jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Harsh Trivedi | Vladimir V. Shvartsman | Marco S. A. Medeiros | Robert C. Pullar | Doru C. Lupascu
Source: npj Computational Materials (article, 2018-06-15)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Sequential piezoresponse force microscopy and the ‘small-data’ problem Abstract The term big-data in the context of materials science not only stands for the volume, but also for the heterogeneous nature of the characterization data-sets. This is a common problem in combinatorial searches in materials science, as well as chemistry. However, these data-sets may well be ‘small’ in terms of limited step-size of the measurement variables. Due to this limitation, application of higher-order statistics is not effective, and the choice of a suitable unsupervised learning method is restricted to those utilizing lower-order statistics. As an interesting case study, we present here variable magnetic-field Piezoresponse Force Microscopy (PFM) study of composite multiferroics, where due to experimental limitations the magnetic field dependence of piezoresponse is registered with a coarse step-size. An efficient extraction of this dependence, which corresponds to the local magnetoelectric effect, forms the central problem of this work. We evaluate the performance of Principal Component Analysis (PCA) as a simple unsupervised learning technique, by pre-labeling possible patterns in the data using Density Based Clustering (DBSCAN). Based on this combinational analysis, we highlight how PCA using non-central second-moment can be useful in such cases for extracting information about the local material response and the corresponding spatial distribution. Principal component analysis ; Cluster analysis ; Context (archaeology) ; Unsupervised learning ; Computer science ; Field (mathematics) ; Pattern recognition (psychology) ; Artificial intelligence ; Materials science ; Mathematics Multiferroics and related materials ; Machine Learning in Materials Science ; Ultrasonics and Acoustic Wave Propagation
Authors: Dipankar Mandal
Source: Applied System Innovation (article, 2018-06-20)
Field: Chemistry — Analytical Chemistry

Adaptive Neuro-Fuzzy Inference System Based Grading of Basmati Rice Grains Using Image Processing Technique Grading of rice intents to discriminate broken and whole grain from a sample. Standard techniques for image-based rice grading using advanced statistical methods seldom take into account the domain knowledge associated with the data. In the context of a high product value basmati rice with an image based grading process, one ought to consider the physical properties of grain and the associated knowledge. In this present work, a model of quality grade testing and identification is proposed using a novel digital image processing and knowledge-based adaptive neuro-fuzzy inference system (ANFIS). The rationale behind adopting a grading system based on fuzzy rules relies on capabilities of ANFIS to simulate the behaviour of an expert in the characterization of rice grain using the physical properties of rice grains. The rice kernels are characterized with the help of morphological descriptors and geometric features which are derived from sample images of milled basmati rice. The predictive capability of the proposed technique has been tested on a sufficient number of training and test images of basmati rice grain. The proposed method outperforms with a promising result in an evaluation of rice quality with &gt;98.5% classification accuracy for broken and whole grain as compared to standard machine learning technique viz. support vector machine (SVM) and K-nearest neighbour (KNN). The milling efficiency is also assessed using the ratio between head rice and broken rice percentage and it is 77.27% for the test sample. The overall results of the adopted methodology are promising in terms of classification accuracy and efficiency. Artificial intelligence ; Adaptive neuro fuzzy inference system ; Computer science ; Pattern recognition (psychology) ; Grading (engineering) ; Support vector machine ; Data mining ; Fuzzy logic ; Machine learning ; Mathematics ; Fuzzy control system ; Engineering Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; GABA and Rice Research
Authors: Qiuqin Sun | Fei Lin | Weitao Yan | Feng Wang | She Chen | Lipeng Zhong
Source: Energies (article, 2018-09-17)
Field: Materials Science — Materials Chemistry

Estimation of the Hydrophobicity of a Composite Insulator Based on an Improved Probabilistic Neural Network The estimation of hydrophobicity for composite insulators is of great importance for the purpose of predicting the surface degradation. The hydrophobic image is firstly decomposed by the 2-level wavelet, along with the multi-Retinex algorithm in this paper. The processed low frequency sub-band and high frequency sub-band images are then reconstructed. The 3 × 3 Sobel operator is performed to measure the basic spatial gradient in four directions, including the horizontal direction, the diagonal direction, and then the vertical direction. The shape factor, the area ratio of the largest water droplet, and the coverage rate of the water droplet are selected as the feature parameters and input into the classification network that has been trained to do the hydrophobic level recognition. The effect of the different expansion speed on the desired learning results is discussed. The threshold plays a key role in image processing. Considering that the difference between the water droplet edge and the composite insulator surface is relatively small, the asymptotic semi-soft threshold function is used in pretreatment, whereas the adaptive two-dimensional Otsu’s method is used in image segmentation. The experimental results show that the proposed method has high recognition accuracy up to 94.8% for a diversity of images, and it is superior to the improved Shape Factor Method, the Multi-fractal Method, and the RBF Neural Network. Artificial intelligence ; Artificial neural network ; Pattern recognition (psychology) ; Computer science ; Mathematics ; Computer vision ; Algorithm High voltage insulation and dielectric phenomena ; Image Enhancement Techniques ; Surface Modification and Superhydrophobicity
Authors: Bánk Beszteri | Claire S. Allen | Gastón O. Almandoz | Leanne Armand | María Ángeles Bárcena | Hannelore Cantzler | Xavier Crosta | Oliver Esper | Richard W. Jordan | Gerhard Kauer | Christine Klaas | Michael Kloster | Amy Leventer | Jennifer Pike | Andrés S. Rigual‐Hernández
Source: Journal of Phycology (article, 2018-07-17)
Field: Materials Science — Biomaterials

Quantitative comparison of taxa and taxon concepts in the diatom genus <i>Fragilariopsis</i>: a case study on using slide scanning, multiexpert image annotation, and image analysis in taxonomy<sup>1</sup> Semiautomated methods for microscopic image acquisition, image analysis, and taxonomic identification have repeatedly received attention in diatom analysis. Less well studied is the question whether and how such methods might prove useful for clarifying the delimitation of species that are difficult to separate for human taxonomists. To try to answer this question, three very similar Fragilariopsis species endemic to the Southern Ocean were targeted in this study: F. obliquecostata, F. ritscheri, and F. sublinearis. A set of 501 extended focus depth specimen images were obtained using a standardized, semiautomated microscopic procedure. Twelve diatomists independently identified these specimen images in order to reconcile taxonomic opinions and agree upon a taxonomic gold standard. Using image analyses, we then extracted morphometric features representing taxonomic characters of the target taxa. The discriminating ability of individual morphometric features was tested visually and statistically, and multivariate classification experiments were performed to test the agreement of the quantitatively defined taxa assignments with expert consensus opinion. Beyond an updated differential diagnosis of the studied taxa, our study also shows that automated imaging and image analysis procedures for diatoms are coming close to reaching a broad applicability for routine use. Taxon ; Biology ; Taxonomy (biology) ; Taxonomic rank ; Set (abstract data type) ; Pattern recognition (psychology) ; Genus ; Artificial intelligence ; Evolutionary biology ; Zoology ; Paleontology ; Computer science Diatoms and Algae Research ; Identification and Quantification in Food ; Genomics and Phylogenetic Studies
Authors: Shariful Islam | Sutapa Chowdhury | Shilpi Akter
Source: Journal of Textile Science and Technology (article, 2018-01-01)
Field: Materials Science — Polymers and Plastics

The Experiential Analysis of Woven Fabric for Reproduction The aim of this article was to express the importance of this projected work in the field of present competitive textile-world as fabric manufacturing industries are competing with each other to be unique and aristocrat with their products for attaining customer’s attention and increasing their significance to others. To be superior in market, research is the first priority in this perspective competing field. Industries are producing a wide variety of products with some very good comfort properties in fabrics. Due to achieving this, different properties like construction, weave, internal structure, use of variety of yarn, inner-structural changes are brought in fabrics. The more an industry is advanced with its products, the more it will get market value which is very important. Market research in this field is fundamental prior to going for production. An experimental study may be applied in the market on various products with a view to attaining a good annual turnover with production and manufactures. All these parameters like development, growth, market values etc. are strongly dependent on the analytical and research activities of a group. Considering all, analysis on a fabric and its properties is very much fundament and important. Production (economics) ; Variety (cybernetics) ; Industrial organization ; Reproduction ; Textile ; Field (mathematics) ; Experiential learning ; Market analysis ; Business ; Marketing ; Engineering ; Computer science ; Economics ; Mathematics ; Microeconomics Textile materials and evaluations ; Color perception and design
Authors: Peter Bjørn Jørgensen | Karsten W. Jacobsen | Mikkel N. Schmidt
Source: arXiv (Cornell University) (preprint, 2018-06-08)
Field: Materials Science — Materials Chemistry

Neural Message Passing with Edge Updates for Predicting Properties of\n Molecules and Materials Neural message passing on molecular graphs is one of the most promising\nmethods for predicting formation energy and other properties of molecules and\nmaterials. In this work we extend the neural message passing model with an edge\nupdate network which allows the information exchanged between atoms to depend\non the hidden state of the receiving atom. We benchmark the proposed model on\nthree publicly available datasets (QM9, The Materials Project and OQMD) and\nshow that the proposed model yields superior prediction of formation energies\nand other properties on all three datasets in comparison with the best\npublished results. Furthermore we investigate different methods for\nconstructing the graph used to represent crystalline structures and we find\nthat using a graph based on K-nearest neighbors achieves better prediction\naccuracy than using maximum distance cutoff or the Voronoi tessellation graph.\n Message passing ; Computer science ; Voronoi diagram ; Artificial neural network ; Enhanced Data Rates for GSM Evolution ; Benchmark (surveying) ; Graph ; Theoretical computer science ; Algorithm ; Data mining ; Artificial intelligence ; Mathematics ; Parallel computing Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Graph Neural Networks
Authors: Ross J. Verploegh | Ying Wu | Salah Eddine Boulfelfel | David S. Sholl
Source: The Journal of Physical Chemistry C (article, 2018-02-20)
Field: Chemistry — Inorganic Chemistry

Quantitative Predictions of Molecular Diffusion in Binary Mixed-Linker Zeolitic Imidazolate Frameworks Using Molecular Simulations Experimental studies have shown that adsorbate diffusion in zeolitic imidazolate frameworks (ZIFs) can be tuned by incorporating two different imidazolate linkers in the ZIF crystals. We demonstrate for the first time that atomistic simulations are capable of quantitatively predicting self-diffusion in binary mixed-linker ZIFs. Diffusion coefficients of various adsorbates for which prior experimental data exist are predicted in ZIF-8-90, ZIF-8/SALEM-2 materials composed of imidazolate and 2-methylimidazolate linkers in the cubic SOD topology, and ZIF-7-90 materials over a composition range that is known experimentally to be in the SOD topology. A combination of conventional and biased molecular dynamics simulations as well as a previously developed lattice-diffusion model allows us to access the full range of diffusion time scales for adsorbates such as small hydrocarbons, alcohols, benzene, and water. Zeolitic imidazolate framework ; Imidazolate ; Molecular dynamics ; Diffusion ; Linker ; Chemical physics ; Binary number ; Materials science ; Benzene ; Metal-organic framework ; Topology (electrical circuits) ; Chemistry ; Computational chemistry ; Thermodynamics ; Physical chemistry ; Inorganic chemistry ; Organic chemistry ; Adsorption ; Computer science ; Physics Metal-Organic Frameworks: Synthesis and Applications ; Lanthanide and Transition Metal Complexes ; Magnetism in coordination complexes
Authors: Yue‐Yi Peng | Hui Ma | Wei Ma | Yi‐Tao Long | He Tian
Source: Angewandte Chemie (article, 2018-02-09)
Field: Chemistry — Electrochemistry

Single‐Nanoparticle Photoelectrochemistry at a Nanoparticulate TiO<sub>2</sub>‐Filmed Ultramicroelectrode Abstract An ultrasensitive photoelectrochemical method for achieving real‐time detection of single nanoparticle collision events is presented. Using a micrometer‐thick nanoparticulate TiO 2 ‐filmed Au ultra‐microelectrode (TiO 2 @Au UME), a sub‐millisecond photocurrent transient was observed for an individual N719‐tagged TiO 2 (N719@TiO 2 ) nanoparticle and is due to the instantaneous collision process. Owing to a trap‐limited electron diffusion process as the rate‐limiting step, a random three‐dimensional diffusion model was developed to simulate electron transport dynamics in TiO 2 film. The combination of theoretical simulation and high‐resolution photocurrent measurement allow electron‐transfer information of a single N719@TiO 2 nanoparticle to be quantified at single‐molecule accuracy and the electron diffusivity and the electron‐collection efficiency of TiO 2 @Au UME to be estimated. This method provides a test for studies of photoinduced electron transfer at the single‐nanoparticle level. Ultramicroelectrode ; Photocurrent ; Nanoparticle ; Photoelectrochemistry ; Thermal diffusivity ; Electron transfer ; Materials science ; Microelectrode ; Diffusion ; Nanotechnology ; Electron ; Analytical Chemistry (journal) ; Chemistry ; Optoelectronics ; Electrochemistry ; Electrode ; Photochemistry ; Cyclic voltammetry ; Physics ; Physical chemistry Electrochemical Analysis and Applications ; Electrocatalysts for Energy Conversion ; Advanced Photocatalysis Techniques
Authors: Ahmed Shaker | Eman K. A. Abdelall | Khaled R.A. Abdellatif | Hamdy M. Abdel‐Rahman
Source: Journal of Applied Pharmaceutical Science (article, 2018-11-01)
Field: Chemistry — Organic Chemistry

Design, synthesis, and biological evaluation of 2-(4-(methylsulfonyl) phenyl) indole derivatives with promising COX-2 inhibitory activity A series of 2-(4-(methylsulfonyl)phenyl)-1-substituted-indole derivatives 4a-f was designed and synthesized as indomethacin analogs; All synthesized compounds were assessed for their in vitro COX-2 inhibition effect as well as in vivo anti-inflammatory activity using indomethacin as a reference drug. All synthesized compounds showed good anti-inflammatory activity and more selectivity for COX-2 inhibition. A molecular modeling study was carried out and the results were compatible with that derived from in vitro COX-2 inhibition assays. Antimicrobial ; Indole test ; Pyrimidine ; Inhibitory postsynaptic potential ; Chemistry ; Stereochemistry ; Minimum inhibitory concentration ; Combinatorial chemistry ; Biology ; Organic chemistry Synthesis and biological activity ; Multicomponent Synthesis of Heterocycles ; Synthesis of Indole Derivatives
Authors: Yousef Mohammadi | Alexander Penlidis
Source: Advanced Theory and Simulations (article, 2018-12-20)
Field: Chemistry — Organic Chemistry

Polymerization Data Mining: A Perspective Abstract Polymerization data mining is the art of revealing insights and developing new knowledge from huge amounts of data routinely generated in polymerization systems and polymer characterization (polymerization processes and properties of polymer materials are the specific topic of this article). This becomes possible via development and implementation of robust and versatile intelligent data classifiers/clusterers for precise (numerical) processing of any given large theoretical/experimental datasets. Data mining is capable of effectively “cracking” recipe–microstructure–property interrelationships in modern macromolecular reaction engineering. This work offers a perspective, which contains a brief overview of the current state‐of‐the‐art and history of the area, along with current developments and trends in the data mining field (for polymerizations) with several conceptual examples. All in all, and similar to what is happening in other areas, polymerization data mining is becoming a necessity. The first applications seem promising. Applying molecular simulation approaches and artificial intelligence techniques, the design and establishment of powerful simulators for characterization and processing of virtually synthesized macromolecules are open to future developments, being of paramount importance to both industry and academia. Computer science ; Perspective (graphical) ; Polymerization ; Characterization (materials science) ; Field (mathematics) ; Data science ; Nanotechnology ; Systems engineering ; Polymer ; Artificial intelligence ; Materials science ; Engineering Advanced Polymer Synthesis and Characterization ; Analytical Chemistry and Chromatography ; Machine Learning in Materials Science
Authors: Marcin Maździarz | Adam Mrozek | Wacław Kuś | Tadeusz Burczyński
Source: Materials (article, 2018-03-16)
Field: Materials Science — Materials Chemistry

Anisotropic-Cyclicgraphene: A New Two-Dimensional Semiconducting Carbon Allotrope A potentially new, single-atom thick semiconducting 2D-<i>graphene</i>-like material, called <i>Anisotropic-cyclicgraphene</i> , has been generated by the two stage searching strategy linking molecular and ab initio approach. The candidate was derived from the evolutionary-based algorithm and molecular simulations was then profoundly analysed using first-principles density functional theory from the structural, mechanical, phonon, and electronic properties point of view. The proposed polymorph of <i>graphene</i> (<i>rP16</i>-P1m1) is mechanically, dynamically, and thermally stable and can achieve semiconducting with a direct band gap of 0.829 eV. Density functional theory ; Band gap ; Ab initio ; Carbon fibers ; Electronic structure ; Electronic band structure ; Ab initio quantum chemistry methods ; Point (geometry) 2D Materials and Applications ; Graphene research and applications ; Boron and Carbon Nanomaterials Research
Authors: Marcus Weber
Source: Computation (article, 2018-02-19)
Field: Chemistry — Spectroscopy

Implications of PCCA+ in Molecular Simulation Upon ligand binding or during chemical reactions the state of a molecular system changes in time. Usually we consider a finite set of (macro-) states of the system (e.g., ‘bound’ vs. ‘unbound’), although the process itself takes place in a continuous space. In this context, the formula χ = X A connects the micro-dynamics of the molecular system to its macro-dynamics. χ can be understood as a clustering of micro-states of a molecular system into a few macro-states. X is a basis of an invariant subspace of a transfer operator describing the micro-dynamics of the system. The formula claims that there is an unknown linear relation A between these two objects. With the aid of this formula we can understand rebinding effects, the electron flux in pericyclic reactions, and systematic changes of binding rates in kinetic ITC experiments. We can also analyze sequential spectroscopy experiments and rare event systems more easily. This article provides an explanation of the formula and an overview of some of its consequences. Molecular dynamics ; Statistical physics ; Context (archaeology) ; Operator (biology) ; Invariant (physics) ; Pericyclic reaction ; Linear subspace ; Basis (linear algebra) ; Computational chemistry ; Chemistry ; Computer science ; Mathematics ; Physics ; Pure mathematics ; Quantum mechanics Mass Spectrometry Techniques and Applications ; Protein Structure and Dynamics ; Nanopore and Nanochannel Transport Studies
Authors: Akihiko Sakata | Tomohisa Okada | Yosuke Yamamoto | Yasutaka Fushimi | Toshiki Dodo | Yoshiki Arakawa | Yohei Mineharu | Benjamin Schmitt | Susumu Miyamoto | Kaori Togashi
Source: American Journal of Neuroradiology (article, 2018-01-04)
Field: Materials Science — Materials Chemistry

Addition of Amide Proton Transfer Imaging to FDG-PET/CT Improves Diagnostic Accuracy in Glioma Grading: A Preliminary Study Using the Continuous Net Reclassification Analysis Addition of amide proton transfer imaging to FDG-PET/CT may improve the ability to differentiate high-grade from low-grade gliomas. Medicine ; Glioma ; Nuclear medicine ; Grading (engineering) ; Amide ; Receiver operating characteristic ; Area under the curve ; Radiology ; Internal medicine ; Chemistry ; Cancer research Lanthanide and Transition Metal Complexes ; Advanced MRI Techniques and Applications ; Medical Imaging Techniques and Applications
Authors: Osman Karslıoğlu | Mathias Gehlmann | Juliane Müller | Slavomír Nemšák | James A. Sethian | A. P. Kaduwela | Hendrik Bluhm | C. S. Fadley
Source: Journal of Electron Spectroscopy and Related Phenomena (article, 2018-10-29)
Field: Materials Science — Materials Chemistry

An Efficient Algorithm for Automatic Structure Optimization in X-ray Standing-Wave Experiments Algorithm ; Optimization algorithm ; Computer science ; Process (computing) ; Synchrotron ; Optimization problem ; Mathematical optimization ; Optics ; Physics ; Mathematics Machine Learning in Materials Science ; Electron and X-Ray Spectroscopy Techniques ; Advancements in Photolithography Techniques