamics ; Artificial neural network ; Theoretical computer science ; Chemistry ; Computational chemistry ; Thermodynamics ; Physics ; Detector Machine Learning in Materials Science ; Protein Structure and Dynamics ; Computational Drug Discovery Methods
Authors: K. Nasri | Lotfi Toubal
Source: Polymers (article, 2024-02-17)
Field: Materials Science — Polymers and Plastics

Artificial Neural Network Approach for Assessing Mechanical Properties and Impact Performance of Natural-Fiber Composites Exposed to UV Radiation Amidst escalating environmental concerns, short natural-fiber thermoplastic (SNFT) biocomposites have emerged as sustainable materials for the eco-friendly production of mechanical components. However, their limited durability has prompted research into the experimental evaluation of the deterioration of the mechanical characteristics of SNFT biocomposites, particularly under the influence of ultraviolet rays. However, conducting tests to evaluate the mechanical properties can be time-consuming and expensive. In this study, an artificial neural network (ANN) model was employed to predict the mechanical properties (tensile strength) and the impact performance (resistance and absorbed energy) of polypropylene reinforced with 30 wt.% short flax or wood pine fibers (referred to as PP30-F or PP30-P, respectively). Eight parameters were collected from experimental studies. The ANN input parameters comprised nondestructive test results, including mass, hardness, roughness, and natural frequencies, while the output parameters were the tensile strength, the maximum impact load, and absorbed energy. The model was developed using the ANN toolbox in MATLAB. The linear coefficient of correlation and mean squared error were selected as the metrics for evaluating the performance function and accuracy of the ANN model. They calculate the relationship and the average squared difference between the predicted and actual values. The data analysis conducted by the models demonstrated exceptional predictive capability, achieving an accuracy rate exceeding 96%, which was deemed satisfactory. For both the PP30-F and PP30-P biocomposites, the ANN predictions deviated from the experimental data by 3, 5, and 6% with regard to the impact load, absorbed energy, and tensile strength, respectively. Ultimate tensile strength ; Materials science ; Composite material ; Izod impact strength test ; Natural fiber ; Artificial neural network ; Coefficient of determination ; Durability ; Fiber ; Computer science ; Machine learning Natural Fiber Reinforced Composites ; Bamboo properties and applications ; Wood Treatment and Properties
Authors: Mengjia Sheng | Xiang Zhang | Hongye Cheng | Zhen Song | Zhiwen Qi
Source: Green Chemical Engineering (article, 2024-07-17)
Field: Chemistry — Inorganic Chemistry

Multi-criteria computational screening of [BMIM][DCA]@MOF composites for CO2 capture Ionic liquid (IL) can be inserted into metal organic framework (MOF) to form IL@MOF composite with enhanced properties. In this work, hypothetical IL@MOFs were computationally constructed and screened by integrating molecular simulation and convolutional neural network (CNN) for CO2 capture. First, the IL [BMIM][DCA] with a large CO2 solubility was inserted into 1631 pre-selected Computational-Ready Experimental (CoRE) MOFs to create hypothetical IL@MOFs. Then, given the temperature and pressure of adsorption and desorption, the CO2/N2 selectivity and CO2 working capacity of 700 representative IL@MOFs were assessed via molecular simulations. Based on the results, two CNN models were trained and used to predict the performance of other IL@MOFs, which reduces the computational costs effectively. By combining the simulation results and CNN model predictions, 22 IL@MOFs with top-ranked performance were identified. Three distinct ones IL@HABDAS, IL@GUBKUL, and IL@MARJAQ were chosen for explicit analysis. It was found that a desired balance between CO2/N2 selectivity and CO2 working capacity can be obtained by inserting the optimal number of IL molecules. This helps guide a novel design of IL@MOF composites with advanced performance on carbon capture. Ionic liquid ; Selectivity ; Materials science ; Adsorption ; Metal-organic framework ; Desorption ; Composite number ; Computer science ; Convolutional neural network ; Chemical engineering ; Nanotechnology ; Composite material ; Chemistry ; Machine learning ; Organic chemistry ; Engineering Metal-Organic Frameworks: Synthesis and Applications ; Ionic liquids properties and applications ; Carbon dioxide utilization in catalysis
Authors: Bingqin Wang | Long Zhao | Yongfeng Chen | Lingsheng Zhu | Chao Liu | Xuequn Cheng | Xiaogang Li
Source: Journal of Materials Research and Technology (article, 2024-09-13)
Field: Materials Science — Materials Chemistry

Intelligent corrosion analysis and life prediction of ductile iron pipe systems using machine learning and electrochemical sensors This study established a circulating system to control the concentration of substances and temperature in the aqueous solution. Simultaneously, sensors were used to continuously monitor the corrosion of three pipe materials: ductile iron (DI), surface-treated ductile iron (SDI), and carbon steel (CS). A corrosion decision model based on a machine learning framework was developed for data mining. The results show that the developed model provides accurate corrosion prediction strategies. Analysis revealed that high temperature is the primary factor accelerating corrosion in water systems. SDI accelerates at 60 °C, reaching its peak at 90 °C, while DI and CS peak at 80 °C. The superior corrosion resistance of SDI is attributed to its ability to withstand accelerated corrosion under high temperatures and environmental coupling, making it more stable when immersed in water. Materials science ; Corrosion ; Metallurgy ; Electrochemistry ; Ductile iron ; Cast iron ; Electrode Corrosion Behavior and Inhibition ; Hydrogen embrittlement and corrosion behaviors in metals ; Non-Destructive Testing Techniques
Authors: Soufyane Bouchelaghem | Imad Eddine Tibermacine | Marco Balsi | Monica Moroni | Christian Napoli
Source:  (article, 2024-04-15)
Field: Chemistry — Analytical Chemistry

Cross-Domain Machine Learning Approaches using Hyperspectral Imaging for Plastics litter detection Plastics detection and polymer separation are important tasks for environmental monitoring and waste recycling. The authors previously used hyperspectral data in the lower bands of the SWIR range from 900nm to 1700 nm to perform plastics classification, by applying supervised machine learning methods to data gathered by a custom-assembled stand-alone push-broom system in indoor and outdoor conditions. In this work, we explored the application of two unsupervised learning algorithms: K-Means clustering and Gaussian Mixture Models (GMM), in order to avoid the use of a manually-labeled test set, and to obtain more reliable and flexible application in different environmental conditions. The results suggest that GMM performs well indoors but poorly outside, with GMM exhibiting subtle segmentation and K-Means displaying broader, more generalist categorization. The cross-domain adaptation is difficult and models that balance granularity and generalization are needed to detect plastic in varied settings. This effort advances garbage management and recycling and establishes hyperspectral imaging in real-world environments. Hyperspectral imaging ; Computer science ; Domain (mathematical analysis) ; Artificial intelligence ; Pattern recognition (psychology) ; Mathematics Spectroscopy and Chemometric Analyses
Authors: Jie Zhao | Xiaoyan Wang | Haobo Li | Xiaoyong Xu
Source: RSC Advances (article, 2024-01-01)
Field: Materials Science — Materials Chemistry

Interpretable machine learning-assisted screening of perovskite oxides Perovskite oxides are extensively utilized in energy storage and conversion. However, they are conventionally screened <i>via</i> time-consuming and cost-intensive experimental approaches and density functional theory. Herein, interpretable machine learning is applied to identify perovskite oxides from virtual perovskite-type combinations by constructing classification and regression models to predict their thermodynamic stability and energy above the convex hull (<i>E</i><sub>h</sub>), respectively, and interpreting the models using SHapley Additive exPlanations. The highest occupied molecular orbital energy and the elastic modulus of the B-site elements of perovskite oxides are the top two features for stability prediction, whereas the Stability Label and features involving the elastic modulus and ionic radius are crucial for <i>E</i><sub>h</sub> regression. A classification model, which displays an accuracy of 0.919, precision of 0.937, F1-score of 0.932, and recall of 0.935, screens 682 143 stable perovskite oxides from 1 126 668 virtual perovskite-type combinations. The <i>E</i><sub>h</sub> values of the predicted stable perovskites are forecasted by a regression model with a coefficient of determination of 0.916, and root mean square error of 24.2 meV atom<sup>-1</sup>. Good agreement is observed between the regression model predicted and density functional theory-calculated <i>E</i><sub>h</sub> values. Perovskite (structure) ; Materials science ; Computer science ; Artificial intelligence ; Chemical engineering ; Engineering Machine Learning in Materials Science ; Advancements in Solid Oxide Fuel Cells ; Electronic and Structural Properties of Oxides
Authors: Fengwen Kang | Mei Hong | Xiangyun Gao | Daxu Zhang | Fang Ye | Yi Zhang | Laifei Cheng
Source: Carbon (article, 2024-11-19)
Field: Materials Science — Ceramics and Composites

Three-dimensional in-situ observation and cohesive zone modeling of tension-induced delamination of two-dimensional C/SiC composites via deep learning-based damage identification Delamination (geology) ; Tension (geology) ; In situ ; Composite material ; Materials science ; Geology ; Ultimate tensile strength ; Chemistry ; Seismology Advanced ceramic materials synthesis ; Mechanical Behavior of Composites ; Advanced Surface Polishing Techniques
Authors: Jincheng Yue | Shiqian Hu | Bin Xu | Rongkun Chen | Long Xiong | Rulei Guo | Yuanzhe Li | Lei‐Lei Nian | Junichiro Shiomi | Bo Zheng
Source: Physical review. B./Physical review. B (article, 2024-03-07)
Field: Materials Science — Materials Chemistry

Unraveling the mechanisms of thermal boundary conductance at the graphene-silicon interface: Insights from ballistic, diffusive, and localized phonon transport regimes Effective heat dissipation is critical for the performance and longevity of electronic devices. This study delves into the intricacies of thermal boundary conductance (TBC) between multilayer graphene and silicon, scrutinizing its correlation with graphene thickness. Employing a combination of the no-transducer time-domain thermal reflection technique and nonequilibrium molecular dynamics simulations with empirical and machine-learning based potentials, we discover an intriguing behavior: TBC initially increases with graphene thickness, but it eventually converges, suggesting that this phenomenon originates from the transition of the phonon transport from the ballistic to the diffusive regime in multilayer graphene. It is further demonstrated that a graphene-${\mathrm{MoS}}_{2}$ heterostructure exhibits a thickness-independent TBC behavior due to the strong phonon localization across the entire frequency range. The present study emphasizes the significance of incorporating phonon transport regimes in the design and optimization of thermal interfaces, laying the groundwork for innovative strategies to enhance heat dissipation in electronic devices. Phonon ; Ballistic conduction ; Graphene ; Condensed matter physics ; Materials science ; Conductance ; Silicon ; Ballistic conduction in single-walled carbon nanotubes ; Interface (matter) ; Thermal conductivity ; Physics ; Nanotechnology ; Electron ; Quantum mechanics ; Optoelectronics Thermal properties of materials ; Graphene research and applications ; Thermal Radiation and Cooling Technologies
Authors: Anna Ściążko | Yosuke Komatsu | Takaaki Shimura | Naoki Shikazono
Source: npj Computational Materials (article, 2024-03-09)
Field: Materials Science — Materials Chemistry

Prediction of electrode microstructure evolutions with physically constrained unsupervised image-to-image translation networks Abstract Microstructure of electrodes determines the performance of electrochemical devices such as fuel cells and batteries. The efficiency and economic feasibility of these technologies depend on the stability of the microstructures throughout their lifetime. Although modeling techniques were proposed for determining electrode performance from 2- or 3-dimensional microstructural data, it is still extremely challenging to predict long-term structural degradation by means of numerical simulations. One of the major challenges is to overcome the difficulties in obtaining experimental data of an identical sample through the degradation process. In this work, a machine learning-based framework for predicting microstructural evolutions with limited amount of un-paired training data is proposed. Physically-constrained unsupervised image-to-image translation (UNIT) network is incorporated to predict nickel oxide reduction process in solid oxide fuel cell anode. The proposed framework is firstly validated by simplified toy-problems. Secondly, the UNIT network is applied to real microstructures of solid oxide fuel cells, which results in excellent visual and statistical agreements between real and artificially reduced samples. The proposed network can predict evolutions in new microstructures, which have not been used during training. Furthermore, a conditional UNIT network (C-UNIT) was demonstrated, which can predict the microstructure evolutions based on process conditions as well as continuous time series of microstructural changes. Microstructure ; Solid oxide fuel cell ; Materials science ; Anode ; Computer science ; Process (computing) ; Translation (biology) ; Degradation (telecommunications) ; Stability (learning theory) ; Electrode ; Artificial intelligence ; Machine learning ; Composite material Advancements in Solid Oxide Fuel Cells ; Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion
Authors: Churna Bhandari | Gavin Nop | Jonathan D. H. Smith | Durga Paudyal
Source: Physical Review Applied (article, 2024-08-16)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Accurate machine-learning predictions of coercivity in high-performance permanent magnets Increased demand for high-performance permanent magnets in the electric vehicle and wind-turbine industries has prompted the search for cost-effective alternatives. Discovering magnetic materials with the desired intrinsic and extrinsic permanent magnet properties presents a significant challenge to researchers because of issues with the global supply of rare-earth elements, material stability, and a low maximum magnetic energy product $B{H}_{\text{max}}$. While first-principles density functional theory (DFT) predicts materials' magnetic moments, magnetocrystalline anisotropy constants, and exchange interactions, it cannot compute extrinsic properties such as coercivity (${H}_{c}$). Although it is possible to calculate ${H}_{c}$ theoretically with micromagnetic simulations, the predicted value is larger than the experiment by almost an order of magnitude due to the Brown paradox. To circumvent these issues, we employ machine-learning (ML) methods on an extensive database obtained from experiments, DFT calculations, and micromagnetic modeling. The use of a large experimental dataset enables realistic ${H}_{c}$ predictions for materials such as $\text{Ce}$-doped ${\text{Nd}}_{2}{\text{Fe}}_{14}\text{B}$, comparing favorably against micromagnetically simulated coercivities. Remarkably, our ML model accurately identifies uniaxial magneto-crystalline anisotropy as the primary contributor to ${H}_{c}$. With DFT calculations, we predict the $\mathrm{Nd}$-site-dependent magnetic anisotropy behavior in ${\text{Nd}}_{2}{\text{Fe}}_{14}\text{B}$, confirming that $\text{Nd}\phantom{\rule{0.2em}{0ex}}4g$ sites mainly contribute to uniaxial magnetocrystalline anisotropy, and also calculate the Curie temperature (${T}_{\mathrm{c}}$). Both calculated results are in good agreement with the experiments. The coupled experimental dataset and ML modeling with DFT input predict ${H}_{c}$ with far greater accuracy and speed than was previously possible using micromagnetic modeling. Further, we reverse engineer the grain-boundary and intergrain exchange coupling with micromagnetic simulations by employing the ML predictions. Magnetocrystalline anisotropy ; Coercivity ; Magnet ; Condensed matter physics ; Anisotropy ; Curie temperature ; Magnetic anisotropy ; Density functional theory ; Materials science ; Ferromagnetism ; Physics ; Magnetization ; Magnetic field ; Optics ; Quantum mechanics Magnetic Properties of Alloys ; Magnetic properties of thin films ; Magnetic Properties and Applications
Authors: Huitao Bian | Benxue Ma | Guowei Yu | Fujia Dong | Yujie Li | Ying Xu | Haibo Tan
Source: Food Research International (article, 2024-09-04)
Field: Chemistry — Analytical Chemistry

Fusion features of microfluorescence hyperspectral imaging for qualitative detection of pesticide residues in Hami melon Hyperspectral imaging ; Melon ; Chemistry ; Artificial intelligence ; Biology ; Computer science ; Horticulture Spectroscopy and Chemometric Analyses ; Water Quality Monitoring and Analysis ; Pesticide Residue Analysis and Safety