jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Dongxiao Wang | Qiangqiang Zhu | Zhenxiong Wei | Baosen Lin | Yi Jing | Yuan Shi | R.D.K. Misra | Jianping Li
Source: SSRN Electronic Journal (article, 2021-01-01)
Field: Materials Science — Biomaterials

Hot Deformation Behaviors of AZ91 Magnesium Alloy: Constitutive Equation, Ann-Based Prediction, Processing Map and Microstructure Evolution Microstructure ; Constitutive equation ; Magnesium alloy ; Materials science ; Deformation (meteorology) ; Metallurgy ; Evolution equation ; Alloy ; Composite material ; Structural engineering ; Engineering ; Mathematics ; Finite element method ; Mathematical analysis Magnesium Alloys: Properties and Applications ; Aluminum Alloys Composites Properties ; Aluminum Alloy Microstructure Properties
Authors: Chao Li | Yingming Song | Zehuan Zhang | Jie Mao | Weiwei Yuan | Bo Wang
Source: Science and Technology of Nuclear Installations (article, 2021-01-25)
Field: Materials Science — Materials Chemistry

A Novel and High-Precision Method for Calculating the γ-Ray Build-Up Factor for Multilayer Shields In the field of radiation protection, the point-kernel code method is a practical tool widely used in the calculation of 3-D radiation field, and the accuracy of the point-kernel integration method strongly depends on the accuracy of the build-up factor. It is well known that calculation of the build-up factor for single-layer shields is composed of single material, but it is very complicated to calculate the build-up factor for multilayer shields (MLBUF). Recently, a novel and high-precision method based on the deep neural network (DNN) for calculating MLBUF has been proposed. In this paper, the novel method is described completely by slab models. Through the study of photon transport in multilayer shields, the parameters that mainly affect the calculation of build-up factor are analyzed. These parameters are trained by DNN as the input vectors, and the build-up factor for multilayer shields is predicted based on the trained DNN. The results predicted by DNN confirm that the method can calculate the build-up factor for multilayer shields quickly and accurately. The method has been preliminarily applicated into a 3-D radiation field calculation software, and it has proved that the method for calculating MLBUF has a broad application prospects in 3-D radiation field calculation. Shields ; Kernel (algebra) ; Field (mathematics) ; Slab ; Point (geometry) ; Artificial neural network ; Computer science ; Factor (programming language) ; Algorithm ; Electronic engineering ; Engineering ; Structural engineering ; Artificial intelligence ; Mathematics ; Geometry ; Electromagnetic shielding ; Electrical engineering Radiation Shielding Materials Analysis ; Advanced X-ray and CT Imaging ; Advanced Radiotherapy Techniques
Authors: Hasan Kurban | Mustafa Kurban | Parichit Sharma | Mehmet Dalkılıç
Source: Key engineering materials (article, 2021-03-01)
Field: Materials Science — Materials Chemistry

Predicting Atom Types of Anatase TiO&lt;sub&gt;2&lt;/sub&gt; Nanoparticles with Machine Learning Machine learning (ML) has recently made a major contribution to the fields of Material Science (MS). In this study, ML algorithms are used to learn atoms types over structural geometrical data of anatase TiO 2 nanoparticles produced at different temperature levels with the density-functional tight-binding method (DFTB). Especially for this work, Random Forest (RF), Decision Trees (DT), K-Nearest Neighbor (KNN), Naïve Bayes (NB), which are among the most popular ML algorithms, were run to learn titanium (Ti) and oxygen (O) atoms. RF outperforms other algorithms, almost succeeding in learning this skewed data set close to perfect. The use of ML algorithms with datasets compatible with its mathematical design increases their learning performance. Therefore, we find it remarkable that a certain type of ML algorithm performs almost perfectly. Because it can help material scientists predict the behavior and structural and electronic properties of atoms at different temperatures. Anatase ; k-nearest neighbors algorithm ; Atom (system on chip) ; Random forest ; Nanoparticle ; Algorithm ; Naive Bayes classifier ; Machine learning ; Materials science ; Artificial intelligence ; Titanium ; Set (abstract data type) ; Computer science ; Nanotechnology ; Chemistry ; Support vector machine ; Metallurgy Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Text and Document Classification Technologies
Authors: Benjamin Brunel | Fatima Alsamad | Olivier Piot
Source: Chemometrics and Intelligent Laboratory Systems (article, 2021-10-28)
Field: Chemistry — Analytical Chemistry

Toward automated machine learning in vibrational spectroscopy: Use and settings of genetic algorithms for pre-processing and regression optimization Computer science ; Artificial intelligence ; Smoothing ; Machine learning ; Algorithm ; Estimator ; Data processing ; Mathematics ; Statistics ; Computer vision Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Fault Detection and Control Systems
Authors: Krzysztof Jarosz | Eneko Ukar | Alexander Krödel | Tuğrul Özel
Source: The International Journal of Advanced Manufacturing Technology (article, 2021-09-08)
Field: Materials Science — Materials Chemistry

Laser ablation and processing of polycrystalline cubic boron nitride cutting tool material Materials science ; Boron nitride ; Machining ; Laser ; Laser ablation ; Surface integrity ; Tool wear ; Surface roughness ; Tungsten carbide ; Ablation ; Surface finish ; Metallurgy ; Composite material ; Optics ; Engineering Diamond and Carbon-based Materials Research ; Advanced machining processes and optimization ; Metal and Thin Film Mechanics
Authors: Aline Zanchet | Francisco Maciel Monticeli | Fabiula Danielli Bastos de Sousa | Heitor Luiz Ornaghi
Source: Cleaner Engineering and Technology (article, 2021-10-22)
Field: Materials Science — Polymers and Plastics

Experimental and artificial neural network approach for prediction of the thermal degradation behavior of sugarcane-based vulcanization additives in natural rubber compounds The use of natural additives in elastomeric compounds is gaining the special attention of researchers and industry due to their potential applications as environmentally friendly compounds and lower cost-related. Another important issue is the use of powerful mathematical tools to predict experimental results, which is crucial for saving cost and time. Artificial neural network (ANN) combined with other mathematical methods, such as surface response methodology (SRM), can guarantee reliability and faster response of the predicted data for similar materials or properties. The great advantage of the present method is the fast prediction of the analyzed property, in the present case, thermal degradation curves, at heating rates not experimentally tested. In this study, a modified activator from sugarcane bagasse was incorporated in different concentrations in natural rubber compounds, and the degradation behavior was simulated by ANN and SRM based on the experimental thermal degradation curves at different heating rates from the thermogravimetric analysis. The simulated results showed an outstanding agreement with the experimental ones, evidencing the importance of using ANN and SRM tools in the prediction of properties of elastomeric compounds. Vulcanization ; Artificial neural network ; Natural rubber ; Elastomer ; Degradation (telecommunications) ; Biological system ; Thermogravimetric analysis ; Thermal ; Materials science ; Petrochemical ; Computer science ; Bagasse ; Redundancy (engineering) ; Process engineering ; Biochemical engineering ; Environmentally friendly ; Environmental science ; Pulp and paper industry ; Chemical engineering ; Composite material ; Engineering ; Machine learning ; Environmental engineering ; Thermodynamics Polymer Nanocomposites and Properties ; Lignin and Wood Chemistry ; Plant biochemistry and biosynthesis
Authors: Jun Zhang | Yukun Luo | Zilong Tao | Jie You
Source: Applied Optics (article, 2021-06-07)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Graphic-processable deep neural network for the efficient prediction of2D diffractive chiral metamaterials We propose a novel, to the best of our knowledge, graphic-processable deep neural network (DNN) to automatically predict and elucidate the optical chirality of two-dimensional (2D) diffractive chiral metamaterials. Four classes of 2D chiral metamaterials are studied here, with material components changing among Au, Ag, Al, and Cu. The graphic-processable DNN algorithm can not only handle arbitrary 2D images representing any metamaterials that may even go beyond human intuition, but also capture the influence of other parameters such as thickness and material composition, which are rarely explored in the field of metamaterials, laying the groundwork for future research into more complicated nanostructures and nonlinear optical devices. Notably, the rigorous coupled wave analysis (RCWA) algorithm is first deployed to calculate circular dichroism (CD) in the higher-order diffraction beams and simultaneously promote the training of DNN. For the first time we creatively encode the material component and thickness of the metamaterials into the color images serving as input of the graphic-processable DNN, in addition to arbitrary graphical parameters. Especially, the smallest intensity is found in the third-order diffraction beams of E-like metamaterials, whose CD response turns out to be the largest. A comprehensive study is conducted to capture the influence of shape, unit period, thickness, and material component of arrays on chiroptical response. As expected, a satisfied precision and an accelerated computing speed that is 4 orders of magnitude quicker than RCWA are both achieved using DNN. This work belongs to one of the first attempts to thoroughly examine the generalization ability of the graphic-processable DNN for the study of arbitrary-shaped nanostructures and hypersensitive nanodevices. Metamaterial ; Diffraction ; Rigorous coupled-wave analysis ; Optics ; Materials science ; Artificial neural network ; Computer science ; Diffraction efficiency ; Physics ; Diffraction grating ; Artificial intelligence Metamaterials and Metasurfaces Applications ; Plasmonic and Surface Plasmon Research ; Plant and Fungal Species Descriptions
Authors: Joy P. Dunkers | Hariharan Iyer | Brynna H. Jones | Charles H. Camp | Stephan J. Stranick | Nancy J. Lin
Source: Journal of Biophotonics (article, 2021-09-12)
Field: Chemistry — Electrochemistry

Toward absolute viability measurements for bacteria We aim to develop a quantitative viability method that distinguishes individual quiescent from dead cells and is measured in time (ns) as a referenceable, comparable quantity. We demonstrate that fluorescence lifetime imaging of an anionic, fluorescent membrane voltage probe fulfills these requirements for Streptococcus mutans. A random forest machine-learning model assesses whether individual S. mutans can be correctly classified into their original populations: stationary phase (quiescent), heat killed and inactivated via chemical fixation. We compare the results to intensity using three models: lifetime variables (τ<sub>1</sub> , τ<sub>2</sub> and p<sub>1</sub> ), phasor variables (G, S) or all five variables, with the five variable models having the most accurate classification. This initial work affirms the potential for using fluorescence lifetime of a membrane voltage probe as a viability marker for quiescent bacteria, and future efforts on other bacterial species and fluorophores will help refine this approach. Fluorescence ; Bacteria ; Biological system ; Random forest ; Membrane ; Phasor ; Voltage ; Chemistry ; Biology ; Computer science ; Biochemistry ; Machine learning ; Optics ; Physics ; Thermodynamics Electrochemical Analysis and Applications ; Lipid Membrane Structure and Behavior ; Bacterial biofilms and quorum sensing
Authors: Michał Maciusowicz | Grzegorz Psuj | Paweł Kochmański
Source: Materials (article, 2021-12-24)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Identification of Grain Oriented SiFe Steels Based on Imaging the Instantaneous Dynamics of Magnetic Barkhausen Noise Using Short-Time Fourier Transform and Deep Convolutional Neural Network This paper presents a new approach to the extraction and analysis of information contained in magnetic Barkhausen noise (MBN) for evaluation of grain oriented (GO) electrical steels. The proposed methodology for MBN analysis is based on the combination of the Short-Time Fourier Transform for the observation of the instantaneous dynamics of the phenomenon and deep convolutional neural networks (DCNN) for the extraction of hidden information and building the knowledge. The use of DCNN makes it possible to find even complex and convoluted rules of the Barkhausen phenomenon course, difficult to determine based solely on the selected features of MBN signals. During the tests, several samples made of conventional and high permeability GO steels were tested at different angles between the rolling and transverse directions. The influences of the angular resolution and the proposed additional prediction update algorithm on the DCNN accuracy were investigated, obtaining the highest gain for the angle of 3.6°, for which the overall accuracy exceeded 80%. The obtained results indicate that the proposed new solution combining time-frequency analysis and DCNN for the quantification of information from MBN having stochastic nature may be a very effective tool in the characterization of the magnetic materials. Barkhausen effect ; Convolutional neural network ; Fourier transform ; Materials science ; Noise (video) ; Artificial neural network ; Biological system ; Extraction (chemistry) ; Artificial intelligence ; Computer science ; Acoustics ; Pattern recognition (psychology) ; Nuclear magnetic resonance ; Physics ; Magnetic field ; Mathematics ; Chemistry ; Mathematical analysis ; Image (mathematics) Magnetic Properties and Applications ; Non-Destructive Testing Techniques ; Microstructure and Mechanical Properties of Steels
Authors: Catalin D. Spataru | Matthew Witman | Reese E. Jones
Source: Physical Review Materials (article, 2021-08-18)
Field: Materials Science — Materials Chemistry

Tuning the critical Li intercalation concentrations for <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:mi>Mo</mml:mi><mml:msub><mml:mi>X</mml:mi><mml:mn>2</mml:mn></mml:msub></mml:mrow></mml:math> (<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:mi>X</mml:mi><mml:mo>=</mml:mo><mml:mi mathvariant="normal">S</mml:mi></mml:mrow></mml:math>, Se, or Te) bilayer phase transitions Transition metal dichalcogenides, such as ${\mathrm{MoS}}_{2}$, are known to undergo a structural phase transformation as well as a change in the electronic conductivity upon Li intercalation. These properties make them candidates for charge-tunable ion-insertion materials that could be used in electrochemical devices. In this paper, we study the phase stability and electronic structure of H and T' Li-intercalated $\mathrm{Mo}{X}_{2}$ bilayers with X = S, Se, or Te. Using first-principles calculations in combination with classical and machine learning modeling approaches, we find that the H phase is more stable at low Li concentration for all X and the critical Li concentration at which the T'$\ensuremath{\rightarrow}\mathrm{H}$ transition occurs decreases with increasing mass of X. Furthermore, the relative free energy of the two phases becomes less sensitive to Li insertion with increasing atomic mass of the chalcogen atom X. While the electronic conductivity increases with increasing ion concentration at low concentrations, we do not observe a (positive) conductivity jump at the phase transition from H to T'. Materials science ; Intercalation (chemistry) ; Phase (matter) ; Phase transition ; Conductivity ; Atom (system on chip) ; Ion ; Doping ; Crystallography ; Physical chemistry ; Thermodynamics ; Physics ; Inorganic chemistry ; Chemistry 2D Materials and Applications ; Machine Learning in Materials Science ; Advanced Memory and Neural Computing
Authors: Daeseong Yong | Yeongsik Kim | Seungyun Jo | Du Yeol Ryu | Jaeup U. Kim
Source: Macromolecules (article, 2021-12-09)
Field: Materials Science — Materials Chemistry

Order-to-Disorder Transition of Cylinder-Forming Block Copolymer Films Confined within Neutral Interfaces We present the phase behavior of cylinder-forming block copolymer (BCP) films confined within neutral interfaces through theory and experiment. The Langevin field-theoretic simulation (L-FTS) is utilized to account for the compositional fluctuation effect on the order-to-disorder transition (ODT), and the ultraviolet divergence in the L-FTS result is eliminated by renormalization of the Flory–Huggins parameter χ, allowing accurate comparison of thickness-dependent (χN)ODT, where N is the polymerization index. The L-FTS is accelerated by utilizing a deep learning method, and we managed to achieve a 50–60% reduction in simulation time. The L-FTS results show that (χN)ODT decreases as the film thickness decreases at a very large value of the invariant polymerization index N̅, and BCPs undergo a phase transition from disordered to cylindrical phases through a spherical phase as predicted by the self-consistent field theory calculation. By performing the L-FTS at experimentally relevant low N̅, we show that the BCPs undergo a direct transition from disordered to cylindrical phases, and the (χN)ODT value of cylinder-forming BCPs confined within neutral interfaces increases as the film thickness decreases. To verify and complement the theoretical results, we investigate the ODT temperature (TODT) of cylinder-forming polystyrene-b-poly(2-vinylpyridine) (PS-b-P2VP) films supported on a neutral mat of P(S-r-2VP) random copolymers using grazing-incidence small-angle X-ray scattering. Our film experiments with low-N̅ PS-b-P2VP confirm the direct transition from disordered to cylindrical phases and the decrease in TODT with decreasing thickness below the onset thickness above which the TODT levels off. Copolymer ; Materials science ; Cylinder ; Polystyrene ; Phase transition ; Scattering ; Polymerization ; Flory–Huggins solution theory ; Phase (matter) ; Polymer chemistry ; Polymer ; Optics ; Condensed matter physics ; Physics ; Composite material ; Geometry ; Mathematics Block Copolymer Self-Assembly ; Theoretical and Computational Physics ; Material Dynamics and Properties
Authors: Xinqiang Ding | Bin Zhang
Source: Biophysical Journal (article, 2021-02-01)
Field: Materials Science — Materials Chemistry

Computing Absolute Free Energy with Deep Generative Models Computer science ; Sampling (signal processing) ; Energy (signal processing) ; Work (physics) ; Thermodynamic integration ; State (computer science) ; Mathematical optimization ; Algorithm ; Mathematics ; Mechanical engineering ; Statistics ; Engineering Machine Learning in Materials Science ; Protein Structure and Dynamics ; Computational Drug Discovery Methods
Authors: Petra Ranušová | Peter Nemeček | J. Lehotay | J Čiẑmárik
Source: Chemical Papers (article, 2021-01-19)
Field: Chemistry — Spectroscopy

QSRR modelling aimed on the HPLC retention prediction of dimethylamino- and pyrrolidino-substitued esters of alkoxyphenylcarbamic acid Chromatography ; Chemistry ; Acetonitrile ; Homologous series ; High-performance liquid chromatography ; Molecular descriptor ; Retention time ; Chromatographic separation ; Quantitative structure–activity relationship ; Organic chemistry ; Stereochemistry Analytical Chemistry and Chromatography ; Computational Drug Discovery Methods ; Analytical Methods in Pharmaceuticals
Authors: Victor García Satorras | Emiel Hoogeboom | Fabian B. Fuchs | Ingmar Posner | Max Welling
Source: arXiv (Cornell University) (preprint, 2021-05-19)
Field: Materials Science — Materials Chemistry

E(n) Equivariant Normalizing Flows This paper introduces a generative model equivariant to Euclidean symmetries: E(n) Equivariant Normalizing Flows (E-NFs). To construct E-NFs, we take the discriminative E(n) graph neural networks and integrate them as a differential equation to obtain an invertible equivariant function: a continuous-time normalizing flow. We demonstrate that E-NFs considerably outperform baselines and existing methods from the literature on particle systems such as DW4 and LJ13, and on molecules from QM9 in terms of log-likelihood. To the best of our knowledge, this is the first flow that jointly generates molecule features and positions in 3D. Equivariant map ; Invertible matrix ; Homogeneous space ; Mathematics ; Euclidean geometry ; Generative grammar ; Discriminative model ; Flow (mathematics) ; Graph ; Pure mathematics ; Computer science ; Discrete mathematics ; Artificial intelligence ; Geometry Machine Learning in Materials Science ; Topic Modeling ; Advanced Graph Neural Networks
Authors: Ali Riza Durmaz | Martin Müller | Bo Lei | Akhil Thomas | Dominik Britz | Elizabeth A. Holm | Christoph Eberl | Frank Mücklich | Peter Gumbsch
Source: Research Square (preprint, 2021-04-12)
Field: Materials Science — Materials Chemistry

A Deep Learning Approach for Complex Microstructure Inference Inference ; Artificial intelligence ; Deep learning ; Computer science ; Microstructure ; Materials science ; Metallurgy Machine Learning in Materials Science ; Mineral Processing and Grinding ; Industrial Vision Systems and Defect Detection
Authors: Emil Annevelink | Harley T. Johnson | Elif Ertekin
Source: Current Opinion in Solid State and Materials Science (article, 2021-01-19)
Field: Materials Science — Materials Chemistry

Pathways to controlled 3D deformation of graphene: Manipulating the motion of topological defects Curvature ; Computer science ; Topology (electrical circuits) ; Graphene ; Position (finance) ; Monte Carlo method ; Algorithm ; Materials science ; Nanotechnology ; Geometry ; Mathematics Graphene research and applications ; Machine Learning in Materials Science ; Advanced Memory and Neural Computing
Authors: Heba Abd El‐Aziz | Mona E. Fathy | Nahed El‐Enany | Fatma A. Aly | Manar M. Tolba
Source: Spectrochimica Acta Part A Molecular and Biomolecular Spectroscopy (article, 2021-02-09)
Field: Chemistry — Analytical Chemistry

Investigation of some univariate and multivariate spectrophotometric methods for concurrent estimation of Vancomycin and Ciprofloxacin in their laboratory prepared mixture and application to biological fluids Chromatography ; Analyte ; Partial least squares regression ; Spectrophotometry ; Univariate ; Chemometrics ; Calibration ; Dosage form ; Chemistry ; Second derivative ; Derivative (finance) ; Analytical Chemistry (journal) ; Multivariate statistics ; Mathematics ; Statistics Spectroscopy and Chemometric Analyses ; Analytical Methods in Pharmaceuticals ; Antibiotics Pharmacokinetics and Efficacy