jablonkagroup/rise_ai_scientists huggingface dataset:
Source: Advanced Intelligent Systems (article, 2019-05-01)
Field: Materials Science — Materials Chemistry

<i>Advanced Intelligent Systems</i>: Keeping Up with Intelligent Systems in an Era of Rapid Transformation Advanced Intelligent Systems: Keeping Up with Intelligent Systems in an Era of Rapid TransformationWe are currently in an inflexion point in the history of technology referred to by some as the Fourth Industrial Revolution. [1]This rapid change to the way we live, work, and communicate is driven by the exponential increase in data fueled by the rapid adoption of internetenabled devices.Many of these technologies have, in turn, undergone a rapid evolution due to the progress in artificial intelligence (AI) algorithms.As the world-wide-web celebrates its 30th anniversary, [2] AI now pervades nearly every aspect of our lives.[7] The potential of intelligent systems as well as the concerns and the fears they engender are widely discussed both within academia and in the wider media, [8] and scientific journals play a crucial role in facilitating this conversation.The Advanced journals have published developments in science and technology for over 30 years with titles covering topics such as robotics, sensing, and neuromorphic computing.The growth in these areas of research has led us to launch new journals to cover these topics in more depth.In 2016, we successfully launched Advanced Materials Technologies to encompass technology-related research in materials science.At the end of 2018, we started a special series within that journal to showcase advances in the field of intelligent systems.The popularity of the series underlines the pressing need for a journal dedicated to interdisciplinary, holistic research on hard and soft intelligence.Advanced Intelligent Systems is an open access journal focused on various aspects of intelligent systems that combine hard intelligence (including robotics and self-assembly) with soft intelligence (such as artificial intelligence and machine learning).Crucially, the journal also welcomes essays on the ethical and legal aspects of intelligent systems.To cater to this rapidly evolving area of research, accepted articles are published online in advance of the final edited and typeset version of record, as part of the journal launch phase.These articles are citable via the digital object identifier (DOI) and appear online within a few days of acceptance.The article processing charge (APC) is waived in 2019 making articles in Advanced Intelligent Systems both free to read and free to publish.Advanced Intelligent Systems is supported by a topically and geographically diverse advisory board of leading scientists who reflect the active nature of this vibrant community. Computer science ; World Wide Web ; The Internet ; Intelligent decision support system ; Simple (philosophy) ; Point (geometry) ; Citation ; Data science ; Artificial intelligence Machine Learning in Materials Science
Authors: Juan Velez Alvarez | Álvaro Bastidas | Alejandra Monsalve | Tehseen Adel | Isabel Calle | Rafael Arango
Source:  (preprint, 2019-06-17)
Field: Chemistry — Analytical Chemistry

Inelastic scattering imaging system for <i>Pseudocercospora fijiensis</i> detection This work sought to develop an inelastic scattering imaging system based on Raman spectroscopy for the detection of the fungal phytopathogen, Pseudocercospora fijiensis , which causes Black Sigatoka disease in banana crops, very important in Colombian agro-industrial economy. This system consists of a modified stereoscope with an optical setup able to simultaneously capture spectral images together with its Raman spectra. The camera has two different bandpass filters attached, centered in the spectral region of C=O stretching of Chitin and the equatorial bending vibration of beta-1,3-glucan, molecules of the fungal cell wall. In this way, the system can get images with unique spectral features, suitable for training a convolutional neural network in order to get a recognition pattern of the fungal strain growing in the PDA agar. As a result, the instrument was able to detect the presence of P.fijiensis over the culture media. Opacity ; Optics ; Raman spectroscopy ; Light scattering ; Materials science ; Raman scattering ; Physics ; Scattering Spectroscopy and Chemometric Analyses
Authors: Juan Velez Alvarez | Álvaro Bastidas | Alejandra Monsalve | Tehseen Adel | Isabel Calle | Rafael Arango
Source:  (preprint, 2019-06-17)
Field: Chemistry — Analytical Chemistry

Inelastic scattering imaging system for <i>Pseudocercospora fijiensis </i> detection This work sought to develop an inelastic scattering imaging system based on Raman spectroscopy for the detection of the fungal phytopathogen, Pseudocercospora fijiensis , which causes Black Sigatoka disease in banana crops, very important in Colombian agro-industrial economy. This system consists of a modified stereoscope with an optical setup able to simultaneously capture spectral images together with its Raman spectra. The camera has two different bandpass filters attached, centered in the spectral region of C=O stretching of Chitin and the equatorial bending vibration of beta-1,3-glucan, molecules of the fungal cell wall. In this way, the system can get images with unique spectral features, suitable for training a convolutional neural network in order to get a recognition pattern of the fungal strain growing in the PDA agar. As a result, the instrument was able to detect the presence of P.fijiensis over the culture media. Opacity ; Optics ; Raman spectroscopy ; Materials science ; Raman scattering ; Physics ; Computer science Spectroscopy and Chemometric Analyses
Authors: Mohammad Atif Faiz Afzal | Johannes Hachmann
Source: arXiv (Cornell University) (preprint, 2019-02-11)
Field: Materials Science — Materials Chemistry

High-Throughput Computational Studies in Catalysis and Materials Research, and their Impact on Rational Design In the 21st century, many technology fields have become reliant on advancements in process automation. We have seen dramatic growth in areas and industries that have successfully implemented a high level of automation. In drug discovery, for example, it has alleviated an otherwise extremely complex and tedious process and has resulted in the development of several new drugs. Over the last decade, these automation techniques have begun being adapted in the chemical and materials community as well with the goal of exploring chemical space and pursuing the discovery and design of novel compounds for various applications. The impact of new materials on industrial and economic development has been stimulating tremendous research efforts by the materials community, and embracing automation as well as tools from computational and data science have led to acceleration and streamlining of the discovery process. In particular, virtual high-throughput screening (HTPS) is now becoming a mainstream technique to search for materials with properties that are tailored for specific applications. Its efficiency combined with the increasing availability of open-source codes and large computational resources makes it a powerful and attractive tool in materials research. Herein, we will review a selection of recent, high-profile HTPS projects for new materials and catalysts. In the case of catalysts, we focus on the HTPS studies for oxygen reduction reaction, oxygen evolution reaction, hydrogen evolution reaction, and carbon dioxide reduction reaction. Whereas, for other materials applications, we emphasize on the HTPS studies for photovoltaics, gas separation, high-refractive-index materials, and OLEDs. Automation ; Throughput ; Chemical space ; Computer science ; Process (computing) ; Nanotechnology ; Biochemical engineering ; Process engineering ; Data science ; Drug discovery ; Materials science ; Engineering ; Chemistry ; Mechanical engineering ; Telecommunications Machine Learning in Materials Science ; Cloud Computing and Resource Management ; Catalysis and Oxidation Reactions
Authors: 一旗 宮里
Source: Hokkaido University Collection of Scholarly and Academic Papers (Hokkaido University) (article, 2019-03-25)
Field: Materials Science — Materials Chemistry

Exploration of novel two dimensional materials by using first principle calculation and materials informatics approach boron based two dimensional materials. . . . . . . Computer science ; Informatics ; Materials informatics ; Calculus (dental) ; Data science ; Health informatics ; Engineering ; Political science ; Engineering informatics ; Medicine ; Law Machine Learning in Materials Science
Authors: Chiara Panosetti | Artur Engelmann | Lydia Nemec | Karsten Reuter | Johannes T. Margraf
Source: ChemRxiv (preprint, 2019-10-02)
Field: Materials Science — Materials Chemistry

Learning to Use the Force: Fitting Repulsive Potentials in Density-Functional Tight-Binding with Gaussian Process Regression The Density-Functional Tight Binding (DFTB) method is a popular semiempirical approximation to Density Functional Theory (DFT). In many cases, DFTB can provide comparable accuracy to DFT at a fraction of the cost, enabling simulations on length- and time-scales that are unfeasible with first principles DFT. At the same time (and in contrast to empirical interatomic potentials and force-fields), DFTB still offers direct access to electronic properties such as the band-structure. These advantages come at the cost of introducing empirical parameters to the method, leading to a reduced transferability compared to true first-principle approaches. Consequently, it would be very useful if the parameter-sets could be routinely adjusted for a given project. While fairly robust and transferable parameterization workflows exist for the electronic structure part of DFTB, the so-called repulsive potential Vrep poses a major challenge. In this paper we propose a machine-learning (ML) approach to fitting Vrep, using Gaussian Process Regression (GPR). The use of GPR circumvents the need for non-linear or global parameter optimization, while at the same time offering arbitrary flexibility in terms of the functional form. We also show that the proposed method can be applied to multiple elements at once, by fitting repulsive potentials for organic molecules containing carbon, hydrogen and oxygen. Overall, the new approach removes focus from the choice of functional form and parameterization procedure, in favour of a data-driven philosophy. Density functional theory ; Computer science ; Gaussian ; Workflow ; Flexibility (engineering) ; Gaussian process ; Tight binding ; Statistical physics ; Electronic structure ; Mathematical optimization ; Computational chemistry ; Chemistry ; Mathematics ; Physics ; Statistics Machine Learning in Materials Science ; Mass Spectrometry Techniques and Applications ; Metabolomics and Mass Spectrometry Studies
Authors: Xiang Tian
Source: SHAREOK (University of Oklahoma; Oklahoma State University; Central Oklahoma University) (article, 2019-12-01)
Field: Chemistry — Spectroscopy

Single-probe Mass Spectrometry Imaging: Applications and Advanced Data Analysis Mass spectrometry imaging (MSI) is becoming a powerful tool in the bioanalytical studies owing to its unique capability to sensitively map the spatial distribution of broad ranges of molecules on biological samples. Due to the large size and complex structure of the image datasets, conventional analysis methods, such as directly mapping the selected ions, is insufficient to achieve comprehensive data analysis. To increase the data analysis efficiency and fully extract the information contained in MS image data, advanced data analysis methods are needed. This dissertation focuses on the studies using the combined Single-probe MSI method, an ambient MSI technique, with advanced data analysis, including multivariate curve resolution (MCR), machine learning (ML), and multi-modal imaging fusion. MCR is a technique to decompose the hyperdimensional dataset into major components, which possess similar spatial distributions, and extract molecules from each component. ML is becoming increasingly popular for analyzing MSI data due to its superior capability to deal with big data. Here, both supervised and unsupervised ML methods were used to segment the MSI data, providing fast and accurate approaches to image segmentation. In addition, image fusion technique was applied to enhance the MSI data analysis, in which microscope images and MS images were fused together to increase the spatial resolution and correlate the spatial information of protein biomarkers and metabolites. The integration of the Single-probe MSI experimental techniques and advanced data analysis methods can potentially benefit fundamental research and broad types of applications such as in drug discovery and studies of disease. Mass spectrometry ; Mass spectrometry imaging ; Computer science ; Chemistry ; Chromatography Mass Spectrometry Techniques and Applications
Authors: Ibrahim F. Waheed | Faiz M. AL-Abady | Baidaa M. Ali
Source: Tikrit Journal of Pure Science (article, 2019-12-23)
Field: Materials Science — Materials Chemistry

Synthesis of Mg-Ferrite nanoparticles via auto combustion method and investigation their structural, morphological and magnetic properties Magnesium ferrite (MgFe2O4) nanoparticles is prepared by sol-gel auto combustion method and calcinated at (200,450, 900) °C. The capping agent was urea and (Mg(NO3)2.6H2O) and (Fe(NO3)3.9H2O) nitrates as sources of metal. X-ray diffraction (XRD) and Fourier transform infrared spectroscopy (FT-IR) characteristic show clear modes of the cubic Mg-ferrite structure formation. Infrared spectrum of metal-oxygen vibration at (703-636) and (574-433) cm−1 show the tetrahedral and octahedral site of Mg-ferrite structure. Scanning Electron Microscope (SEM) images shown pure crystalline microstructure with polyhedral shapes and very small numbers of globular small particles. The crystallite size of Mg-ferrite is calculated using Debye-Scherrer relation and was in the range of 29 nm. Crystallite ; Materials science ; Ferrite (magnet) ; Scanning electron microscope ; Microstructure ; Nanoparticle ; Octahedron ; Fourier transform infrared spectroscopy ; Analytical Chemistry (journal) ; Infrared spectroscopy ; Crystallography ; Crystal structure ; Chemical engineering ; Composite material ; Metallurgy ; Nanotechnology ; Chemistry ; Chromatography Magnetic Properties and Synthesis of Ferrites ; Iron oxide chemistry and applications ; Advancements in Battery Materials
Authors: Dr. Amar Nath Jain
Source: International Journal of Chemical Studies (article, 2019-01-01)
Field: Materials Science — Materials Chemistry

Synthesis, characterization and applications of perovskite oxide nanomaterials What makes perovskite oxide nanomaterials one of the most intensively studied classes of functional ceramics in modern materials science? This review examines the principal synthesis routes, characterization techniques, and applications of ABO?-type perovskite oxide nanomaterials reported in the literature up to 2018. Perovskite oxides, first identified as the mineral CaTiO? in 1839, have evolved into a remarkably versatile family of compounds whose properties can be tuned through compositional substitution at the A- and B-site cation positions. Synthesis methods surveyed include solid-state reaction, sol-gel, co-precipitation, hydrothermal, microwave-assisted, and combustion techniques, each offering distinct advantages in controlling particle size, morphology, phase purity, and surface area. Characterization data drawn from X-ray diffraction, scanning and transmission electron microscopy, Fourier transform infrared spectroscopy, thermogravimetric analysis, and BET surface area measurements are discussed to illustrate how synthesis parameters govern the structural and morphological features of the resulting nanomaterials. Crystallite sizes in the range of 15 to 100 nm have been achieved through wet-chemical routes, with sol-gel and combustion methods consistently yielding single-phase perovskite powders at calcination temperatures between 600 and 900 degrees Celsius. Applications spanning heterogeneous catalysis, solid oxide fuel cells, electrochemical sensing, environmental remediation, and energy storage are reviewed, with emphasis on how nanoscale dimensions enhance catalytic activity, ionic conductivity, and surface reactivity compared to bulk counterparts. The findings indicate that partial substitution of A- and B-site cations generates oxygen vacancies and mixed oxidation states that are central to the functional performance of these materials. This review aims to provide a consolidated reference for researchers working on perovskite oxide nanomaterials and to identify directions for future investigation. Perovskite (structure) ; Nanomaterials ; Oxide ; Thermogravimetric analysis ; Calcination ; Crystallite ; Characterization (materials science) ; Specific surface area Advancements in Solid Oxide Fuel Cells ; Pigment Synthesis and Properties ; Catalytic Processes in Materials Science
Authors: Liam Wilbraham | Denisa Smajli | isabelle Heath-Apostolopoulos | Martijn A. Zwijnenburg
Source: ChemRxiv (preprint, 2019-07-19)
Field: Materials Science — Materials Chemistry

Mapping the Optoelectronic Property Space of Small Aromatic Molecules We perform a high-throughput virtual screening using the xTB family of density functional tight-binding methods to map the optoelectronic property space of ~250,000 aromatic molecules and quinone derivatives that find application in organic transistors, solar-cells, thermoelectrics, batteries and photocatalysts. The large volume of data generated allows for a broad understanding of how the presence of a wide range of heteroatoms and functional groups affect the ionisation potential, electron affinity and optical gap values of these molecular semiconductors and how the structural features – on their own or in combination with one another – allow access to particular regions of the optoelectronic property space. Finally, we identify the apparent boundaries of the optoelectronic property space for these molecules: regions of property space that appear off limits for any small conjugated cyclic organic molecule. Heteroatom ; Chemical space ; Conjugated system ; Molecule ; Materials science ; Space (punctuation) ; Organic semiconductor ; Semiconductor ; Property (philosophy) ; Electron affinity (data page) ; Density functional theory ; Organic solar cell ; Optoelectronics ; Nanotechnology ; Chemistry ; Computational chemistry ; Polymer ; Computer science ; Organic chemistry ; Drug discovery ; Alkyl Machine Learning in Materials Science ; Organic Electronics and Photovoltaics ; Molecular Junctions and Nanostructures
Authors: Valentina V. Sobornova | Konstantin V. Belov | Sergey V. Efimov | V.V. Klochkov | I.E. Eremeev | Ilya A. Khodov
Source:  (article, 2019-01-01)
Field: Materials Science — Materials Chemistry

Experimental Observation of Hidden Conformations of Strychnine by NMR Spectroscopy Magnetic resonance is a very dynamic phenomenon. Even when the spin system is at equilibrium, there is constant motion and relaxation. All too quickly anything we may do to the spin system is lost and forgotten. This limits what we are able to do with spins, whether it is measuring nanoscale distances with DEER/PELDOR spectroscopy, transferring dynamic nuclear polarization from near an electron spin to bulk nuclei, or making complex calculations with spin qubits. One major limitation is imposed in solids by the interaction of an unpaired electron spin with its nearby nuclear spin and the mutual interactions of the nuclear spins. Spins ; Pulsed EPR ; Relaxation (psychology) ; Chemistry ; Spin diffusion ; Unpaired electron ; Spin echo ; Spin polarization ; Nuclear magnetic resonance ; Spectroscopy ; Hyperpolarization (physics) ; Spin (aerodynamics) ; Condensed matter physics ; Nuclear magnetic resonance spectroscopy ; Electron ; Molecular physics ; Chemical physics ; Atomic physics ; Electron paramagnetic resonance ; Physics ; Quantum mechanics Solid-state spectroscopy and crystallography ; Advanced NMR Techniques and Applications ; Electron Spin Resonance Studies
Authors: Cormac Toher | Corey Oses | Stefano Curtarolo
Source:  (preprint, 2019-08-16)
Field: Materials Science — Materials Chemistry

Automated Computation of Materials Properties Materials informatics offers a promising pathway toward rational materials design, replacing the current trial-and-error approach and accelerating the development of new functional materials. Through the use of sophisticated data analysis techniques, underlying property trends can be identified, facilitating the formulation of new design rules. Such methods require large sets of consistently generated, programmatically accessible materials data. Computational materials design frameworks using standardized parameter sets are the ideal tools for producing such data. This work reviews the state-of-the-art in computational materials design, with a focus on these automated ab initio frameworks. Features such as structural prototyping and automated error correction that enable rapid generation of large data sets are discussed, and the way in which integrated workflows can simplify the calculation of complex properties, such as thermal conductivity and mechanical stability, is demonstrated. The organization of large data sets composed of ab initio calculations, and the tools that render them programmatically accessible for use in statistical learning applications are also described. Finally, recent advances in leveraging existing data to predict novel functional materials, such as entropy-stabilized ceramics, bulk metallic glasses, thermoelectrics, superalloys, and magnets, are surveyed. Computer science ; Workflow ; Materials informatics ; Thermoelectric materials ; Computational science ; Theoretical computer science ; Materials science ; Thermal conductivity ; Health informatics Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Catalysis and Oxidation Reactions
Authors: Martin S. Barbosa
Source: LA Referencia (Red Federada de Repositorios Institucionales de Publicaciones Científicas) (article, 2019-04-15)
Field: Materials Science — Polymers and Plastics

Estudo de desempenho e mecanismos de operação em transistors de interface eletrolítica baseados em óxido de tungstênio Submitted by Martin Schwellberger Barbosa (martin.s.barbosa@unesp.br) on 2019-04-25T19:02:30Z&#13;\nNo. of bitstreams: 1&#13;\ntese_martin_barbosa_definitiva.pdf: 5142601 bytes, checksum: fb47da0649ecb89f423fa8e1872afbe4 (MD5) Computer science Transition Metal Oxide Nanomaterials ; Semiconductor materials and devices ; Analytical Chemistry and Sensors
Authors: Gamze Doğdu Okçu | Emre Dikmen | Arda Yalçuk
Source: DergiPark (Istanbul University) (article, 2019-06-18)
Field: Materials Science — Materials Chemistry

The Sonocatalytic Degradation of 2,4-Dichlorophenoxyacetic acid (2,4-D) Herbicide Using Taguchi Experimental Design 2,4-Dichlorophenoxyacetic acid ; Taguchi methods ; Degradation (telecommunications) ; Chemistry ; Glyphosate ; Materials science ; Agronomy ; Engineering ; Botany ; Biology ; Composite material ; Electronic engineering Ultrasound and Cavitation Phenomena ; bioluminescence and chemiluminescence research