jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: M. Alper Selver | Tuğçe Toprak | Mustafa Seçmen | E. Yesim Zoral
Source:  (article, 2019-11-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Utilizing Resonant Scattering Signal Characteristics of Magnetic Spheres via Deep Learning for Improved Target Classification Object classification using LAte-time Resonant Scattering Electromagnetic Signals (LARSESs) is a significant problem found in different areas of application. Due to their special properties, spherical objects play an important role in this field both as a challenging target and analytical LARSES source. Although many studies focus on their detailed analysis, the challenges associated with target classification by resonant LARSESs from multi-layer spheres have not been investigated in detail. Moreover, existing studies made the simplifying assumption that the objects having (one or more) layers constitute equal permeability values at the core and coatings. However, especially for metamaterials, magneto-dielectric inclusions require consideration of magnetic properties as well as dielectric ones. In this respect, this study shows that the utilization LARSESs of magnetic spheres provides diverse information and features, which result with superior object classification performance. For this purpose, first, time domain LARSESs are generated numerically for single and multi-layer radially symmetrical dielectric and magnetic spheres. Then, by using emerging deep learning tools, particularly Convolutional Neural Network (CNNs), which are trained with spheres having different material properties, a high multi-layer object classification performance is achieved. Moreover, by extending the proposed strategy to measured data via modern data augmentation and transfer learning techniques, an improved classification performance is also obtained for more complex targets. SPHERES ; Dielectric ; Convolutional neural network ; Scattering ; Metamaterial ; Deep learning ; Computer science ; Transfer of learning ; Focus (optics) ; Artificial intelligence ; Object (grammar) ; Magnetic field ; Materials science ; Pattern recognition (psychology) ; Physics ; Optics ; Optoelectronics Metamaterials and Metasurfaces Applications ; Indoor and Outdoor Localization Technologies ; Electromagnetic Scattering and Analysis
Authors: Yoshihiro Hayashi | Susumu Kawauchi
Source: Journal of Computer Chemistry Japan (article, 2019-01-01)
Field: Materials Science — Polymers and Plastics

Construction of Band Gap Prediction Model for π-conjugated Polymers Using Aromatic/Quinoid and Donor/Acceptor Properties A band gap prediction model of π-conjugated polymers was constructed using aromatic/quinoid, donor/acceptor, and torsion properties to predict quantitatively the band gap of π-conjugated polymers from properties of monomers. Quinoid stabilization energy (QSE), energy difference between HOMO of donor and LUMO of acceptor, torsion angle in homo-dimer of monomers were used as descriptors of aromatic/quinoid, donor/acceptor, and torsion properties. The neural network, which was constructed by 2 hidden layers with 5 neurons per layer, quantitatively predicts (RMSD = 0.207 eV, R2 = 0.885) the band gap of the π-conjugated polymers from descriptors of monomers. Conjugated system ; Acceptor ; Monomer ; Band gap ; HOMO/LUMO ; Polymer ; Dimer ; Materials science ; Torsion (gastropod) ; Photochemistry ; Computational chemistry ; Chemistry ; Molecule ; Organic chemistry ; Physics ; Optoelectronics ; Composite material Conducting polymers and applications ; Organic Electronics and Photovoltaics ; Synthesis and properties of polymers
Authors: P.S. Noori Banu | S. Devaki Rani
Source: International Journal of Microstructure and Materials Properties (article, 2019-01-01)
Field: Materials Science — Materials Chemistry

Microstructure evaluation and modelling the tensile strength and yield strength of titanium alloys The rationale of the current study was to develop artificial neural network (ANN) models of titanium alloys for predicting tensile strength and yield strength using the alloy composition and processing parameters as the inputs and validate the models through experimental evaluation and correlate with microstructure characteristics. The robustness of the model was tested through experimental evaluation of tensile strength in Ti-6Al-4V, Ti-5.8Al-4Sn-3.5 Zr-0.7Nb-0.5Mo-0.3 Si, Ti-3Al-8V-4Zr-6Cr-4Mo, and Ti-10V-2Fe-3Al alloys. Microstructure characteristics i.e., volume fraction of alpha, alpha grain area and Feret ratio of Ti, Ti-6Al-4V, Ti-6Al-5V, Ti-6Al-6V-2Sn, Ti-8Mn, and Ti-13V-11Cr-3Al correlated inversely with predicted tensile strength and yield strength. This three-tier validation ensures efficient performance of the developed ANN models. Ultimate tensile strength ; Materials science ; Microstructure ; Titanium alloy ; Volume fraction ; Titanium ; Alloy ; Composite material ; Grain size ; Metallurgy Titanium Alloys Microstructure and Properties ; Metal and Thin Film Mechanics ; Welding Techniques and Residual Stresses
Authors: Alberto Hernández | Adarsh Balasubramanian | Fenglin Yuan | Simon Mason | Tim Mueller
Source: npj Computational Materials (preprint, 2019-11-18)
Field: Materials Science — Materials Chemistry

Fast, accurate, and transferable many-body interatomic potentials by symbolic regression Abstract The length and time scales of atomistic simulations are limited by the computational cost of the methods used to predict material properties. In recent years there has been great progress in the use of machine-learning algorithms to develop fast and accurate interatomic potential models, but it remains a challenge to develop models that generalize well and are fast enough to be used at extreme time and length scales. To address this challenge, we have developed a machine-learning algorithm based on symbolic regression in the form of genetic programming that is capable of discovering accurate, computationally efficient many-body potential models. The key to our approach is to explore a hypothesis space of models based on fundamental physical principles and select models within this hypothesis space based on their accuracy, speed, and simplicity. The focus on simplicity reduces the risk of overfitting the training data and increases the chances of discovering a model that generalizes well. Our algorithm was validated by rediscovering an exact Lennard-Jones potential and a Sutton-Chen embedded-atom method potential from training data generated using these models. By using training data generated from density functional theory calculations, we found potential models for elemental copper that are simple, as fast as embedded-atom models, and capable of accurately predicting properties outside of their training set. Our approach requires relatively small sets of training data, making it possible to generate training data using highly accurate methods at a reasonable computational cost. We present our approach, the forms of the discovered models, and assessments of their transferability, accuracy and speed. Overfitting ; Simplicity ; Computer science ; Symbolic regression ; Genetic programming ; Machine learning ; Set (abstract data type) ; Artificial intelligence ; Simple (philosophy) ; Interatomic potential ; Algorithm ; Artificial neural network ; Molecular dynamics ; Computational chemistry ; Physics Machine Learning in Materials Science ; Protein Structure and Dynamics ; Computational Drug Discovery Methods
Authors: Thomas Koprucki | Anieza Maltsi | Tore Niermann | Timo Streckenbach | Karsten Tabelow | Jörg Polzehl
Source:  (article, 2019-07-01)
Field: Materials Science — Surfaces, Coatings and Films

On a Database of Simulated TEM Images for In(Ga)As/GaAs Quantum Dots with Various Shape We present a database of simulated transmission electron microscopy (TEM) images for In(Ga)As quantum dots (QDs) embedded in bulk-like GaAs samples. The database contains series of TEM images for QDs with various shapes, e.g. pyramidal and lens-shaped, depending on the size and indium concentration as well as on the excitation conditions of the electron beam. This database is a key element of a novel concept for model-based geometry reconstruction (MBGR) of semiconductor QDs from TEM imaging and can be used to establish a statistical procedure for the estimation of QD properties and classification of QD types based on machine learning techniques. Quantum dot ; Materials science ; Indium ; Transmission electron microscopy ; Semiconductor ; Gallium arsenide ; Database ; Computer science ; Optoelectronics ; Nanotechnology Electron and X-Ray Spectroscopy Techniques ; Advanced Electron Microscopy Techniques and Applications ; Integrated Circuits and Semiconductor Failure Analysis
Authors: Jonathan Lahnsteiner | Ryosuke Jinnouchi | Menno Bokdam
Source: arXiv (Cornell University) (article, 2019-05-29)
Field: Materials Science — Materials Chemistry

The essence of long-range order in hybrid perovskites The crystal structure of the MAPbI$_3$ hybrid perovskite forms an intricate electrostatic puzzle with different ordering patterns of the MA molecules at elevated temperatures. For this perovskite three published model Hamiltonians based on the point-dipole (pd) approximation combined with short-range effective interactions are compared to a recently developed machine-learning force field. A molecular order parameter is used to consistently compare the transformation of the anti-ferroelectric ordering in the orthorhombic phase upon raising the temperature. We show that the ground states and the order-disorder transition of the three models are completely different. Our analysis indicates that the long-range order in the low-temperature orthorhombic phase can be captured by pd-based models with a short cutoff radius, including the nearest and next-nearest neighbor molecules. By constructing effective atomic interactions the ordering can already be described within 6 A radius. By extracting the coupling energetics of the molecules from density functional theory calculations on MA$_x$Cs$_{1-x}$PbI$_3$ test systems, we show that the pd-approximation holds at least for static structures. To improve the accuracy of the pd-interaction an Ewald summation is applied combined with a distance dependent electronic screening function. Orthorhombic crystal system ; Perovskite (structure) ; Ferroelectricity ; k-nearest neighbors algorithm ; Phase transition ; Dipole ; RADIUS ; Condensed matter physics ; Phase (matter) ; Order (exchange) ; Physics ; Materials science ; Statistical physics ; Chemistry ; Dielectric ; Quantum mechanics ; Crystallography ; Diffraction ; Computer science Machine Learning in Materials Science ; Perovskite Materials and Applications ; Solid-state spectroscopy and crystallography
Authors: Vahid Attari | Pejman Honarmandi | Thien Duong | Daniel Sauceda | Douglas Allaire | Raymundo Arróyave
Source: SSRN Electronic Journal (article, 2019-01-01)
Field: Materials Science — Materials Chemistry

Uncertainty Propagation in a Multiscale CALPHAD-Reinforced Elastochemical Phase-Field Model CALPHAD ; Field (mathematics) ; Phase (matter) ; Environmental science ; Statistical physics ; Phase diagram ; Physics ; Mathematics Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Block Copolymer Self-Assembly
Authors: Daisuke Fujita
Source: Vacuum and Surface Science (article, 2019-03-09)
Field: Materials Science — Surfaces, Coatings and Films

Measurement Informatics in Surface Science Following the historical development of informatics, data-intensive science, machine learning and materials informatics, we introduce important applications to surface science. As an indication of first application of multivariate analysis to surface science, application of target factor analysis to depth profiling data by Auger electron spectroscopy is introduced including the detailed analysis principle. We believe the way the future measurement system should be is a “smart measurement system” linked with cyber space and physical space. In such a data driven measurement system, novel informatics that can be applied to big measurement data are required. From such a viewpoint, the fusion of surface analysis and informatics, that is, “surface measurement informatics” is expected as a new paradigm of surface science. Informatics ; Computer science ; Data science ; Materials informatics ; Big data ; Profiling (computer programming) ; Sensor fusion ; Surface (topology) ; Health informatics ; Data mining ; Engineering informatics ; Artificial intelligence ; Engineering ; Mathematics ; Medicine Electron and X-Ray Spectroscopy Techniques ; Advanced Materials Characterization Techniques ; Machine Learning in Materials Science
Authors: Jong-Dae Kim | Jeong-Uk Park | Hye-Jeong Song | Yu-Seop Kim | Chan-Young Park
Source: Sensors and Materials (article, 2019-02-07)
Field: Chemistry — Physical and Theoretical Chemistry

Polymerase Chain Reaction Thermoelectric Module Based on Prediction and Attachment of Aging Phenomena Many machines around us continue to age over time. This aging of machines affects experimental results, and the accuracy of experimental results may be reduced or not fully achieved. To prevent this problem, we decided to predict the lifetime of a machine to help us analyze experimental results. Thus, we carried out a polymerase chain reaction (PCR) experiment, which is an experiment to amplify the gene information stored in DNA to the amount required for gene analysis experiments, to determine the temperature change of a thermoelectric module (Peltier). The Peltier was used to determine the reliability of the results by learning how it affects the lifetime of the device. As a method of measuring aging, we used deep learning technology. In this study, we expect to learn how machines age. By learning the machine's own aging data of important parts, the reliability of test results can be greatly improved. Polymerase chain reaction ; Thermoelectric effect ; Chain (unit) ; Materials science ; Computational biology ; Computer science ; Chemistry ; Biology ; Physics ; Thermodynamics ; Biochemistry thermodynamics and calorimetric analyses
Authors: Jonghwan Lee
Source: IEEE Electron Device Letters (article, 2019-02-28)
Field: Materials Science — Materials Chemistry

Thermoelectric Transport Modeling Based on Neural Computation Analytical and neural network models for the thermoelectric transport in Bi <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> Te <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sub> are presented with non-parabolic band effects' consideration. The analytical model is newly formulated by using the Boltzmann transport equation, and the artificial neural network (ANN) models are employed for the computational integration of the thermoelectric coefficients. In order to incorporate the realistic band structures in Bi <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> Te <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sub> materials, the integrand of the thermoelectric coefficients is simulated by using both the integration of ANN and the radial basis function ANN (RBF-ANN). It is found that the RBF-ANN model has a good performance for predicting the thermoelectric transport. Thermoelectric effect ; Artificial neural network ; Computation ; Artificial intelligence ; Computer science ; Algorithm ; Applied mathematics ; Topology (electrical circuits) ; Physics ; Thermodynamics ; Mathematics ; Engineering ; Electrical engineering Advanced Thermoelectric Materials and Devices ; Thermal properties of materials ; Thermal Radiation and Cooling Technologies
Authors: S. G. Pozdnyakov | Artem R. Oganov | Efim Mazhnik | Arslan Mazitov | Ivan A. Kruglov
Source: arXiv (Cornell University) (preprint, 2019-10-16)
Field: Materials Science — Materials Chemistry

Fast general two- and three-body interatomic potential We introduce a new class of machine learning interatomic potentials - fast General Two- and Three-body Potential (GTTP), which is as fast as conventional empirical potentials and require computational time that remains constant with increasing fitting flexibility. GTTP does not contain any assumptions about the functional form of two- and three-body interactions. These interactions can be modeled arbitrarily accurately, potentially by thousands of parameters not affecting resulting computational cost. Time complexity is O(1) per every considered pair or triple of atoms. The fitting procedure is reduced to simple linear regression on ab initio calculated energies and forces and leads to effective two- and three-body potential, reproducing quantum many-body interactions as accurately as possible. Our potential can be made continuously differentiable any number of times at the expense of increased computational time. We made a number of performance tests on one-, two- and three-component systems. The flexibility of the introduced approach makes the potential transferable in terms of size and type of atomic systems. We show that trained on randomly generated structures with just 8 atoms in the unit cell, it significantly outperforms common empirical interatomic potentials in the study of large systems, such as grain boundaries in polycrystalline materials. Interatomic potential ; Flexibility (engineering) ; Differentiable function ; Ab initio ; Statistical physics ; Computer science ; Ab initio quantum chemistry methods ; Simple (philosophy) ; Molecular dynamics ; Mathematics ; Computational chemistry ; Physics ; Chemistry ; Quantum mechanics ; Mathematical analysis ; Molecule ; Statistics Machine Learning in Materials Science ; Hydrogen Storage and Materials ; Quantum, superfluid, helium dynamics
Authors: Zhen Wang | Yanli Zhang | Osama A. Mohammed
Source: 2019 International Applied Computational Electromagnetics Society Symposium (ACES) (article, 2019-04-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Measurement and Modeling of Magnetostriction in Transformer Core Based on a BPNN Method Assisted with Levenberg-Marquardt Algorithm The reasons causing the vabriations and noises of tranformer cores include the magnetostrictiove force in the core material and electromagnetic forces acting on lapped seams of cores and among silicon steel sheets. This paper carried out the measurement of magnetostrictive strains in a grain-oriented steel sheet under alternating magnetizations and dicussed the effect of a dc bias present in the exciting current on the magnetostriction. Based on the experimental data, the magnetostrictive characteristics are modeled by a back propagation neural network (BPNN) method. In order to achive an acceptable convergence condition, the Levenberg-Marquardt (LM) algorithm instead of the traditional gradient descent algorithm was employed to assist the BPNN. Magnetostriction ; Levenberg–Marquardt algorithm ; Gradient descent ; Electrical steel ; Convergence (economics) ; Backpropagation ; Transformer ; Artificial neural network ; Algorithm ; Current transformer ; Magnetic core ; Core (optical fiber) ; Electromagnetic coil ; Computer science ; Acoustics ; Engineering ; Materials science ; Electrical engineering ; Magnetic field ; Artificial intelligence ; Physics ; Voltage ; Composite material Magnetic Properties and Applications ; Non-Destructive Testing Techniques ; Neural Networks and Applications
Authors: Arun Mannodi‐Kanakkithodi | Michael Y. Toriyama | Fatih G. Sen | Michael J. Davis | Robert F. Klie | Maria K. Y. Chan
Source:  (article, 2019-06-01)
Field: Materials Science — Materials Chemistry

Machine learning defect properties in Cd-based chalcogenides Impurity energy levels in the band gap can have serious consequences for a semiconductor's performance as a photovoltaic absorber. Data-driven approaches can help accelerate the prediction of point defect properties in common semiconductors, and thus lead to the identification of potential deep lying impurity states. In this work, we use density functional theory (DFT) to compute defect formation energies and charge transition levels of hundreds of impurities in CdX chalcogenide compounds, where X = Te, Se or S. We apply machine learning techniques on the DFT data and develop on-demand predictive models for the formation energy and relevant transition levels of any impurity atom in any site. The trained ML models are general and accurate enough to predict the properties of any possible point defects in any Cd-based chalcogenide, as we prove by testing on a few selected defects in mixed chalcogen compounds CdTe <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0.5</sub> Se <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0.5</sub> and CdSe <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0.5</sub> S <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0.5</sub> . The ML framework used in this work can be extended to any class of semiconductors. Chalcogenide ; Impurity ; Chalcogen ; Band gap ; Semiconductor ; Artificial intelligence ; Physics ; Computer science ; Algorithm ; Machine learning ; Materials science ; Chemistry ; Crystallography ; Condensed matter physics ; Optoelectronics ; Quantum mechanics Machine Learning in Materials Science ; Chalcogenide Semiconductor Thin Films ; Advanced Semiconductor Detectors and Materials