jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Samir Kamel | Mohamed El‐Sakhawy | Badawi Anis | Hebat‐Allah S. Tohamy
Source: Egyptian Journal of Chemistry (review, 2019-10-02)
Field: Materials Science — Materials Chemistry

Graphene: Structure, Synthesis, and Characterization; a brief review In 2004, Andrei Geim and Kostya Novoselov used a simple technique to separate graphene layer from graphite. They were awarded the Nobel Prize in Physics in 2010. After this, the publications of graphene have been increasing year after year and have emerged as the most popular topic in the research field which indicates the importance of graphene research. Due to unique properties of graphene, it can be considered as promising material for various applications in the fields of physics, chemistry, material science and engineering, and biology. Consequently, this review will focus on using of biomass and other wastes as precursor material for graphene preparation. We hope it could be of any help to people who are interested in this field. The future outlook of graphene is also considered. Characterization (materials science) ; Materials science ; Nanotechnology Graphene research and applications
Authors: Massimiliano Comin | Laurent J. Lewis
Source: Physical review. B./Physical review. B (article, 2019-09-16)
Field: Materials Science — Materials Chemistry

Deep-learning approach to the structure of amorphous silicon We present a deep-learning approach for modeling the atomic structure of amorphous silicon ($a$-Si). While accurate models of disordered systems require an ab initio description of the energy landscape which severely limits the attainable system size, large-scale models rely on empirical potentials, at the price of reduced reliability and a computational load that is still restricting for many purposes. In this paper, we explore an approach based on deep learning, particularly generative modeling that could reconcile both requirements of accuracy and efficiency by learning structural features from data. When trained on a set of observations, such models can generate new structures very efficiently with the desired level of accuracy, as determined by the data set. We first validate our approach by training a convolutional neural network to approximate the potential-energy surface of $a$-Si, as given by the Stillinger-Weber potential, which results in a root-mean-square error of 5.05 meV per atom---about $0.16%$ of the atomic energy. We then train a deep generative model, the Wasserstein autoencoder, for the generation of $a$-Si configurations. Our approach leads to models which exhibit some of the essential features of $a$-Si while possessing too much structural disorder, thus suggesting that the method is viable; we indicate avenues for improving it towards the generation of state-of-the-art structures. Deep learning ; Autoencoder ; Computer science ; Artificial intelligence ; Convolutional neural network ; Generative grammar ; Set (abstract data type) ; Silicon ; Ab initio ; Artificial neural network ; Atom (system on chip) ; Machine learning ; Materials science ; Physics ; Quantum mechanics Machine Learning in Materials Science ; Thin-Film Transistor Technologies ; Theoretical and Computational Physics
Authors: Kevin R. Talley | Rachel Sherbondy | Andriy Zakutayev | Geoff L. Brennecka
Source: Journal of Vacuum Science & Technology A Vacuum Surfaces and Films (article, 2019-10-24)
Field: Materials Science — Materials Chemistry

Review of high-throughput approaches to search for piezoelectric nitrides Piezoelectric materials are commonplace in modern devices, and the prevalence of these materials is poised to increase in the years to come. The majority of known piezoelectrics are oxide materials, due in part to the related themes of a legacy of ceramists building off of mineralogical crystallography and the relative simplicity of fabricating oxide specimens. However, diversification beyond oxides offers exciting opportunities to identify and develop new materials perhaps better suited for certain applications. Aluminum nitride (and recently, its Sc-modified derivative) is the only commercially integrated piezoelectric nitride in use today, although this is likely to change in the near future with increased use of high-throughput techniques for materials discovery and development. This review covers modern methods—both computational and experimental—that have been developed to explore chemical space for new materials with targeted characteristics. Here, the authors focus on the application of computational and high-throughput experimental approaches to discovering and optimizing piezoelectric nitride materials. While the focus of this review is on the search for and development of new piezoelectric nitrides, most of the research approaches discussed in this article are both chemistry- and application-agnostic. Nitride ; Piezoelectricity ; Throughput ; Nanotechnology ; Materials science ; Focus (optics) ; Computer science ; Engineering physics ; Engineering ; Physics ; Telecommunications ; Composite material Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides ; Acoustic Wave Resonator Technologies
Authors: Vitalii Lozovan | Ruslan Skrynkovskyy | Volodymyr Yuzevych | Mykhailo Yasinskyi | Grzegorz Pawlowski
Source: Eastern-European Journal of Enterprise Technologies (article, 2019-04-02)
Field: Materials Science — Materials Chemistry

Forming the toolset for development of a system to control quality of operation of underground pipelines by oil and gas enterprises with the use of neural networks A set of defining parameters for modeling stages of a surface defect propagation in the outer surface of a metal pipeline taking into consideration fatigue strength has been formed.\n\nFor a section of a pipeline with a surface defect, it was proposed to use an algorithm of forecasting polarization potentials using means of neural networks. A procedure of functioning of the testing set was elaborated for estimating efficiency of neural networks. The procedure includes appropriate training methods.\n\nAccording to the results of analysis of interconnected deformation and corrosion processes, elements of a methodology of formation of information support for forecasting service life of a linear part of underground metal pipelines taking into consideration corrosion fatigue have been developed.\n\nKnown results of estimation of service life of underground metal pipelines assumed linear nature of corrosion rate. Relevant information was presented in international and national standards. Recent experimental studies have shown that it is advisable to take into consideration nonlinear nature of corrosion rate in the outer surface of underground metal pipelines (BMP).\n\nA BMP section was inspected with the aid of a polarization potential meter together with a contactless current meter and principles of using neural networks for processing experimental results were formulated. An example of actual BMP was considered and analyzed for metal of a pipe of 17G1S grade steel with a corrosion defect in its outer surface. This analysis has resulted in estimation of metal service life and revealed nonlinearity characterized by magnitude of d=1.136.\n\nA control method and procedures for estimating polarization potentials with the help of neural networks were proposed. They make it possible to describe the process of corrosion defect propagation into the depth of the pipe wall physically sound and mathematically more correct in contrast to the standard procedures.\nThe information presented is important for improving methods of control of underground metal pipelines operated by oil and gas enterprises, in particular, methods of correct measurement and evaluation of polarization potentials and anode currents in insulation defects taking into consideration nonlinearity of informative parameters Corrosion ; Pipeline transport ; Service life ; Artificial neural network ; Nonlinear system ; Polarization (electrochemistry) ; Engineering ; Structural engineering ; Computer science ; Materials science ; Mechanical engineering ; Metallurgy ; Artificial intelligence Material Properties and Failure Mechanisms ; Engineering Diagnostics and Reliability ; Non-Destructive Testing Techniques
Authors: Bidisha Samanta | Abir De | Gourhari Jana | Pratim Kumar Chattaraj | Niloy Ganguly | Manuel Gomez-Rodriguez
Source: Proceedings of the AAAI Conference on Artificial Intelligence (preprint, 2019-07-17)
Field: Materials Science — Materials Chemistry

NeVAE: A Deep Generative Model for Molecular Graphs Deep generative models have been praised for their ability to learn smooth latent representation of images, text, and audio, which can then be used to generate new, plausible data. However, current generative models are unable to work with molecular graphs due to their unique characteristics—their underlying structure is not Euclidean or grid-like, they remain isomorphic under permutation of the nodes labels, and they come with a different number of nodes and edges. In this paper, we propose NeVAE, a novel variational autoencoder for molecular graphs, whose encoder and decoder are specially designed to account for the above properties by means of several technical innovations. In addition, by using masking, the decoder is able to guarantee a set of valid properties in the generated molecules. Experiments reveal that our model can discover plausible, diverse and novel molecules more effectively than several state of the art methods. Moreover, by utilizing Bayesian optimization over the continuous latent representation of molecules our model finds, we can also find molecules that maximize certain desirable properties more effectively than alternatives. Autoencoder ; Computer science ; Representation (politics) ; Stability (learning theory) ; Generative model ; Property (philosophy) ; Generative grammar ; Artificial intelligence ; Euclidean geometry ; Algorithm ; Encoder ; Theoretical computer science ; Pattern recognition (psychology) ; Deep learning ; Machine learning ; Mathematics Machine Learning in Materials Science ; Topic Modeling ; Protein Structure and Dynamics
Authors: Maxim Tsypin | Senait Asmellash | Krista Meyer | Brandon Touchet | Heinrich Röder
Source: PLoS ONE (article, 2019-12-09)
Field: Chemistry — Spectroscopy

Extending the information content of the MALDI analysis of biological fluids via multi-million shot analysis We demonstrate that substantially increasing the number of laser shots in a MALDI-TOF analysis leads to more informative and reliable data on the protein content of unfractionated serum. This approach has already been used in the development of clinical tests in oncology. Reproducibility ; Concordance ; Shot noise ; Analytical Chemistry (journal) ; Spectral line ; Chromatography ; Chemistry ; Biological system ; Physics ; Bioinformatics ; Optics ; Biology Mass Spectrometry Techniques and Applications ; Bacterial Identification and Susceptibility Testing ; Advanced Proteomics Techniques and Applications
Authors: Charlie S. Marzan | Conrado Ruiz
Source: International Journal of Machine Learning and Computing (article, 2019-12-01)
Field: Chemistry — Analytical Chemistry

Automated Tobacco Grading Using Image Processing Techniques and a Convolutional Neural Network Tobacco grading is very important for crop market price determination. It is beneficial for graders who need to manually classify tobacco leaves according to their grades. As such, a grading system using image processing techniques and a Convolutional Neural Network (CNN) is proposed in this study which aims to surpass existing results in tobacco grading. The system consists of image acquisition, pre-processing, leaf detection, segmentation, and classification. Tobacco leaf images were directly taken at the tobacco grading room and pre-processed for subsequent tasks. Through a Haar cascade classifier and applying image processing techniques, air-cured tobacco leaves are automatically detected and extracted in images. This method produced satisfactory results as it can successfully detect single and multiple tobacco leaves taken under different positions and scale. All detected tobacco leaves underwent various image processing to precisely segment leaves from the rest of the image. The experimental results also reveal that using segmented and nonsegmented images, CNN classifier can effectively grade tobacco leaves as high as 96.25% accuracy rate and on average, took 7.43 ms to classify a single tobacco leaf. This approach outperforms current methods in grading tobacco leaves. Computer science ; Convolutional neural network ; Grading (engineering) ; Artificial intelligence ; Artificial neural network ; Computer vision ; Pattern recognition (psychology) Spectroscopy and Chemometric Analyses ; AI in cancer detection
Authors: Mathias Wiegmann | Andreas Backhaus | Udo Seiffert | William Thomas | Andrew J. Flavell | Klaus Pillen | Andreas Maurer
Source: PLoS ONE (article, 2019-11-07)
Field: Chemistry — Analytical Chemistry

Optimizing the procedure of grain nutrient predictions in barley via hyperspectral imaging Hyperspectral imaging enables researchers and plant breeders to analyze various traits of interest like nutritional value in high throughput. In order to achieve this, the optimal design of a reliable calibration model, linking the measured spectra with the investigated traits, is necessary. In the present study we investigated the impact of different regression models, calibration set sizes and calibration set compositions on prediction performance. For this purpose, we analyzed concentrations of six globally relevant grain nutrients of the wild barley population HEB-YIELD as case study. The data comprised 1,593 plots, grown in 2015 and 2016 at the locations Dundee and Halle, which have been entirely analyzed through traditional laboratory methods and hyperspectral imaging. The results indicated that a linear regression model based on partial least squares outperformed neural networks in this particular data modelling task. There existed a positive relationship between the number of samples in a calibration model and prediction performance, with a local optimum at a calibration set size of ~40% of the total data. The inclusion of samples from several years and locations could clearly improve the predictions of the investigated nutrient traits at small calibration set sizes. It should be stated that the expansion of calibration models with additional samples is only useful as long as they are able to increase trait variability. Models obtained in a certain environment were only to a limited extent transferable to other environments. They should therefore be successively upgraded with new calibration data to enable a reliable prediction of the desired traits. The presented results will assist the design and conceptualization of future hyperspectral imaging projects in order to achieve reliable predictions. It will in general help to establish practical applications of hyperspectral imaging systems, for instance in plant breeding concepts. Hyperspectral imaging ; Calibration ; Partial least squares regression ; Set (abstract data type) ; Data set ; Statistics ; Linear model ; Computer science ; Regression ; Biological system ; Linear regression ; Population ; Data mining ; Mathematics ; Pattern recognition (psychology) ; Artificial intelligence ; Biology Spectroscopy and Chemometric Analyses ; Leaf Properties and Growth Measurement ; Horticultural and Viticultural Research
Authors: Canyi Huang | Jianqiang Du | Bin Nie | Riyue Yu | Wangping Xiong | Qingxia Zeng
Source: Computational and Mathematical Methods in Medicine (article, 2019-07-01)
Field: Chemistry — Analytical Chemistry

Feature Selection Method Based on Partial Least Squares and Analysis of Traditional Chinese Medicine Data The partial least squares method has many advantages in multivariable linear regression, but it does not include the function of feature selection. This method cannot screen for the best feature subset (referred to in this study as the "Gold Standard") or optimize the model, although contrarily using the L1 norm can achieve the sparse representation of parameters, leading to feature selection. In this study, a feature selection method based on partial least squares is proposed. In the new method, exploiting partial least squares allows extraction of the latent variables required for performing multivariable linear regression, and this method applies the L1 regular term constraint to the sum of the absolute values of the regression coefficients. This technique is then combined with the coordinate descent method to perform multiple iterations to select a better feature subset. Analyzing traditional Chinese medicine data and University of California, Irvine (UCI), datasets with the model, the experimental results show that the feature selection method based on partial least squares exhibits preferable adaptability for traditional Chinese medicine data and UCI datasets. Partial least squares regression ; Feature selection ; Computer science ; Pattern recognition (psychology) ; Artificial intelligence ; Data mining ; Mathematics ; Machine learning Spectroscopy and Chemometric Analyses ; Grey System Theory Applications ; Traditional Chinese Medicine Studies
Authors: Alon Grinberg Dana | Mengjie Liu | William H. Green
Source: International Journal of Chemical Kinetics (article, 2019-07-09)
Field: Chemistry — Organic Chemistry

Automated chemical resonance generation and structure filtration for kinetic modeling Abstract This work discusses efficient and automated methods for constructing a set of representative resonance structures for arbitrary chemical species, including radicals and biradicals, consisting of the elements H, C, O, N, and S. Determining the representative reactive structures of chemical species is crucial for identification of reactive sites and consequently applying the correct reaction templates to generate the set of important reactions during automated chemical kinetic model generation. We describe a fundamental set of resonance pathway types, accounting for simple resonating structures, as well as global approaches for polycyclic aromatic species. Automatically discovering potential localized structures along with filtration to identify the representative structures was shown to be robust and relatively fast. The algorithms discussed here were recently implemented in the Reaction Mechanism Generator (RMG) software. The final structures proposed by this method were found to be in reasonable agreement with quantum chemical computation results of localized structure contributions to the resonance hybrid. Chemistry ; Resonance (particle physics) ; Biological system ; Quantum chemical ; Generator (circuit theory) ; Computation ; Template ; Kinetic energy ; Chemical reaction ; Radical ; Work (physics) ; Chemical physics ; Molecule ; Computational chemistry ; Algorithm ; Nanotechnology ; Computer science ; Atomic physics ; Physics ; Thermodynamics ; Organic chemistry ; Materials science ; Quantum mechanics Free Radicals and Antioxidants ; Advanced Chemical Physics Studies ; Photochemistry and Electron Transfer Studies
Authors: Ante Miličević
Source: Archives of Industrial Hygiene and Toxicology (article, 2019-06-01)
Field: Chemistry — Organic Chemistry

The relationship between antioxidant activity, first electrochemical oxidation potential, and spin population of flavonoid radicals I have shown that by averaging antioxidant activity (AA) values measured by different methods it is possible to obtain an excellent correlation (R2=0.960) between the first electrochemical oxidation potential, Ep1, and AA. Separate correlations using the AA values obtained with each of the four methods [R2 were 0.561 for diphenyl-1-picrylhydrazyl (DPPH), 0.849 for Folin Ciocalteu reagent (FCR), 0.848 for the ferric-reducing ability of plasma (FRAP), and 0.668 for the Trolox equivalent antioxidant capacity (TEAC)] were all worse, and in some cases not useful at all, such as the one for DPPH. Also, the sum of atomic orbital spin populations on the carbon atoms in the skeleton of radicals ( s(C) Σ AOSPRad), calculated with the semi-empirical parameterisation method 6 (PM6) in water, was used to correlate both Ep1 and AA, yielding R2=0.926 and 0.950, respectively. This showed to be a much better variable for the estimation of Ep1 and AA than the bond dissociation energy (BDE), R2=0.854 and 0.901 for Ep1 and AA, respectively, and especially the ionisation potential (IP), R2=0.445 and 0.435 for Ep1 and AA, respectively. Radical ; Flavonoid ; Antioxidant ; Chemistry ; Electrochemistry ; Population ; Photochemistry ; Organic chemistry ; Physical chemistry ; Medicine ; Electrode ; Environmental health Free Radicals and Antioxidants ; Phytochemicals and Antioxidant Activities ; Antioxidant Activity and Oxidative Stress
Authors: Thang Q. Tran | Sangjun Lee | Sangin Kim
Source: Scientific Reports (article, 2019-08-29)
Field: Materials Science — Electronic, Optical and Magnetic Materials

A graphene-assisted all-pass filter for a tunable terahertz transmissive modulator with near-perfect absorption We proposed an all-pass filter based perfect absorber scheme which also can function as a highly efficient transmissive modulator. We theoretically analyzed the proposed scheme using the temporal coupled mode theory and showed that near-perfect absorption could be achieved with practically modest deviation from the critical coupling condition. We also demonstrated the feasibility of the proposed scheme in a grating-based all-pass filter device with a variable loss implemented by two separate graphene layers, achieving an absorption of ~99.8% and a transmission modulation depth of ~70 dB in a terahertz frequency range. We also numerically investigated the tunability of the designed device. Terahertz radiation ; Materials science ; Filter (signal processing) ; Absorption (acoustics) ; Optics ; Graphene ; Modulation (music) ; Transmission (telecommunications) ; Grating ; Coupling (piping) ; Optoelectronics ; Physics ; Computer science ; Telecommunications ; Acoustics ; Nanotechnology Metamaterials and Metasurfaces Applications ; Plasmonic and Surface Plasmon Research ; Photonic and Optical Devices