jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: M. E. McMahon | Raymond Santucci | Carol Frances Glover | Balaji Kannan | Zachery R. Walsh | John R. Scully
Source: Frontiers in Materials (review, 2019-09-06)
Field: Materials Science — Materials Chemistry

A Review of Modern Assessment Methods for Metal and Metal-Oxide Based Primers for Substrate Corrosion Protection Pigmented coatings developed for the corrosion protection of engineering alloys have been successful in the field of corrosion control, and thus are ubiquitous in application. Supporting this success are an array of methods to diagnostically assess these systems and measure their state of degradation. These methods assist the development of next generation coatings, in-service evaluation, and post-mortem analysis of performance. This review comprehensively explores these modern assessment methods, used to screen candidate corrosion prevention pigments, assess pigment protection efficiency, quantify overall pigment effectiveness, and/or predict coating service lifetime. Coating service life assessment is conducted based on the class of coating considered, as categorized by (1) chemical inhibition, (2) galvanic protection, (3) high impedance ion barrier, or a combination of these protection mechanisms. Exciting new in-situ and operando methods are growing in use to enable high fidelity measurement of chemical inhibitor release and deposition, such as micro and in-situ Raman spectroscopy and inductively coupled plasma atomic emission spectrometry (ICP-AES), to name a few. In the galvanic protection coating class, service life and the state of coating performance are now being quantified through methods such as galvanostatic pulse and accelerated cycle testing. Galvanic throwing power is assessed by scanning vibrating electrode technique (SVET), multi-electrode array (MEA), and scanning kelvin probe (SKP). The performance of high impedance ion barrier coatings has traditionally been understood through use of electrochemical impedance spectroscopy (EIS) and equivalent circuit modeling, which have been applied to all coating classes. Finite element analysis (FEA) models the combined effects of coating resistance, water layer thickness, and pigment potential on galvanic coupling and/or inhibitor species release as well. The development of effective corrosion prevention pigments and the in-service evaluation of their efficiency/effectiveness demands a broad collection of techniques. These methods now include new advances in thermodynamic modeling via chemical stability diagrams to assess pigment function in specific conditions, in-situ pH measurement, as well as energy dispersive (EDS) and x-ray photoelectron spectroscopic (XPS) analyses to determine elemental distribution and corrosion product formation. These techniques and more are explored in this review to document the state-of-the-art in the field. Dielectric spectroscopy ; Coating ; Materials science ; Corrosion ; Galvanic cell ; Kelvin probe force microscope ; Nanotechnology ; Electrode ; Composite material ; Metallurgy ; Electrochemistry ; Chemistry Corrosion Behavior and Inhibition ; Concrete Corrosion and Durability ; Analytical Chemistry and Sensors
Authors: Filip Lisiecki | Justyna Rychły-Gruszecka | Piotr Kuświk | Hubert Głowiński | Jarosław W. Kłos | Felix Groß | Nick Träger | Iuliia Bykova | Markus Weigand | Mateusz Zelent | E. Goering | Gislea Schütz | Maciej Krawczyk | F. Stobiecki | J. Dubowik | Joachim Gräfe
Source: Physical Review Applied (article, 2019-05-22)
Field: Materials Science — Materials Chemistry

Magnons in a Quasicrystal: Propagation, Extinction, and Localization of Spin Waves in Fibonacci Structures Magnonic crystals (magnetic metamaterials for manipulating spin waves) are important building blocks for rf filters and logic. However, regular crystals pose significant constraints for complex designs. This study combines time-resolved x-ray microscopy and numerical modeling to show that quasiperiodic Fibonacci crystals provide advanced control of the magnonic band structure. Furthermore, x-ray microscopy yields insight into the quasicrystal's inner workings, and a simple analytical model is derived to facilitate application of these systems. Quasiperiodicity ; Quasicrystal ; Fibonacci number ; Quasiperiodic function ; Magnon ; Permalloy ; Magnonics ; Condensed matter physics ; Physics ; Spin wave ; Photonic crystal ; Band gap ; Metamaterial ; Spectral line ; Optics ; Quantum mechanics ; Ferromagnetism ; Magnetization ; Magnetic field ; Mathematics Quasicrystal Structures and Properties ; Magnetic properties of thin films ; Metamaterials and Metasurfaces Applications
Authors: A. P. Purnomoadi | Armando Rodrigo Mor | J.J. Smit
Source: International Journal of Electrical Power & Energy Systems (article, 2019-09-28)
Field: Materials Science — Materials Chemistry

Spacer flashover in Gas Insulated Switchgear (GIS) with humid SF6 under different electrical stresses Humid insulating gas (SF6) has been observed in a case study of 631 CB-bays of Gas Insulated Switchgear (GIS) operating under tropical conditions. The routine gas quality check in the case study reported that 20% of the non-Circuit Breaker enclosures have humidity above the value recommended by the IEEE and IEC standards. Therefore, an investigation into the flashover characteristics of a spacer in humid SF6 has been initiated in the High Voltage Laboratory of TU Delft, The Netherlands. The setup is a small model resembling the insulation system of a GIS with controlled parameters of humidity content and gas pressure. The electrical stresses in the test are AC, LI+, LI−, and SI with homogeneous, quasi-homogeneous-, and inhomogeneous field configurations. In general, the humidity does not influence the withstand strength of the spacer as long there is no condensation. When condensation occurred, the flashover voltage dropped by 28% during the test under AC at 2.6 bars, and by 38% during the test under LI+ at 2.5 bars; both with quasi-homogeneous field configuration. In the test with homogeneous field setup, the flashover voltage was dropped by 67% under LI+ at 3.4 bars. In our setup, it has also been observed that the flashover-drop due to condensation is higher than due to a 2-mm aluminum particle attached to the sample close to the high electric field region simulating the inhomogeneous field configuration. Switchgear ; Arc flash ; Sulfur hexafluoride ; Materials science ; Humidity ; Circuit breaker ; Dielectric withstand test ; Composite material ; Homogeneous ; Electric field ; High voltage ; Voltage ; Electrical engineering ; Environmental science ; Meteorology ; Chemistry ; Engineering ; Insulator (electricity) ; Thermodynamics ; Physics High voltage insulation and dielectric phenomena ; Power Transformer Diagnostics and Insulation ; Thermal Analysis in Power Transmission
Authors: Maxwell Fulford | Matteo Salvalaglio | Carla Molteni
Source: Journal of Chemical Information and Modeling (article, 2019-03-15)
Field: Materials Science — Materials Chemistry

DeepIce: A Deep Neural Network Approach To Identify Ice and Water Molecules Computer simulation studies of multiphase systems rely on the accurate identification of local molecular structures and arrangements in order to extract useful insights. Local order parameters, such as Steinhardt parameters, are widely used for this identification task; however, the parameters are often tailored to specific local structural geometries and generalize poorly to new structures and distorted or undercoordinated bonding environments. Motivated by the desire to simplify the process and improve the accuracy, we introduce DeepIce, a novel deep neural network designed to identify ice and water molecules, which can be generalized to new structures where multiple bonding environments are present. DeepIce demonstrates that the characteristics of a crystalline or liquid molecule can be classified using as input simply the Cartesian coordinates of the nearest neighbors without compromising the accuracy. The network is flexible and capable of inferring rotational invariance and produces a high predictive accuracy compared to the Steinhardt approach, the tetrahedral order parameter and polyhedral template matching in the detection of the phase of molecules in premelted ice surfaces. Computer science ; Artificial neural network ; Tetrahedron ; Identification (biology) ; Task (project management) ; Cartesian coordinate system ; Matching (statistics) ; Artificial intelligence ; Process (computing) ; Convolutional neural network ; Algorithm ; Liquid water ; Biological system ; Topology (electrical circuits) ; Geometry ; Mathematics ; Physics ; Engineering Machine Learning in Materials Science ; nanoparticles nucleation surface interactions ; Advanced Chemical Physics Studies
Authors: Jeffery A. Aguiar | Matthew L. Gong | Tolga Taşdizen
Source: Computational Materials Science (article, 2019-11-26)
Field: Materials Science — Materials Chemistry

Crystallographic prediction from diffraction and chemistry data for higher throughput classification using machine learning Computer science ; Data reduction ; Identification (biology) ; Data science ; Data mining ; Chemistry ; Machine learning Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Computational Drug Discovery Methods
Authors: Ke Duan | Yonglyu He | Yijun Li | Jiahuan Liu | Junjie Zhang | Yujin Hu | Rongming Lin | Xuelin Wang | Weiming Deng | Li Li
Source: Materials & Design (article, 2019-08-19)
Field: Materials Science — Materials Chemistry

Machine-learning assisted coarse-grained model for epoxies over wide ranges of temperatures and cross-linking degrees We present a practical computational framework for the coarse-graining of cross-linked epoxies by developing a machine-learning technique, which integrates molecular dynamics simulations with artificial neural network (ANN) assisted particle swarm optimization (PSO) algorithm. Key features of the framework include two aspects: (1) determining the bonded interactions via the iterative Boltzmann inversion method to emulate the local structures of the epoxies and, (2) optimizing the nonbonded interaction potentials through the machine-learning approach to reproduce the mechanical properties. Such machine-learning based technique is computationally efficient in searching for the optimal solution of nonbonded potential parameters and enables the CG model to become transferable within a wide range of cross-linking degrees. This is mainly attributed to the fact that ANN can give good predictions based on training database obtained from CG simulations and thus greatly accelerates the PSO algorithm in achieving the optimal solution. On the basis of the DOC-transferable CG model, the cohesive interaction strength is phenomenologically adjusted to preserve the temperature-dependent properties. The CG model allows the mechanical properties of cross-linked epoxies to be predicted with reasonable accuracy over wide ranges of cross-linking degrees and temperature. The proposed framework will become highly beneficial to the design of high performance epoxy-matrix nanocomposites. Keywords: Machine-learning approach, Cross-linked epoxy, Coarse-grained model, Molecular dynamics Particle swarm optimization ; Artificial neural network ; Materials science ; Computer science ; Range (aeronautics) ; Boltzmann machine ; Key (lock) ; Biological system ; Artificial intelligence ; Algorithm ; Composite material Machine Learning in Materials Science ; Epoxy Resin Curing Processes ; Block Copolymer Self-Assembly
Authors: Joshua Agar | Brett Naul | Shishir Pandya | Stéfan van der Walt | Joshua Maher | Yao Ren | Long‐Qing Chen | Sergei V. Kalinin | Rama K. Vasudevan | Ye Cao | J. S. Bloom | Lane W. Martin
Source: Nature Communications (article, 2019-10-22)
Field: Materials Science — Materials Chemistry

Revealing ferroelectric switching character using deep recurrent neural networks The ability to manipulate domains underpins function in applications of ferroelectrics. While there have been demonstrations of controlled nanoscale manipulation of domain structures to drive emergent properties, such approaches lack an internal feedback loop required for automatic manipulation. Here, using a deep sequence-to-sequence autoencoder we automate the extraction of latent features of nanoscale ferroelectric switching from piezoresponse force spectroscopy of tensile-strained PbZr<sub>0.2</sub>Ti<sub>0.8</sub>O<sub>3</sub> with a hierarchical domain structure. We identify characteristic behavior in the piezoresponse and cantilever resonance hysteresis loops, which allows for the classification and quantification of nanoscale-switching mechanisms. Specifically, we identify elastic hardening events which are associated with the nucleation and growth of charged domain walls. This work demonstrates the efficacy of unsupervised neural networks in learning features of a material's physical response from nanoscale multichannel hyperspectral imagery and provides new capabilities in leveraging in operando spectroscopies that could enable the automated manipulation of nanoscale structures in materials. Nanoscopic scale ; Ferroelectricity ; Materials science ; Nanotechnology ; Artificial intelligence ; Piezoresponse force microscopy ; Hysteresis ; Artificial neural network ; Autoencoder ; Computer science ; Optoelectronics ; Physics Machine Learning in Materials Science ; Ferroelectric and Negative Capacitance Devices ; Ferroelectric and Piezoelectric Materials
Authors: Lin Shang-Chun | Martin Oettel
Source: SciPost Physics (article, 2019-02-26)
Field: Materials Science — Materials Chemistry

A classical density functional from machine learning and a convolutional neural network We use machine learning methods to approximate a classical density functional. The functional ‘learns’ by comparing the density profile it generates with that of simulations. As a study case, we choose the model problem of a Lennard–Jones fluid in one dimension where there is no exact solution available. After separating the excess free energy functional into a “repulsive” and an “attractive” part, machine learning finds a functional for the attractive part in weighted–density form. The predictions of density profile at a hard wall shows good agreement when subject to thermodynamic conditions beyond those in the training set. This also holds for the equation of state if this is evaluated near the training temperature. We discuss the applicability to problems in higher dimensions. Dimension (graph theory) ; Energy (signal processing) ; Convolutional neural network ; Artificial neural network ; Feature (linguistics) ; State (computer science) ; Computational learning theory ; Training (meteorology) Machine Learning in Materials Science ; Phase Equilibria and Thermodynamics ; Quantum many-body systems
Authors: Bo Xiong | Lin Deng | Ru‐Wen Peng | Yongmin Liu
Source: Nanoscale Advances (review, 2019-01-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Controlling the degrees of freedom in metasurface designs for multi-functional optical devices This review focuses on the control over the degrees of freedom (DOF) in metasurfaces, which include the input DOF (the polarization, wavelength and incident angle of the input light and some dynamic controls), parameter DOF (the complex geometric design of metasurfaces) and output DOF (the phase, polarization and amplitude of the output light). This framework could clearly show us the development process of metasurfaces, from single-functional to multi-functional ones. Advantages of the multi-functional metasurfaces are discussed in the context of various applications, including 3D holography, broadband achromatic metalenses and multi-dimensional encoded information. By combining all the input and output DOF together, we can realize ideal optical meta-devices with deep subwavelength thickness and striking functions beyond the reach of traditional optical components. Moreover, new research directions may emerge when merging different DOF in metasurfaces with other important concepts, such as parity-time symmetry and topology, so that we can have the complete control of light waves in a prescribed manner. Degrees of freedom (physics and chemistry) ; Materials science ; Optoelectronics ; Computer science ; Nanotechnology ; Physics ; Quantum mechanics Metamaterials and Metasurfaces Applications ; Advanced Antenna and Metasurface Technologies ; Plasmonic and Surface Plasmon Research
Authors: Jasmina Lukinac | Kristina Mastanjević | Krešimir Mastanjević | Gjore Nakov | Marko Jukić
Source: Beverages (article, 2019-06-01)
Field: Chemistry — Analytical Chemistry

Computer Vision Method in Beer Quality Evaluation—A Review Beers are differentiated mainly according to their visual appearance and their fermentation process. The main quality characteristics of beer are appearance, aroma, flavor, and mouthfeel. Important visual attributes of beer are foam appearance (volume and persistence), as well as the color and clarity. To replace manual inspection, automatic, objective, rapid and repeatable external quality inspection systems, such as computer vision, are becoming very important and necessary. Computer vision is a non-contact optical technique, suitable for the non-destructive evaluation of the food product quality. Currently, the main application of computer vision occurs in automated inspection and measurement, allowing manufacturers to keep control of product quality. This paper presents an overview of the applications and the latest achievements of the computer vision methods in determining the external quality attributes of beer. Mouthfeel ; Quality (philosophy) ; Computer science ; Visual inspection ; Computer vision ; Product (mathematics) ; Machine vision ; Artificial intelligence ; Process (computing) ; CLARITY ; Engineering drawing ; Engineering ; Mathematics Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Fermentation and Sensory Analysis
Authors: R. Santagati | A. A. Gentile | S. Knauer | S. Schmitt | S. Paesani | C. Granade | N. Wiebe | C. Osterkamp | L. P. McGuinness | J. Wang | M. G. Thompson | J. G. Rarity | F. Jelezko | A. Laing
Source: Physical Review X (article, 2019-04-29)
Field: Materials Science — Materials Chemistry

Magnetic-Field Learning Using a Single Electronic Spin in Diamond with One-Photon Readout at Room Temperature Machine learning enables a nitrogen-vacancy center to track a changing magnetic field with unprecedented sensitivity at room temperature, paving the way for new and practical nanoscale quantum sensing applications. Diamond ; Nanoscopic scale ; Sensitivity (control systems) ; Quantum sensor ; Spin (aerodynamics) ; Quantum dot ; Magnetic field Diamond and Carbon-based Materials Research ; Electronic and Structural Properties of Oxides ; Quantum and electron transport phenomena
Authors: Kaushalya Madhawa | Katsuhiko Ishiguro | Kosuke Nakago | Motoki Abe
Source: arXiv (Cornell University) (preprint, 2019-09-25)
Field: Materials Science — Materials Chemistry

GraphNVP: an Invertible Flow-based Model for Generating Molecular Graphs We propose GraphNVP, the first invertible, normalizing flow-based molecular graph generation model. We decompose the generation of a graph into two steps: generation of (i) an adjacency tensor and (ii) node attributes. This decomposition yields the exact likelihood maximization on graph-structured data, combined with two novel reversible flows. We empirically demonstrate that our model efficiently generates valid molecular graphs with almost no duplicated molecules. In addition, we observe that the learned latent space can be used to generate molecules with desired chemical properties. Invertible matrix ; Adjacency matrix ; Molecular graph ; Graph ; Adjacency list ; Computer science ; Comparability graph ; Mathematics ; Theoretical computer science ; Algorithm ; Line graph ; Voltage graph ; Pure mathematics Machine Learning in Materials Science ; Advanced Graph Neural Networks ; Computational Drug Discovery Methods
Authors: Fulvio Baldovin | Enzo Orlandini | Flavio Seno
Source: Frontiers in Physics (article, 2019-09-24)
Field: Materials Science — Materials Chemistry

Polymerization Induces Non-Gaussian Diffusion Recent theoretical modeling offers a unified picture for the description of stochastic processes characterized by a crossover from anomalous to normal behavior. This is particularly welcome, as a growing number of experiments suggest the crossover to be a common feature shared by many systems: in some cases the anomalous part of the dynamics amounts to a Brownian yet non-Gaussian diffusion; more generally, both the diffusion exponent and the distribution may deviate from normal behavior in the initial part of the process. Since proposed theories work at a mesoscopic scale invoking the subordination of diffusivities, it is of primary importance to bridge these representations with a more fundamental, "microscopic" description. We argue that the dynamical behavior of macromolecules during simple polymerization processes provide suitable setups in which analytic, numerical, and particle-tracking experiments can be contrasted at such a scope. Specifically, we demonstrate that Brownian yet non-Gaussian diffusion of the center of mass of a polymer is a direct consequence of the polymerization process. Through the kurtosis, we characterize the early-stage non-Gaussian behavior within a phase diagram, and we also put forward an estimation for the crossover time to ordinary Brownian motion. Statistical physics ; Crossover ; Brownian motion ; Mesoscopic physics ; Anomalous diffusion ; Gaussian ; Diffusion ; Stochastic process ; Diffusion process ; Physics ; Mathematics ; Computer science ; Quantum mechanics ; Statistics Material Dynamics and Properties ; Advanced Thermodynamics and Statistical Mechanics ; Theoretical and Computational Physics
Authors: A. Díaz | I.I. Cuesta | Emilio Martínez‐Pañeda | J.M. Alegre
Source: International Journal of Fracture (article, 2019-12-17)
Field: Materials Science — Metals and Alloys

Analysis of hydrogen permeation tests considering two different modelling approaches for grain boundary trapping in iron Permeation ; Grain boundary ; Grain boundary diffusion coefficient ; Materials science ; Thermal diffusivity ; Effective diffusion coefficient ; Hydrogen ; Diffusion ; Trapping ; Thermodynamics ; Lattice diffusion coefficient ; Crystallite ; Grain size ; Chemistry ; Metallurgy ; Physics ; Microstructure Hydrogen embrittlement and corrosion behaviors in metals ; Corrosion Behavior and Inhibition ; Non-Destructive Testing Techniques