jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Hanfeng Zhai | Jingjie Yeo
Source: International Journal of Applied Mechanics (article, 2023-03-17)
Field: Materials Science — Materials Chemistry

Multiscale Mechanics of Thermal Gradient Coupled Graphene Fracture: A Molecular Dynamics Study The thermo-mechanical coupling mechanism of graphene fracture under thermal gradients possesses rich applications whereas is hard to study due to its coupled non-equilibrium nature. We employ non-equilibrium molecular dynamics to study the fracture of graphene by applying a fixed strain rate under different thermal gradients by employing different potential fields. It is found that for AIREBO and AIREBO-M, the fracture stresses do not strictly follow the positive correlations with the initial crack length. Strain-hardening effects are observed for “REBO-based” potential models of small initial defects, which is interpreted as blunting effect observed for porous graphene. The temperature gradients are observed to not show clear relations with the fracture stresses and crack propagation dynamics. Quantized fracture mechanics verifies our molecular dynamics calculations. We provide a unique perspective that the transverse bond forces share the loading to account for the nonlinear increase of fracture stress with shorter crack length. Anomalous kinetic energy transportation along crack tips is observed for “REBO-based” potential models, which we attribute to the high interatomic attractions in the potential models. The fractures are honored to be more “brittle-liked” carried out using machine learning interatomic potential (MLIP), yet incapable of simulating post fracture dynamical behaviors. The mechanical responses using MLIP are observed to be not related to temperature gradients. The temperature configuration of equilibration simulation employing the dropout uncertainty neural network potential with a dropout rate of 0.1 is reported to be the most accurate compared with the rest. This work is expected to inspire further investigation of non-equilibrium dynamics in graphene with practical applications in various engineering fields. Graphene ; Molecular dynamics ; Materials science ; Fracture mechanics ; Temperature gradient ; Fracture (geology) ; Mechanics ; Thermal ; Dynamics (music) ; Statistical physics ; Composite material ; Physics ; Nanotechnology ; Thermodynamics Carbon Nanotubes in Composites ; Graphene research and applications ; Boron and Carbon Nanomaterials Research
Authors: Yupeng Wang
Source: Academic Journal of Computing & Information Science (article, 2023-01-01)
Field: Chemistry — Inorganic Chemistry

Study of relic classification based on neural network and K mean classification Over time, its own chemical elements will change in response to environmental changes, commonly known as weathering, which will lead to changes in the chemical composition ratios and affect the judgment of the class. This paper focuses on the chemical composition of different types of glass. By comparing the chemical indicators of each type, an identification model of the cultural relic category is established to predict the chemical content of the weathering process. To solve this, the neural network, chi-square test, and Spearman correlation analysis are used and the calculation results are given. According to the definition of sensitivity, after analysis, the accuracy was taken as the evaluation index, and the type of chemical composition was taken as the factor. K-value cluster analysis was performed for the type of chemical composition of the two glass categories in turn, and the accuracy was compared, and the accuracy of high potassium glass was close to 83%, and the accuracy of lead-barium glass was close to 69%, concluding that the type of chemical composition, which has little effect on the accuracy, has little sensitivity. In different categories, the variation of independent variables has a large impact on the accuracy and a large sensitivity. Finally, the advantages and disadvantages of the model are analyzed, and the model is extended to a larger domain. Chemical composition ; Sensitivity (control systems) ; Statistics ; Mathematics ; Artificial neural network ; Weathering ; Principal component analysis ; Pattern recognition (psychology) ; Biological system ; Artificial intelligence ; Chemistry ; Computer science ; Geology ; Engineering Pigment Synthesis and Properties
Authors: Gamal M. A. Mahran | A. M. Omran
Source: Heliyon (review, 2023-10-01)
Field: Materials Science — Materials Chemistry

Using a neural network model to evaluate the mechanical and tribological properties of vermicular cast iron based on hardness Measuring the tensile strength, wear resistance, and impact strength of metals, particularly cast iron, is complex and more expensive than performing hardness tests. In the present study, owing to the ease of specimen preparation and low cost, the Hardness (HB) test was used to approximately predict Wear Rate (WR), Impact Energy (IE), and tensile strength (TS). The relation between Mg% and HB, tensile strength, WR, and IE was examined by using three experimental groups of compacted graphite cast iron (CGI) treated with a nodulizer (Fe-Si-Mg) alloy at different carbon equivalents (CEs) of 3.5, 4.0, and 4.5 %. The produced CGI exhibited HB, TS, WR, and IE of 191-226 HB, 402-455 MPa, 30.1-23.8 mg/cm<sup>2</sup>, and 22-15 J, respectively. The good results were taken at a CE of 4.5 % and Mg content of 0.0118-0.0155 %. the regression analysis and artificial neural network model (ANNs) were used in the hardness test, and the results indicated the possibility of predicting IE, WR, tensile strength, and high accuracy Mg% of the produced CGI. It could be observed that, the neural network algorithm model has a high prediction precision for determining the Mg% content and the properties of the prepared CGI based on hardness. In the case of CE = 4, the MSE calculated for the predicted and measured data taken from the used ANNs model is 3.7 E-8, 20.33, 0.3084, and 0.099 for Mg%, TS, WR, and IE, respectively. Tribology ; Cast iron ; Artificial neural network ; Materials science ; Indentation hardness ; Metallurgy ; Composite material ; Computer science ; Artificial intelligence ; Microstructure Metal Alloys Wear and Properties ; Surface Treatment and Coatings ; Advanced machining processes and optimization
Authors: Stefano Sanvito
Source:  (article, 2023-10-22)
Field: Materials Science — Materials Chemistry

From the Periodic Table to New Magnets: Climbing the Inverse Design Mountain<sup>*</sup> The process of finding new materials, optimal for a given application, is lengthy, often unpredictable, and has a low throughput. Here I will describe a collection of numerical methods, merging advanced electronic structure theory and machine learning, for the discovery of novel compounds, which demonstrates an unprecedented throughput and discovery speed. This is applied here to magnetism, but it can be used for any materials class and potential application. Throughput ; Climbing ; Computer science ; Table (database) ; Class (philosophy) ; Magnet ; Magnetism ; Process (computing) ; Inverse ; Computational science ; Artificial intelligence ; Physics ; Engineering ; Electrical engineering ; Mathematics ; Data mining ; Quantum mechanics ; Telecommunications Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides ; X-ray Diffraction in Crystallography
Authors: Noah Hoffmann | Tiago F. T. Cerqueira | Pedro Borlido | Antonio Sanna | Jonathan Schmidt | Miguel A. L. Marques
Source: arXiv (Cornell University) (preprint, 2023-06-07)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Searching for ductile superconducting Heusler X2YZ compounds Heusler compounds have always attracted a great deal of attention from researchers thanks to a wealth of interesting properties for technological applications. They are intermetallic ductile compounds, and some of them have been found to be superconducting. With this in mind, we perform an extensive study of the superconducting and elastic properties of the cubic (full-)Heusler family. Starting from thermodynamically stable compounds, we use ab initio methods for the calculation of the phonon spectra, electron-phonon couplings, superconducting critical temperatures and elastic tensors. By analyzing the statistical distributions of these properties and comparing them to anti-perovskites we recognize universal behaviors that should be common to all conventional superconductors while others turn out to be specific to the material family. The resulting data is used to train interpretable and predictive machine learning models, that are used to extend our knowledge of superconductivity in Heuslers and to provide an interpretation of our results. In total, we discover a total of 8 hypothetical materials with critical temperatures above 10 K, to be compared with the current record of Tc = 4.7 K in this family. Furthermore, we expect most of these materials to be highly ductile, making them potential candidates for the manufacture of wires and tapes for superconducting magnets. Superconductivity ; Intermetallic ; Phonon ; Condensed matter physics ; Materials science ; Ab initio ; Interpretation (philosophy) ; Ab initio quantum chemistry methods ; Spectral line ; Physics ; Computer science ; Quantum mechanics ; Metallurgy Heusler alloys: electronic and magnetic properties ; Machine Learning in Materials Science ; Advanced Thermoelectric Materials and Devices
Authors: Rebecca M. Neeser | Clemens Isert | Thijs Stuyver | Gisbert Schneider | Connor W. Coley
Source: SSRN Electronic Journal (article, 2023-01-01)
Field: Materials Science — Materials Chemistry

Qmugs 1.1: Quantum Mechanical Properties of Organic Compounds Commonly Encountered in Reactivity Datasets Reactivity (psychology) ; Quantum ; Chemistry ; Computational chemistry ; Physics ; Quantum mechanics ; Medicine Machine Learning in Materials Science
Authors: Jens Timo Neumann | Abhilash Srikantha | Philipp Hüthwohl | Keumsil Lee | James William B. | Thomas Korb | E. Foca | Tomasz Garbowski | Daniel Boecker | Sayantan Das | Sandip Halder
Source: Journal of Micro/Nanopatterning Materials and Metrology (article, 2023-03-01)
Field: Materials Science — Surfaces, Coatings and Films

Defect detection and classification on imec iN5 node BEoL test vehicle with multibeam scanning electron microscope We present an automated application for defect detection and classification from ZEISS multibeam scanning electron microscope (MultiSEM®) images, based on machine learning (ML) technology. We acquire MultiSEM images of a semiconductor wafer suited for process window characterization at the imec iN5 logic node and use a dedicated application to train ML models for defect detection and classification. We show the user flow for training and execution, and the resulting capture and nuisance rates. Due to straightforward parallelization, the application is designed for the large amounts of data generated rapidly by the MultiSEM. Node (physics) ; Computer science ; Wafer ; Scanning electron microscope ; Process (computing) ; Artificial intelligence ; Embedded system ; Pattern recognition (psychology) ; Materials science ; Optoelectronics ; Operating system ; Acoustics ; Physics Electron and X-Ray Spectroscopy Techniques ; Industrial Vision Systems and Defect Detection ; Integrated Circuits and Semiconductor Failure Analysis
Authors: Adisa Jarubenjaluk | Pannapha Kullattanapratep | Apinporn Pornpipattanasiri | Kulpavee Jitapunkul | Pawin Iamprasertkun
Source: Chemical Engineering Journal Advances (article, 2023-08-13)
Field: Chemistry — Electrochemistry

“Crypton 1.0”: Accurate cyclic voltammetry forecasting of activated carbon electrode with machine learning Cyclic voltammetry (CV) is a technique for determining the electrochemical properties of the electrode, and electrolyte in electrochemical systems. However, it is sensitive to various feature, and the correlation between them is not fully explained hitherto. An artificial neural network (ANN) was employed to create a CVs prediction for further explain the electrochemical properties in “water-in-salt” electrolyte e.g., scan rate, electrolyte concentration, and potential window. The electrochemical assistant software based on the developed model are then present (namely “Crypton 1.0” beta version). The designed network architecture consists of two hidden layers with fixed number of neurons in the latter to reduce the calculation burden for loop training. Five-fold cross validation and single loop training with a variation of hidden neurons in the first hidden layer from 1 to 20 neurons were applied to generalize the prediction. The training was performed in batches corresponding to positive scan, negative scan, and full cycle scan to achieve comprehensive models. The final predictions are the product of averaged models with coefficient of determination (R2) over 0.98 for each scanning characteristic. Interestingly, the prediction for wide potential windows showed superior accuracy comparable with the CV from experimental measurement. In addition, the electrochemical stability window has been investigated, and found to be increased along with the electrolyte concentration explaining the concepts of using “water-in-salt” electrolyte. This work covers the understanding of electrochemistry and the model development via ANN for software development. Therefore, our work could be an alternative approach to reduce the experimental burden in the future development of electrochemical applications. Electrolyte ; Cyclic voltammetry ; Horizontal scan rate ; Electrochemistry ; Artificial neural network ; Electrode ; Biological system ; Computer science ; Software ; Correlation coefficient ; Electrochemical window ; Materials science ; Artificial intelligence ; Analytical Chemistry (journal) ; Chemistry ; Machine learning ; Chromatography Electrochemical Analysis and Applications ; Conducting polymers and applications ; Analytical Chemistry and Sensors
Authors: Laura Mismetti | Max Hodapp
Source: arXiv (Cornell University) (preprint, 2023-11-03)
Field: Materials Science — Materials Chemistry

Automated atomistic simulations of dissociated dislocations with ab initio accuracy In (M Hodapp and A Shapeev 2020 Mach. Learn.: Sci. Technol. 1 045005), we have proposed an algorithm that fully automatically trains machine-learning interatomic potentials (MLIPs) during large-scale simulations, and successfully applied it to simulate screw dislocation motion in body-centered cubic tungsten. The algorithm identifies local subregions of the large-scale simulation region where the potential extrapolates, and then constructs periodic configurations of 100--200 atoms out of these non-periodic subregions that can be efficiently computed with plane-wave Density Functional Theory (DFT) codes. In this work, we extend this algorithm to dissociated dislocations with arbitrary character angles and apply it to partial dislocations in face-centered cubic aluminum. Given the excellent agreement with available DFT reference results, we argue that our algorithm has the potential to become a universal way of simulating dissociated dislocations in face-centered cubic and possibly also other materials, such as hexagonal closed-packed magnesium, and their alloys. Moreover, it can be used to construct reliable training sets for MLIPs to be used in large-scale simulations of curved dislocations. Dislocation ; Cubic crystal system ; Ab initio ; Materials science ; Interatomic potential ; Partial dislocations ; Scale (ratio) ; Motion (physics) ; Face (sociological concept) ; Work (physics) ; Algorithm ; Statistical physics ; Computer science ; Physics ; Molecular dynamics ; Condensed matter physics ; Classical mechanics ; Quantum mechanics Machine Learning in Materials Science ; Advanced Materials Characterization Techniques ; Microstructure and mechanical properties
Authors: Olexiy Eduardovich Narivs’kyi
Source: Physical Sciences and Technology (article, 2023-12-01)
Field: Materials Science — General Materials Science

Corrosion behavior of austenitic steels in chloride-containing media during the operation of plate-like heat exchangers Mathematical models that describe the dependences of the critical temperatures of pitting formation of AISI 304, 08Kh18N10, AISI 321, 12Kh18N10T steels in model circulating waters with pH 4…8 and chloride concentrations from 350 to 600 mg/l on their chemical composition and structure have been developed. They are based on linear squares regressions and on a feed-forward neural network for reduced feature numbers. Using the developed mathematical models, it was found that the critical pitting temperatures of these steels increase with an increase in the pH of the circulating water, the number of oxides up to 3.95 μm in size, the average distance between titanium nitrides, the Cr content and a decrease in the concentration of chlorides in the circulating waters, the average distance between oxides and average austenite grain diameter. At the same time, it was found that the geometric dimensions of the steel structure most intensively affect their pitting resistance in circulating waters, and the effect of their chemical composition is minimal and is determined by the amount of Cr, which contributes to an increase in the pitting resistance of steels, probably increasing the solubility of nitrogen in the austenite solid solution. It is proposed to use the developed mathematical models to select the optimal heats of these steels for the production of heat exchangers and predict their pitting resistance during their operation in circulating waters. Austenite ; Heat exchanger ; Corrosion ; Metallurgy ; Materials science ; Chloride ; Engineering ; Mechanical engineering ; Microstructure Material Properties and Applications ; Material Properties and Failure Mechanisms
Authors: Cristiana Di Valentin
Source:  (article, 2023-10-22)
Field: Materials Science — Materials Chemistry

Computational modeling of complex nanosystems for drug delivery, targeting and imaging<sup>*</sup> Inorganic nanoparticles are excellent tools to create smart bioinorganic nanodevices for drug delivery, targeting and imaging. To investigate these complex systems, we use multiscale approaches ranging from first-principles calculations to atomistic molecular dynamics simulations, metadynamics, umbrella sampling and machine learning. Metadynamics ; Drug delivery ; Molecular dynamics ; Nanotechnology ; Molecular imaging ; Computer science ; Nanoparticle ; Materials science ; Chemistry ; Computational chemistry Machine Learning in Materials Science ; Nanoparticle-Based Drug Delivery ; Advanced Electron Microscopy Techniques and Applications
Authors: Abdus Sattar | Md. Asif Mahmud Ridoy | Aloke Kumar Saha | Hafiz Md. Hasan Babu | Mohammad Nurul Huda
Source: SSRN Electronic Journal (preprint, 2023-01-01)
Field: Chemistry — Analytical Chemistry

Computer Vision Based Deep Learning Approach for Toxic and Harmful Substances Detection in Fruits Deep learning ; Artificial intelligence ; Computer science ; Machine learning Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI
Authors: Xun Liu | Dabao Lu | Zhufeng Hou | Kenji Nagata | Bo Da | Hideki Yoshikawa | Shigeo Tanuma | Yang Sun | Zejun Ding
Source: Physical Chemistry Chemical Physics (article, 2023-01-01)
Field: Materials Science — Surfaces, Coatings and Films

Establishment and validation of an electron inelastic mean free path database for narrow bandgap inorganic compounds with a machine learning approach Narrow bandgap inorganic compounds are extremely important in many areas of physics. However, their basic parameter database for surface analysis is incomplete. Electron inelastic mean free paths (IMFPs) are important parameters in surface analysis methods, such as electron spectroscopy and electron microscopy. Our previous research has presented a machine learning (ML) method to describe and predict IMFPs from calculated IMFPs for 41 elemental solids. This paper extends the use of the same machine learning method to 42 inorganic compounds based on the experience in predicting elemental electron IMFPs. The in-depth discussion extends to including material dependence discussion and parameter value selections. After robust validation of the ML method, we have produced an extensive IMFP database for 12 039 narrow bandgap inorganic compounds. Our findings suggest that ML is very efficient and powerful for IMFP description and database completion for various materials and has many advantages, including stability and convenience, over traditional methods. Band gap ; Path (computing) ; Mean free path ; Inelastic mean free path ; Electron ; Materials science ; Computer science ; Chemistry ; Physics ; Optoelectronics ; Nuclear physics Electron and X-Ray Spectroscopy Techniques ; Corrosion Behavior and Inhibition ; Machine Learning in Materials Science
Authors: Li Zhao | Soumil Y. Joshi | Yin Wang | Sanket A. Deshmukh | John B. Matson
Source: Angewandte Chemie (article, 2023-05-17)
Field: Materials Science — Biomaterials

Supramolecular Peptide Nanostructures Regulate Catalytic Efficiency and Selectivity Abstract We report three constitutionally isomeric tetrapeptides, each comprising one glutamic acid (E) residue, one histidine (H) residue, and two lysine (K S ) residues functionalized with side‐chain hydrophobic S ‐aroylthiooxime (SATO) groups. Depending on the order of amino acids, these amphiphilic peptides self‐assembled in aqueous solution into different nanostructures:nanoribbons, a mixture of nanotoroids and nanoribbons, or nanocoils. Each nanostructure catalyzed hydrolysis of a model substrate, with the nanocoils exhibiting the greatest rate enhancement and the highest enzymatic efficiency. Coarse‐grained molecular dynamics simulations, analyzed with unsupervised machine learning, revealed clusters of H residues in hydrophobic pockets along the outer edge of the nanocoils, providing insight for the observed catalytic rate enhancement. Finally, all three supramolecular nanostructures catalyzed hydrolysis of the l ‐substrate only when a pair of enantiomeric Boc‐ l / d ‐Phe‐ONp substrates were tested. This study highlights how subtle molecular‐level changes can influence supramolecular nanostructures, and ultimately affect catalytic efficiency. Supramolecular chemistry ; Chemistry ; Residue (chemistry) ; Catalysis ; Amphiphile ; Selectivity ; Peptide ; Nanostructure ; Aqueous solution ; Hydrolysis ; Combinatorial chemistry ; Substrate (aquarium) ; Supramolecular catalysis ; Stereochemistry ; Organic chemistry ; Nanotechnology ; Materials science ; Molecule ; Polymer ; Copolymer ; Biochemistry Supramolecular Self-Assembly in Materials ; Chemical Synthesis and Analysis ; Advanced biosensing and bioanalysis techniques