jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Mingyu Ma | Nian‐Ke Chen | Dan Wang | Dong Han | Hong‐Bo Sun | Shengbai Zhang | Xianbin Li
Source: Materials Today Nano (article, 2023-01-13)
Field: Materials Science — Materials Chemistry

Defect physics in 2D monolayer I-VII semiconductor AgI Monolayer ; Vacancy defect ; Dopant ; Semiconductor ; Ionization energy ; Doping ; Extrapolation ; Materials science ; Ionization ; Electronic structure ; Condensed matter physics ; Nanotechnology ; Chemical physics ; Chemistry ; Physics ; Optoelectronics ; Quantum mechanics 2D Materials and Applications ; Advanced Photocatalysis Techniques ; MXene and MAX Phase Materials
Authors: Björn-Ivo Bachmann | Martin Müller | Dominik Britz | Thorsten Staudt | Frank Mücklich
Source: Metals (article, 2023-08-03)
Field: Materials Science — Materials Chemistry

Reproducible Quantification of the Microstructure of Complex Quenched and Quenched and Tempered Steels Using Modern Methods of Machine Learning Current conventional methods of evaluating microstructures are characterized by a high degree of subjectivity and a lack of reproducibility. Modern machine learning (ML) approaches have already shown great potential in overcoming these challenges. Once trained with representative data in combination with objective ground truth, the ML model is able to perform a task properly in a reproducible and automated manner. However, in highly complex use cases, it is often not possible to create a definite ground truth. This study addresses this problem using the underlying showcase of microstructures of highly complex quenched and quenched and tempered (Q/QT) steels. A patch-wise classification approach combined with a sliding window technique provides a solution for segmenting entire microphotographs where pixel-wise segmentation is not applicable since it is hardly feasible to create reproducible training masks. Using correlative microscopy, consisting of light optical microscope (LOM) and scanning electron microscope (SEM) micrographs, as well as corresponding data from electron backscatter diffraction (EBSD), a training dataset of reference states that covers a wide range of microstructures was acquired in order to train accurate and robust ML models in order to classify LOM or SEM images. Despite the enormous complexity associated with the steels treated here, classification accuracies of 88.8% in the case of LOM images and 93.7% for high-resolution SEM images were achieved. These high accuracies are close to super-human performance, especially in consideration of the reproducibility of the automated ML approaches compared to conventional methods based on subjective evaluations through experts. Microstructure ; Ground truth ; Segmentation ; Artificial intelligence ; Reproducibility ; Pixel ; Computer science ; Materials science ; Electron backscatter diffraction ; Pattern recognition (psychology) ; Microscope ; Optics ; Physics ; Mathematics ; Statistics ; Composite material Machine Learning in Materials Science ; Non-Destructive Testing Techniques ; Hydrogen embrittlement and corrosion behaviors in metals
Authors: Hannes Huber | Martin Sommer-Jörgensen | Moritz Gubler | Stefan Goedecker
Source: Physical Review Research (article, 2023-03-17)
Field: Materials Science — Materials Chemistry

Targeting high symmetry in structure predictions by biasing the potential energy surface Ground-state structures found in nature are, in many cases, of high symmetry. But structure prediction methods typically render only a small fraction of high-symmetry structures. Especially for large crystalline unit cells, there are many low-energy defect structures. For this reason, methods have been developed where either preferentially high-symmetry structures are used as input or where the whole structural search is done within a certain symmetry group. In both cases, it is necessary to specify the correct symmetry group beforehand. However, it can in general not be predicted which symmetry group is the correct one leading to the ground state. For this reason, we introduce a potential energy biasing scheme that favors symmetry and where it is not necessary to specify any symmetry group beforehand. On this biased potential energy surface, high-symmetry structures will be found much faster than on an unbiased surface and independently of the symmetry group to which they belong. For our two test cases, i.e., a ${C}_{60}$ fullerene and bulk silicon carbide, we get speedups of 25 and 63. In our data, we also find a clear correlation between the similarity of the atomic environments and the energy. In low-energy structures, all the atoms of a species tend to have similar environments. Symmetry (geometry) ; Symmetry operation ; Rotational symmetry ; Plane symmetry ; Symmetry number ; Energy (signal processing) ; Group (periodic table) ; Asymmetry ; Physics ; Theoretical physics ; Explicit symmetry breaking ; Symmetry breaking ; Geometry ; Quantum mechanics ; Spontaneous symmetry breaking ; Mathematics Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Semiconductor materials and devices
Authors: Wenbin Liu | Phuong Nguyen Thanh | Ming-Yuan Cho | Thao Nguyen Da
Source: Energies (article, 2023-03-06)
Field: Materials Science — Materials Chemistry

Categorizing 15 kV High-Voltage HDPE Insulator’s Leakage Current Surges Based on Convolution Neural Network Gated Recurrent Unit The leakage currents are appropriate for determining the contamination level of insulators in the power distribution system, which are efficiently cleaned or replaced during the maintenance schedule. In this research, the hybrid convolution neural network and gated recurrent unit model (CNN-GRU) are developed to categorize the leakage current pulse of the 15 kV HDPE insulator in the transmission towers in Taiwan. Many weather parameters are accumulated in the online monitoring system, which is installed in different transmission towers in coastal areas that suffer from heavy pollution. The Pearson correlation matrix is computed for selecting the high correlative features with the leakage current. Hyperparameter optimization is employed to decide the enhancing framework of the CNN-GRU methodology. The performance of the CNN-GRU is completely analyzed with other deep learning algorithms, which comprise the GRU, bidirectional GRU, LSTM, and bidirectional LSTM. The developed CNN-GRU acquired the most remarkable improvements of 79.48% CRE, 83.54% validating CRE, 14.14% CP, 20.89% validating CP, 66.24% MAE, 63.59% validating MAE, 73.24% MSE, and 71.59% validating MSE benchmarks compared with other methodologies. Therefore, the hybrid CNN-GRU methodology provides comprehensive information about the contamination degrees of insulator surfaces derived from the property of leakage currents. Convolutional neural network ; Insulator (electricity) ; Computer science ; Artificial neural network ; Hyperparameter ; Leakage (economics) ; Deep learning ; Electronic engineering ; Artificial intelligence ; Engineering ; Pattern recognition (psychology) ; Electrical engineering High voltage insulation and dielectric phenomena ; Power Transformer Diagnostics and Insulation ; Infrastructure Maintenance and Monitoring
Authors: Adel Bakhshipour
Source: Food Science & Nutrition (article, 2023-07-06)
Field: Chemistry — Analytical Chemistry

A data fusion approach for nondestructive tracking of the ripening process and quality attributes of green Hayward kiwifruit using artificial olfaction and proximal hyperspectral imaging techniques A data fusion strategy based on hyperspectral imaging (HSI) and electronic nose (e-nose) systems was developed in this study to inspect the postharvest ripening process of Hayward kiwifruit. The extracted features from the e-nose and HSI techniques, in single or combined mode, were used to develop machine learning algorithms. Performance evaluations proved that the fusion of olfactory and reflectance data improves the performance of discriminative and predictive algorithms. Accordingly, with high classification accuracies of 100% and 94.44% in the calibration and test stages, the data fusion-based support vector machine (SVM) outperformed the partial least square discriminant analysis (PLSDA) for discriminating the kiwifruit samples into eight classes based on storage time. Moreover, the data fusion-based support vector regression (SVR) was a better predictor than partial least squares regression (PLSR) for kiwifruit firmness, soluble solids content (SSC), and titratable acidity (TA) measures. The prediction <i>R</i> <sup>2</sup> and RMSE criteria of the SVR algorithm on the test data were 0.962 and 0.408 for firmness, 0.964 and 0.337 for SSC, and 0.955 and 0.039 for TA, respectively. It was concluded that the hybrid of e-nose and HSI systems coupled with the SVM algorithm delivers an effective tool for accurate and nondestructive monitoring of kiwifruit quality during storage. Titratable acid ; Partial least squares regression ; Artificial intelligence ; Electronic nose ; Support vector machine ; Pattern recognition (psychology) ; Sensor fusion ; Linear discriminant analysis ; Computer science ; Hyperspectral imaging ; Mathematics ; Machine learning ; Chemistry ; Food science Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Water Quality Monitoring and Analysis
Authors: Xin Min | Cheng‐Yen Lao | Yifei Liu | Haipeng Ji | Xiaowen Wu | Yangai Liu | Zhaohui Huang | Minghao Fang | Amr M. Abdelkader | R. Vasant Kumar | Kai Xi
Source: Journal of Luminescence (article, 2023-12-09)
Field: Materials Science — Materials Chemistry

A negative-thermal-quenching LaMgAl11O19: Er3+, Sm3+ phosphor achieved by energy transfer from Er3+ to Sm3+ Phosphor ; Luminescence ; Quenching (fluorescence) ; Emission intensity ; Atmospheric temperature range ; Diode ; Thermal ; Optoelectronics ; Materials science ; Light-emitting diode ; Energy transfer ; Chemistry ; Analytical Chemistry (journal) ; Fluorescence ; Optics ; Thermodynamics Luminescence Properties of Advanced Materials ; Perovskite Materials and Applications ; Solid State Laser Technologies
Authors: M. W. Matsen | T. M. Beardsley | James D. Willis
Source: Physical Review Materials (article, 2023-10-31)
Field: Materials Science — Materials Chemistry

Fluctuation stabilization of the <i>Fddd</i> network phase in diblock, triblock, and starblock copolymer melts The latest complex network phase to be discovered in diblock copolymer melts is the orthorhombic $F\phantom{\rule{0}{0ex}}d\phantom{\rule{0}{0ex}}d\phantom{\rule{0}{0ex}}d$ phase. Mean-field theory predicts it to be stable, but only at weak segregations where ordered phases are typically destroyed by thermal fluctuations. Indeed, Landau-Brazovskii theory confirmed this expectation, raising the question of how $F\phantom{\rule{0}{0ex}}d\phantom{\rule{0}{0ex}}d\phantom{\rule{0}{0ex}}d$ survives in experiments. However, this problem was recently resolved by accurate field-theoretic simulations, which found that $F\phantom{\rule{0}{0ex}}d\phantom{\rule{0}{0ex}}d\phantom{\rule{0}{0ex}}d$ is simply more resilient to fluctuations than other ordered phases. Here, the authors find that this is also true for the family of (AB)${}_{M}$ starblock copolymer architectures. This resilience may very well extend to numerous other architectures, and thus it would be prudent to keep our eyes open for $F\phantom{\rule{0}{0ex}}d\phantom{\rule{0}{0ex}}d\phantom{\rule{0}{0ex}}d$. Orthorhombic crystal system ; Copolymer ; Imaging phantom ; Condensed matter physics ; Materials science ; Phase (matter) ; Mean field theory ; Physics ; Thermodynamics ; Quantum mechanics ; Nuclear magnetic resonance ; Optics Block Copolymer Self-Assembly ; Organic Electronics and Photovoltaics ; Material Dynamics and Properties
Authors: Ali Shalbafan | Heiko Thoemen
Source: Polymers (article, 2023-02-05)
Field: Materials Science — Materials Chemistry

Development of Mineral-Bonded Plywood with Magnesium Oxychloride as a Binder Using the Hot-Pressing Process Environmentally friendly plywood panels were produced by a hot-pressing process using magnesium oxychloride cement (MOC) as a no-added formaldehyde adhesive. Magnesium oxychloride cement binders were prepared with different molar ratios of MgO:MgCl<sub>2</sub> (M/C) and H<sub>2</sub>O:MgCl<sub>2</sub> (W/C) ranging from 6 to 12 and 15 to 21, respectively, for plywood production. The binder properties measured were gel time, differential scanning calorimetry (DSC) and Fourier transom infrared spectroscopy (FTIR). The quality of the plywood panels was analyzed based on their mechanical (shear and bending) and physical (thickness swelling and water absorption) properties. A positive effect on the properties of the MOC binder as well as on the properties of the plywood was observed by increasing the molar ratio M/C up to a value of 9. The shear and flexural properties of the plywood specimens were negatively affected by further increasing the molar ratio M/C to 12 and the molar ratio W/C from 15 to 21. Differential scanning calorimetry analysis showed a peak temperature of less than 100 °C for MOC curing, which meets the requirements of hot press technology. No delamination of the plywood specimens was observed after 24 h immersion in tap water or 6 h immersion in boiling water and after a cyclic delamination test. In general, mineral-bonded plywood with magnesium oxychloride shows promising properties for indoor and outdoor use, although the binder quality should still be improved. Materials science ; Differential scanning calorimetry ; Flexural strength ; Absorption of water ; Composite material ; Adhesive ; Fourier transform infrared spectroscopy ; Magnesium ; Curing (chemistry) ; Swelling ; Chemical engineering ; Metallurgy Magnesium Oxide Properties and Applications ; Recycling and utilization of industrial and municipal waste in materials production ; Concrete and Cement Materials Research
Authors: Shon Cohen | Dina Schneidman‐Duhovny
Source: PROTEOMICS (article, 2023-04-18)
Field: Chemistry — Spectroscopy

A deep learning model for predicting optimal distance range in crosslinking mass spectrometry data Macromolecular assemblies play an important role in all cellular processes. While there has recently been significant progress in protein structure prediction based on deep learning, large protein complexes cannot be predicted with these approaches. The integrative structure modeling approach characterizes multi-subunit complexes by computational integration of data from fast and accessible experimental techniques. Crosslinking mass spectrometry is one such technique that provides spatial information about the proximity of crosslinked residues. One of the challenges in interpreting crosslinking datasets is designing a scoring function that, given a structure, can quantify how well it fits the data. Most approaches set an upper bound on the distance between Cα atoms of crosslinked residues and calculate a fraction of satisfied crosslinks. However, the distance spanned by the crosslinker greatly depends on the neighborhood of the crosslinked residues. Here, we design a deep learning model for predicting the optimal distance range for a crosslinked residue pair based on the structures of their neighborhoods. We find that our model can predict the distance range with the area under the receiver-operator curve of 0.86 and 0.7 for intra- and inter-protein crosslinks, respectively. Our deep scoring function can be used in a range of structure modeling applications. Range (aeronautics) ; Deep learning ; Mass spectrometry ; Function (biology) ; Computer science ; Biological system ; Macromolecule ; Protein structure prediction ; Algorithm ; Artificial intelligence ; Chemistry ; Protein structure ; Data mining ; Materials science ; Biology ; Chromatography Mass Spectrometry Techniques and Applications ; Enzyme Structure and Function ; Protein Structure and Dynamics
Authors: Martha Freire da Silva | Jussara V. Roque | Júlia Martins Soares | Lorena de Oliveira Moura | André Dantas de Medeiros | Felipe Lopes da Silva | Laércio Junio da Silva
Source: Ciência Agronômica/Revista ciência agronômica (article, 2023-09-29)
Field: Chemistry — Analytical Chemistry

Near infrared spectroscopy for the classification of vigor level of soybean seed This work aimed at investigating the viability of near infrared spectrometry (NIR), associated with chemometric methods, in order to identify differences at the levels of vigor of naturally and artificially aged soybean seeds. Seeds of six soybean genotypes were analyzed when freshly harvested, after natural aging in storage for eight months, and after artificial aging at the temperature of 41 °C for 96 hours. The seed moisture content, germination potential and vigor were evaluated. Also, there were measured the content of protein, oil and of the fatty acids: palmitic, stearic, oleic, linoleic and linolenic. The NIR spectra were obtained from the freeze-dried and grinded seeds. The natural and artificial aging of the seeds promote deterioration at distinct levels, reflecting in differences in seed vigor. The regions of the electromagnetic spectrum between wavelengths of 1000-1200 nm, 1350-1450 nm, 1800-1900 nm and 2300-2400 nm are important to distinguish the levels of quality of the soybean seeds. The contents of oil and protein have a relationship with the physiological quality of the seeds. Also, the most relevant wavelengths for the classification of seed vigor present a relationship with these compounds. NIR spectroscopy, in combination with chemometric methods, presents potential to be used in the classification of soybean seeds regarding their physiological quality. Germination ; Water content ; Chemistry ; Horticulture ; Linolenic acid ; Food science ; Near-infrared spectroscopy ; Soybean oil ; Moisture ; Linoleic acid ; Botany ; Biology ; Fatty acid Spectroscopy and Chemometric Analyses ; Soybean genetics and cultivation ; Food composition and properties
Authors: Elena Trunz | Jonathan Klein | Jan Müller | Lukas Bode | Ralf Sarlette | Michael Weinmann | Reinhard Klein
Source: Computers & Graphics (article, 2023-12-27)
Field: Materials Science — Polymers and Plastics

Neural inverse procedural modeling of knitting yarns from images We investigate the capabilities of neural inverse procedural modeling to infer high-quality procedural yarn models with fiber-level details from single images of depicted yarn samples. While directly inferring all parameters of the underlying yarn model based on a single neural network may seem an intuitive choice, we show that the complexity of yarn structures in terms of twisting and migration characteristics of the involved fibers can be better encountered in terms of ensembles of networks that focus on individual characteristics. We analyze the effect of different loss functions including a parameter loss to penalize the deviation of inferred parameters to ground truth annotations, a reconstruction loss to enforce similar statistics of the image generated for the estimated parameters in comparison to training images as well as an additional regularization term to explicitly penalize deviations between latent codes of synthetic images and the average latent code of real images in the encoder’s latent space. We demonstrate that the combination of a carefully designed parametric, procedural yarn model with respective network ensembles as well as loss functions even allows robust parameter inference when solely trained on synthetic data. Since our approach relies on the availability of a yarn database with parameter annotations and we are not aware of such a respectively available dataset, we additionally provide, to the best of our knowledge, the first dataset of yarn images with annotations regarding the respective yarn parameters. For this purpose, we use a novel yarn generator that improves the realism of the produced results over previous approaches. Yarn ; Computer science ; Artificial intelligence ; Artificial neural network ; Inference ; Parametric statistics ; Synthetic data ; Regularization (linguistics) ; Ground truth ; Focus (optics) ; Scalability ; Pattern recognition (psychology) ; Algorithm ; Mathematics ; Statistics Textile materials and evaluations ; Industrial Vision Systems and Defect Detection ; Computer Graphics and Visualization Techniques
Authors: Mahmud Atarianshandiz | Kimberley B. McAuley | Akbar Shahsavand
Source: Processes (article, 2023-09-27)
Field: Chemistry — Inorganic Chemistry

Modeling and Parameter Tuning for Continuous Catalytic Reforming of Naphtha in an Industrial Reactor System A two-dimensional mathematical model was developed to simulate naphtha reforming in a series of three industrial continuous catalytic regeneration (CCR) reactors. Discretization of the resulting partial differential equations (PDEs) in the vertical direction and a coordinate transformation in the radial direction were performed to make the model solvable using Aspen Custom Modeler. A sensitivity-based parameter subset selection method was employed to identify the most influential parameters within the model. Tuning of 8 out of 180 parameters was used to ensure that model predictions match experimental data from one steady-state run. The updated parameter values improved the model fit to the data, reducing the weighted least-squares objective function for parameter estimation by 73%. The proposed model was used to predict reactor temperatures, catalyst coke weight fraction at the exit of the third reactor, and benzene flowrate from the outlet of the third reactor. The simulation results demonstrated a good agreement between the simulated values and the industrial measurements. Finally, the reactor model was utilized to explore the effects of changes in inlet temperatures and inlet level of catalyst deactivation, providing valuable insights for identifying desirable operational conditions that will improve the overall efficiency of the CCR process. Naphtha ; Discretization ; Sensitivity (control systems) ; Estimation theory ; Process engineering ; Inlet ; Process (computing) ; Catalytic reforming ; Computer science ; Control theory (sociology) ; Mathematics ; Catalysis ; Engineering ; Chemistry ; Mechanical engineering ; Algorithm Zeolite Catalysis and Synthesis ; Catalysis and Hydrodesulfurization Studies ; Catalysts for Methane Reforming
Authors: Pu Liu | Quanlin Xiao | Ning Dai | Zhongbin Liu | Chenlong Wang
Source: ACS Omega (article, 2023-04-04)
Field: Chemistry — Analytical Chemistry

Study on Pyrolysis of Shale Gas Oil-Based Drilling Cuttings: Kinetics, Process Parameters, and Product Yield The main reaction range (350-550 °C) of oil-based drilling cutting (OBDC) pyrolysis was studied by a thermogravimetric analyzer and a vacuum tube furnace. The average activation energies calculated by four model-free methods were 185.5 kJ/mol (FM), 184.16 kJ/mol (FWO), 166.17 kJ/mol (KAS), and 176.03 kJ/mol (Starink). The reaction mechanism was predicted by the Criado (Z-master plot) method. It is found that a high heating rate is helpful to predict the reaction mechanism, but it cannot be described by a single reaction model. Under the conditions of target temperature higher than 350 °C, residence time higher than 50 min, laying thickness less than 20 mm, and heating rate lower than 15 °C, the residual oil content is lower than 0.3% and the recovery rate of mineral oil is higher than 98.43%. Solid phase products accounted for more than 70%, reached the maximum 17.04% at 450 °C, and then decreased to 15.87% at 500 °C. Aromatic hydrocarbons, as coking precursors, are transformed from a low ring to a high ring. Recycled mineral oil can reconfigure oil-based mud (OBM). The research results can provide a theoretical basis for process optimization. Pyrolysis ; Thermogravimetric analysis ; Yield (engineering) ; Oil shale ; Residence time (fluid dynamics) ; Materials science ; Chemistry ; Tube furnace ; Analytical Chemistry (journal) ; Chemical engineering ; Waste management ; Organic chemistry ; Metallurgy ; Geology Petroleum Processing and Analysis ; Thermal and Kinetic Analysis ; Thermochemical Biomass Conversion Processes