jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Esmail Sharifzadeh | Yasahr Amiri
Source: Journal of Polymer Engineering (article, 2020-12-21)
Field: Materials Science — Polymers and Plastics

The effects of morphological variation and polymer/polymer interface on the tensile modulus of binary polymer blends: a modeling approach Abstract In this work, the effects of the morphological variation and the polymer/polymer interface on the tensile modulus of binary polymer blends were evaluated using a combined modeling method. The characteristics of the polymer/polymer interface region were evaluated using a neural network system and the results were used to improve the analytical model. The model accuracy was investigated by comparing its predictions with the tensile test results of some prepared iPP/PA blend samples and also some other data from literature which revealed an acceptable accuracy (error &lt; 5%). Materials science ; Polymer ; Ultimate tensile strength ; Composite material ; Polymer blend ; Modulus ; Tensile testing ; Binary number ; Work (physics) ; Young's modulus ; Thermodynamics ; Copolymer ; Mathematics Polymer crystallization and properties ; Rheology and Fluid Dynamics Studies ; Block Copolymer Self-Assembly
Authors: Parinaz Naseri | Sean V. Hum
Source:  (article, 2020-07-05)
Field: Materials Science — Electronic, Optical and Magnetic Materials

A Machine Learning-Based Approach to Synthesize Multilayer Metasurfaces The synthesis of a metasurface exhibiting a specific set of desired scattering properties is a time-consuming and resource-demanding process, which conventionally relies on many cycles of full-wave simulations. It requires the designer to choose the number of the metallic layers, the scatterer shapes, and the type and the thickness of the separating substrates. Here, we propose a machine learning (ML)-based approach to automate the inverse design of dual-layer metasurfaces, which is extendable to multilayer metasurfaces. This approach allows synthesis of thin structures where higher-order mode coupling between the layers is significant, and hence, can be leveraged for extra degrees of freedom. Computer science ; Set (abstract data type) ; Process (computing) ; Coupling (piping) ; Degrees of freedom (physics and chemistry) ; Layer (electronics) ; Dual mode ; Inverse ; Scattering ; Materials science ; Electronic engineering ; Optics ; Nanotechnology ; Engineering ; Physics ; Geometry ; Mathematics Metamaterials and Metasurfaces Applications ; Advanced Antenna and Metasurface Technologies ; Antenna Design and Analysis
Authors: Han Gao | Xiang Li | Zhanpeng Li | Yidi Wang | Yunan Gao | W. Tang | Long Chen | Congchong Yan | Yu Tu | Liang Sun
Source: Journal of Nuclear Science and Technology (article, 2020-12-31)
Field: Materials Science — Materials Chemistry

The buildup factor calculations of concrete with different proportions of CRT based on a BP neural network by MCNP Cathode ray tubes (CRTs) are glass materials that contain harmful heavy metal elements such as lead . CRTs have been widely used in electronic products sets for decades. At present, these discarded CRTs endanger the environment. Because the high-density heavy metal elements in CRTs have a good shielding effect for X/γ rays, these raw materials have become a good choice to produce radiation-proof concrete. In the selection and use of shielding materials, the buildup factor must be considered. Monte Carlo code MCNP is used to calculate the exposure buildup factor (EBF) of concrete with different proportions of CRT fragments in the photon energy range of 0.015–15 MeV for shielding thicknesses of up to 20 mean free paths (mfp). Back-propagation (BP) neural network is proposed to predict the EBF, and the prediction effect is evaluated, reliability of this method is verified; the average error is 3.4%, and the maximum error is 9.1%. Using this method, the EBF of concrete with an arbitrary proportion of CRTs, any energy level and any shielding thickness can be quickly obtained in the ranges of the parameters of the neural network training set (0.015–15 MeV and 0–20 mfp in this paper). CRTS ; Electromagnetic shielding ; Nuclear engineering ; Range (aeronautics) ; Materials science ; Artificial neural network ; Computer science ; Engineering ; Composite material ; Artificial intelligence Radiation Shielding Materials Analysis ; Advanced X-ray and CT Imaging ; Graphite, nuclear technology, radiation studies
Authors: Dali Chen | Yang Liu | Shixin Liu | Fang Liu | YangQuan Chen
Source: Symmetry (article, 2020-05-06)
Field: Materials Science — Materials Chemistry

Framework of Specific Description Generation for Aluminum Alloy Metallographic Image Based on Visual and Language Information Fusion The automatic generation of language description is an important task in the intelligent analysis of aluminum alloy metallographic images, and is crucial for the high-quality development of the non-ferrous metals manufacturing industry. In this paper, we propose a methodological framework to generate the language description for aluminum alloy metallographic images. The framework consists of two parts: feature extraction and classification. In the process of feature extraction, we used ResNet (residual network) and CNN (convolutional neural network) to extract visual features from metallographic images. Meanwhile, we used LSTM (long short term memory), FastText, and TextCNN to extract language text features from questions. Then, we implemented a fusion strategy to integrate these two features. Finally, we used the fused features as the input of the classification network. This framework turns the description generation problem into a classification task, which greatly simplifies the generation process of language description and provides a new idea for the description of metallographic images. Based on this basic framework, we implemented seven different methods to generate the language description of aluminum alloy metallographic images, and their performance comparisons are given. To verify the effectiveness of this framework, we built the aluminum alloy metallographic image dataset. A large number of experimental results show that this framework can effectively accomplish the given tasks. Computer science ; Convolutional neural network ; Artificial intelligence ; Process (computing) ; Feature (linguistics) ; Feature extraction ; Task (project management) ; Pattern recognition (psychology) ; Natural language processing ; Computer vision ; Engineering ; Programming language Machine Learning in Materials Science ; Image Processing and 3D Reconstruction ; Handwritten Text Recognition Techniques
Authors: Dingkun Yang | Weigen Chen | Yongkuo Zhou | Zewei Wang | Ruyue Zhang | Ruimin Song
Source:  (article, 2020-09-06)
Field: Chemistry — Analytical Chemistry

Prediction of Aging Degree of Oil-paper Insulation Based on Raman Spectroscopy and Fuzzy Neural Network In this paper, a method based on Raman spectroscopy to diagnose the aging degree of oil-paper insulation is discussed. According to IEEE guidelines, oil-paper insulation samples were prepared in the laboratory, and the degree of polymerization values of the samples were measured to obtain the exact aging states. Raman detections of the samples were carried out based on the Raman experimental platform. T-S fuzzy neural network was used to analyze the Raman spectrum data of insulating oil. By using the experimental data to train the diagnosis model, constantly modifying the membership function, and mining the internal mathematical relationship between the Raman spectrum characteristics of oil paper insulation and the degree of polymerization of insulating paper, this paper managed to use the Raman spectrum data of insulating oil to predict the aging degree of oil paper insulation. Finally, the deviations between the predicted and the measured degree of polymerization values of the five test samples in this experiment are both less than 150. Raman spectroscopy ; Degree (music) ; Degree of polymerization ; Materials science ; Fuzzy logic ; Artificial neural network ; Polymerization ; Composite material ; Analytical Chemistry (journal) ; Computer science ; Biological system ; Acoustics ; Artificial intelligence ; Physics ; Chemistry ; Optics ; Polymer ; Chromatography Spectroscopy and Chemometric Analyses ; Water Quality Monitoring and Analysis ; Power Transformer Diagnostics and Insulation
Authors: Chun-Han Su | Zihan Lin | Yu-Shin Lin | Hung-Fei Kuo
Source: IEEE Transactions on Semiconductor Manufacturing (article, 2020-06-22)
Field: Materials Science — Surfaces, Coatings and Films

Enhancement of Diffraction-Based Overlay Model for Overlay Target With Asymmetric Sidewall Overlay metrology is crucial to process control in manufacturing semiconductor devices. Diffraction-based overlay (DBO) is an effective overlay measurement approach because it exhibits multiple advantages. This study analyzed measurement errors caused by sidewalls in the bottom gratings of DBO targets. Accordingly, improvement was proposed using a neural network. First, rigorous coupled wave analysis was employed to calculate the pupil images generated by an overlay target. These images were then used as a data set. Next, two-directional twodimensional principal component analysis was used to reduce the dimension of features in these images. The features were then used to train a neural network and determine weighting coefficients in each network layer to create a DBO model. This study used virtual metrology to analyze 30 types of overlay targets and generated 18900 pupil images to create a data set. Each overlay target model was measured 10 times, and shot noise, dark noise, and quantization noise in the pupil images were accounted for. The simulation results revealed that when the dose level was 1000 mJ/s·cm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> , the overlay mean square error of the testing data was 0.40nm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> , indicating notable improvement in the measurement results of overlay targets with bottom grating sidewalls. Therefore, the proposed neural network-based DBO model can be applied to overlay targets with sidewalls and effectively improve the overlay accuracy. Overlay ; Metrology ; Computer science ; Artificial neural network ; Overlay network ; Artificial intelligence ; Electronic engineering ; Optics ; Engineering ; Physics Optical Coatings and Gratings ; Surface Roughness and Optical Measurements ; Advanced Surface Polishing Techniques
Authors: Matthew Ragoza | Tomohide Masuda | David Ryan Koes
Source: arXiv (Cornell University) (preprint, 2020-10-16)
Field: Materials Science — Materials Chemistry

Learning a Continuous Representation of 3D Molecular Structures with\n Deep Generative Models Machine learning in drug discovery has been focused on virtual screening of\nmolecular libraries using discriminative models. Generative models are an\nentirely different approach that learn to represent and optimize molecules in a\ncontinuous latent space. These methods have been increasingly successful at\ngenerating two dimensional molecules as SMILES strings and molecular graphs. In\nthis work, we describe deep generative models of three dimensional molecular\nstructures using atomic density grids and a novel fitting algorithm for\nconverting continuous grids to discrete molecular structures. Our models\njointly represent drug-like molecules and their conformations in a latent space\nthat can be explored through interpolation. We are also able to sample diverse\nsets of molecules based on a given input compound and increase the probability\nof creating valid, drug-like molecules.\n Discriminative model ; Generative model ; Computer science ; Generative grammar ; Virtual screening ; Representation (politics) ; Chemical space ; Interpolation (computer graphics) ; Artificial intelligence ; Space (punctuation) ; Molecular descriptor ; Machine learning ; Drug discovery ; Theoretical computer science ; Chemistry ; Quantitative structure–activity relationship Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Graph Theory and Algorithms
Authors: Razieh Pourdarbani | Sajad Sabzi | Mario Hernández-Hernández | José Luis Hernández-Hernández | Iván Gallardo-Bernal | Israel Herrera-Miranda
Source: Plants (article, 2020-11-12)
Field: Chemistry — Analytical Chemistry

Non-Destructive Estimation of Total Chlorophyll Content of Apple Fruit Based on Color Feature, Spectral Data and the Most Effective Wavelengths Using Hybrid Artificial Neural Network—Imperialist Competitive Algorithm Non-destructive assessment of the physicochemical properties of food products, especially fruits, makes it possible to examine the internal quality without any damage. This is applicable at different stages of fruit growth, harvesting stage, and storage as well as at the market stage. In this regard, the present study aimed to estimate the total chlorophyll content using three types of data: color data, spectral data, and spectral data related to the most effective wavelengths. The most important steps of the proposed algorithms include extracting spectral and color data from each sample of Fuji cultivar apple, selecting the most effective wavelengths at the range of 660-720 nm using hybrid artificial neural network-particle swarm optimization (ANN-PSO), non-destructive assessment of the chemical property of total chlorophyll content based on color data, and spectral data using hybrid artificial neural network-Imperialist competitive algorithm (ANN-ICA). In order to assess the reliability of the hybrid ANN-ICA, 1000 iterations were performed after selecting the optimal structure of the artificial neural network. According to the results, in the best training mode and using spectral data and the most effective wavelength, total chlorophyll content was predicted with the R2 and RMSE of 0.991 and 0.0035, 0.997 and 0.001, 0.997 and 0.0006, respectively. Artificial neural network ; Particle swarm optimization ; Computer science ; Biological system ; Artificial intelligence ; Mean squared error ; Pattern recognition (psychology) ; Algorithm ; Mathematics ; Statistics ; Biology Spectroscopy and Chemometric Analyses ; Leaf Properties and Growth Measurement ; Postharvest Quality and Shelf Life Management
Authors: Antti Pihlajamaki | Joonas Hämäläinen | Joakim Linja | P. Nieminen | Sami Malola | Tommi Kärkkäinen | Hannu Häkkinen
Source: ChemRxiv (preprint, 2020-02-21)
Field: Materials Science — Materials Chemistry

Monte Carlo Simulations of Au38(SCH3)24 Nanocluster Using Distance-Based Machine Learning Methods &lt;div&gt; &lt;div&gt; &lt;div&gt; &lt;p&gt;We present an implementation of distance-based machine learning (ML) methods to create a realistic atomistic interaction potential to be used in Monte Carlo simulations of thermal dynamics of thiolate (SR) protected gold nanoclusters. The ML potential is trained for Au38(SR)24 by using previously published, density functional theory (DFT) -based, molecular dynamics (MD) simulation data on two experimentally characterized structural isomers of the cluster, and validated against independent DFT MD simulations. This method opens a door to efficient probing of the configuration space for further investigations of thermal-dependent electronic and optical properties of Au38(SR)24. Our ML implementation strategy allows for generalization and accuracy control of distance-based ML models for complex nanostructures having several chemical elements and interactions of varying strength. &lt;/p&gt; &lt;/div&gt; &lt;/div&gt; &lt;/div&gt; Nanoclusters ; Molecular dynamics ; Monte Carlo method ; Density functional theory ; Cluster (spacecraft) ; Generalization ; Physics ; Chemistry ; Nanotechnology ; Statistical physics ; Materials science ; Computer science ; Computational chemistry ; Mathematics Nanocluster Synthesis and Applications ; Advanced Nanomaterials in Catalysis ; Machine Learning in Materials Science
Authors: Karl Tassenberg | Bogdan Nenchev | Joel Strickland | Sam Perry | David Weston
Source: Materials Characterization (article, 2020-11-18)
Field: Materials Science — Materials Chemistry

DenMap single crystal solidification structure feature extraction: Automation and application Materials science ; Thresholding ; Microstructure ; Artificial intelligence ; Porosity ; Automation ; Grain boundary ; Gaussian ; Pattern recognition (psychology) ; Deconvolution ; Single crystal ; Computer science ; Algorithm ; Mechanical engineering ; Metallurgy ; Composite material ; Crystallography ; Engineering ; Image (mathematics) Solidification and crystal growth phenomena ; Crystallization and Solubility Studies ; Aluminum Alloy Microstructure Properties
Authors: M. Vollmar | James M. Parkhurst | Dominic Jaques | Arnaud Baslé | Garib N. Murshudov | David G. Waterman | Gwyndaf Evans
Source: IUCrJ (article, 2020-02-26)
Field: Materials Science — Materials Chemistry

The predictive power of data-processing statistics This study describes a method to estimate the likelihood of success in determining a macromolecular structure by X-ray crystallography and experimental single-wavelength anomalous dispersion (SAD) or multiple-wavelength anomalous dispersion (MAD) phasing based on initial data-processing statistics and sample crystal properties. Such a predictive tool can rapidly assess the usefulness of data and guide the collection of an optimal data set. The increase in data rates from modern macromolecular crystallography beamlines, together with a demand from users for real-time feedback, has led to pressure on computational resources and a need for smarter data handling. Statistical and machine-learning methods have been applied to construct a classifier that displays 95% accuracy for training and testing data sets compiled from 440 solved structures. Applying this classifier to new data achieved 79% accuracy. These scores already provide clear guidance as to the effective use of computing resources and offer a starting point for a personalized data-collection assistant. Computer science ; Classifier (UML) ; Data mining ; Data collection ; Predictive power ; Data set ; Phaser ; Data processing ; Machine learning ; Algorithm ; Artificial intelligence ; Statistics ; Database ; Mathematics ; Optics Enzyme Structure and Function ; Protein Structure and Dynamics ; Genetics, Bioinformatics, and Biomedical Research
Authors: Vladislav Gladkikh | Dong Yeon Kim | Amir Hajibabaei | Atanu Jana | Chang Woo Myung | Kwang S. Kim
Source: The Journal of Physical Chemistry (article, 2020-01-01)
Field: Materials Science — Polymers and Plastics

Machine Learning for Predicting the Band Gaps of ABX₃ Perovskites from Elemental Properties The band gap is an important parameter that determines light-harvesting capability of perovskite materials. It governs the performance of various optoelectronic devices such as solar cells, light-emitting diodes, and photodetectors. For perovskites of a formula ABX₃ having a non-zero band gap, we study nonlinear mappings between the band gap and properties of constituent elements (e.g., electronegativities, electron affinities, etc) using alternating conditional expectations (ACE)—a machine learning technique suitable for small data sets. We also compare ACE with other machine learning methods: decision trees, kernel ridge regression, extremely randomized trees, AdaBoost, and gradient boosting. The best performance is achieved by kernel ridge regression and extremely randomized trees. However, ACE has an advantage that it presents its results in a graphic form, helping in interpretation. The models are trained with the data obtained from density functional theory calculations. Different statistical approaches for feature selection are applied and compared: Pearson correlation, Spearman’s rank correlation, maximal information coefficient, distance correlation, and ACE. A classification task of separating metallic perovskites from nonmetallic ones is solved using support-vector machines with the radial basis function kernel. Artificial intelligence ; Chemistry ; Machine learning ; Electronegativity ; Support vector machine ; Band gap ; Computer science ; Condensed matter physics ; Physics Conducting polymers and applications ; Machine Learning in Materials Science ; Perovskite Materials and Applications
Authors: Vadim Korolev | Artem Mitrofanov | Yaroslav Kucherinenko | Yurii M. Nevolin | Vladimir Krotov | Pavel Protsenko
Source: Scripta Materialia (article, 2020-05-20)
Field: Materials Science — Materials Chemistry

Accelerated modeling of interfacial phases in the Ni-Bi system with machine learning interatomic potential Grain boundary ; Materials science ; Interatomic potential ; Phase diagram ; Scalability ; Statistical physics ; Phase (matter) ; Space (punctuation) ; Molecular dynamics ; Chemical physics ; Thermodynamics ; Computer science ; Microstructure ; Metallurgy ; Physics Machine Learning in Materials Science ; Advanced Materials Characterization Techniques ; nanoparticles nucleation surface interactions
Authors: Zi Wang | Tiantian Li | Lorry Chang | Feifan Wang | Yahui Xiao | Dun Mao | Thomas Kananen | Tingyi Gu
Source:  (article, 2020-02-26)
Field: Materials Science — Electronic, Optical and Magnetic Materials

On-chip wavefront shaping with dielectric metasurface Metasurfaces can be used for free-space wavefront shaping, which can generate an arbitrary phase shift with subwavelength thickness. We designed an integrated metasurface that can achieve on-chip spatial signal convertion with a ultracompact dimension and low loss (&lt;1dB). The dielectric metalens integration can significantly reduce the taper length as well as making mechanically robust photonic crystal coupler with high transmission. The proposed integrated metasurfaces can be easily cascaded to achieve on-chip meta-system for signal processing. Here we demonstrated a metasystem that can perform the spatial differentiation and a deep learning meta-system that can achieve wavelength demultiplexing. Wavefront ; Dielectric ; Optics ; Chip ; Optoelectronics ; Materials science ; Computer science ; Physics ; Electronic engineering ; Telecommunications ; Engineering Metamaterials and Metasurfaces Applications ; Random lasers and scattering media ; Advanced Optical Imaging Technologies
Authors: Richard Jana | Julian von Lautz | S. Mostafa Khosrownejad | W. Beck Andrews | Michael Moseler | Lars Pastewka
Source: Journal of Physics Materials (article, 2020-05-21)
Field: Materials Science — Materials Chemistry

Constitutive relations for plasticity of amorphous carbon Abstract We deform representative volume elements of amorphous carbon obtained from melt-quenches in molecular dynamics calculations using bond-order and machine learning interatomic potentials. A Drucker-Prager law with a zero-pressure flow stress of 41.2 GPa and an internal friction coefficient of 0.39 describes the deviatoric stress during flow as a function of pressure. We identify the mean coordination number as the order parameter describing this flow surface. However, a description of the dynamical relaxation of the quenched samples towards steady-state flow requires an additional order parameter. We suggest an intrinsic strain of the samples and present equations for its evolution. Our results provide insights into rehybridization and pressure dependence of friction between coated surfaces as well as routes towards the description of amorphous carbon in macroscale models of deformation. Amorphous solid ; Plasticity ; Materials science ; Flow (mathematics) ; Deformation (meteorology) ; Constitutive equation ; Relaxation (psychology) ; Stress (linguistics) ; Flow stress ; Mechanics ; Thermodynamics ; Strain rate ; Composite material ; Physics ; Chemistry ; Crystallography Diamond and Carbon-based Materials Research ; High-pressure geophysics and materials ; Force Microscopy Techniques and Applications