jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Chiho Kim
Source: ACS Central Science (article, 2018-09-12)
Field: Materials Science — Materials Chemistry

Accelerating Photofunctional Molecule Discovery with Artificial Intelligence ADVERTISEMENT RETURN TO ISSUEPREVFirst ReactionsNEXTAccelerating Photofunctional Molecule Discovery with Artificial IntelligenceAs design of new materials via artificial intelligence becomes more common, a paradigm shift is taking place in materials research.Chiho Kim​Chiho KimSchool of Materials Science and Engineering, Georgia Institute of Technology, 771 Ferst Drive NW, Atlanta, Georgia 30332, United StatesE-mail: [email protected]More by Chiho Kimhttp://orcid.org/0000-0002-1814-4980Cite this: ACS Cent. Sci. 2018, 4, 9, 1089–1091Publication Date (Web):September 12, 2018Publication History Published online12 September 2018Published inissue 26 September 2018https://doi.org/10.1021/acscentsci.8b00550Copyright © 2018 American Chemical SocietyRIGHTS & PERMISSIONSACS AuthorChoiceArticle Views2137Altmetric-Citations1LEARN ABOUT THESE METRICSArticle Views are the COUNTER-compliant sum of full text article downloads since November 2008 (both PDF and HTML) across all institutions and individuals. These metrics are regularly updated to reflect usage leading up to the last few days.Citations are the number of other articles citing this article, calculated by Crossref and updated daily. Find more information about Crossref citation counts.The Altmetric Attention Score is a quantitative measure of the attention that a research article has received online. Clicking on the donut icon will load a page at altmetric.com with additional details about the score and the social media presence for the given article. Find more information on the Altmetric Attention Score and how the score is calculated. Share Add toView InAdd Full Text with ReferenceAdd Description ExportRISCitationCitation and abstractCitation and referencesMore Options Share onFacebookTwitterWechatLinked InReddit PDF (558 KB) Get e-AlertsSUBJECTS:Density functional theory,Materials,Molecules,Organic compounds,Quantum mechanics Get e-Alerts Altmetrics ; Citation ; Social media ; Computer science ; Library science ; Artificial intelligence ; Information retrieval ; World Wide Web ; Data science Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides ; Computational Drug Discovery Methods
Authors: Akihiro Miyauchi
Source: Journal of Photopolymer Science and Technology (article, 2018-06-25)
Field: Materials Science — Materials Chemistry

Creation of Functional Surface by Informatics and Biomimetics Advances in rapid information technology make it possible to find scientific truths inductively in cyber-space, and this kind of research method is spreading worldwide as a fourth science. This is a method of deriving truth from enormous data and is a data driven type induction method. On the other hand, living things repeated enormous trial and error in the process of evolution, and species that acquired the optimum mechanism in the global environment at that time have been survived. Therefore, it can be interpreted that the functions of living matters are obtained by data-driven induction method, and biomimetics is nature-inspired inductive method. Biomimetics is positioned on the extension line of Materials Informatics which is currently being studied, and further combination with information technology is considered to be very important. Biomimetics ; Extension (predicate logic) ; Informatics ; Process (computing) ; Computer science ; Space (punctuation) ; Nanotechnology ; Data science ; Artificial intelligence ; Engineering ; Materials science Machine Learning in Materials Science
Authors: Vladan Končar
Source:  (article, 2018-10-30)
Field: Materials Science — Polymers and Plastics

SMART TEXTILES AND THEIR APPLICATIONS - VISUAL PERCEPTIONS Smart textiles, encompassing electronics combined with textiles also called textronics, have a very promising realm in science and technology nowadays because of commercial viability and public interests. Smart textiles play a significant role as well in the European textile sector and assist the textile industry in its transformation into a competitive knowledge driven industry. These kinds of textiles combine knowledge from many disciplines with the specific requirements of textile. Numerous materials and systems are available together with devices for sensing and actuation, but they are not compatible with a textile or with the textile production processes. They could be transformed into a textile compatible structure or even in a full textile structure. Smart textiles can be defined as textiles that are able to sense and respond to changes in their environment. They may be divided into two classes: passive and active smart textiles. Passive smart textiles have the ability to change their properties according to an environmental stimulation. Shape memory materials, hydrophobic or hydrophilic textiles etc. make part of this category. Active smart textiles are fitted with sensors and actuators, in order to connect internal parameters to the transmitted message. They are able to detect different signals from the environment (temperature, light intensity, pollution), to decide how to react and finally to act using various textile based, flexible or miniaturized actuators (textile displays, micro vibrating devices, LED, OLED). The "decision" can be taken locally in case of embedded electronic devices (textile electronics) to smart textile structures or remotely in case the smart textile is wirelessly connected to clouds containing data base, servers with artificial intelligence software etc. and may be a part of Internet of Things (IoT) concept. This presentation focuses on smart textiles actuators used as active and passive flexible displays. Textile ; Electronics ; Textile design ; Actuator ; Smart material ; Computer science ; Textile industry ; Architectural engineering ; Engineering ; Electrical engineering ; Business ; Materials science ; Nanotechnology ; Artificial intelligence Conducting polymers and applications ; Advanced Sensor and Energy Harvesting Materials ; Polydiacetylene-based materials and applications
Authors: Chunyan Diao | Xingmeng Guo | Jianfeng Li
Source: Aerosol and Air Quality Research (article, 2018-01-01)
Field: Materials Science — Materials Chemistry

Research on Urea Jet Pump Performance Characteristics using the Optimized NOx Removal Equipment in Diesel Engine There are many factors that affect the efficiency of NOx conversion in the diesel engine SCR (selective catalytic reduction) post-processing system. In this study, we researched the factors affecting the efficiency of NOx conversion by engine bench testing. The SCR system was applied to the Weichai WD615 engine exhaust. First, the system used DOC (Diesel Oxidation Catalyst)/DPF (Diesel Particulate Filter) of which DOC is a noble metal-containing filter, it lets the filter passes through converting nitrogen monoxide (NO) to nitrogen dioxide (NO2) for passive regenerations and to provide high hydrocarbon (HC) oxidation activity for active DPF regenerations, the DPF is a wall-flow filter used to trap the remaining soot that the DOC can not oxidize. The combined catalytic oxidation device improved the original SCR deNOx system, which we developed. The new catalytic oxidation apparatus has a simple structure and fundamentally solves the clogging problem of the SCR method and the defects led by the unstable gaseous ammonia generation. Secondly, we studied the influence of an increased urea injection on the conversion efficiency of NOx and the secondary pollution of NH3 at different speeds and torques of the engine. It was found that at the maximum removal rate, the downstream NOx concentration was only 1 ppm. The NOx conversion efficiency can reach 79.10% or more—even 99.90%. Finally, we used the KDS and the BOSCH urea injection pumps to analyze the effect of different pumps on the conversion efficiency of NOx. Hence, the matching diesel engine urea SCR post-processing system can reduce the emission concentration and pollution. There was little difference between the two pumps under most of the working conditions, although KDS was a little better than BOSCH under some individual conditions. NOx ; Diesel engine ; Diesel fuel ; Automotive engineering ; Environmental science ; Urea ; Jet (fluid) ; Jet engine ; Process engineering ; Waste management ; Chemistry ; Engineering ; Mechanical engineering ; Aerospace engineering ; Organic chemistry ; Combustion Catalytic Processes in Materials Science ; Industrial Gas Emission Control ; Vehicle emissions and performance
Authors: Zhifeng Huang | Fei Chen | Qiang Shen | Lianmeng Zhang | Timothy J. Rupert
Source: arXiv (Cornell University) (preprint, 2018-09-06)
Field: Materials Science — Materials Chemistry

Combined effects of nonmetallic impurities and planned metallic dopants on grain boundary energy and strength Most research on nanocrystalline alloys has been focused on planned doping of metals with other metallic elements, but nonmetallic impurities are also prevalent in the real world. In this work, we report on the combined effects of metallic dopants and nonmetallic impurities on grain boundary energy and strength using first-principles calculations, with a $Σ$5 (310) grain boundary in Cu chosen as a model system. We find a clear correlation between the grain boundary energy and the change in excess free volume of doped grain boundaries. A combination of a larger substitutional dopant and an interstitial impurity can fill the excess free volume more efficiently and further reduce the grain boundary energy. We also find that the strengthening effects of dopants and impurities are dominated by the electronic interactions between the host Cu atoms and the two types of dopant elements. For example, the significant competing effects of metal dopants such as Zr, Nb, and Mo with impurities on the grain boundary strength are uncovered from the density of states of the d electrons. As a whole, this work deepens the field's understanding of the interaction between metallic dopants and nonmetallic impurities on grain boundary properties, providing a guide for improving the thermal stability of materials while avoiding embrittling effects. Dopant ; Grain boundary ; Materials science ; Impurity ; Nanocrystalline material ; Doping ; Condensed matter physics ; Metallurgy ; Grain boundary diffusion coefficient ; Chemical physics ; Nanotechnology ; Chemistry ; Microstructure ; Physics ; Optoelectronics Microstructure and mechanical properties ; Metal and Thin Film Mechanics ; Diamond and Carbon-based Materials Research
Authors: Thee Chowwanonthapunya
Source: Maritime Technology and Research (article, 2018-12-21)
Field: Materials Science — Metals and Alloys

The monitoring of pitting corrosion in stainless steel in the simulated corrosive conditions of marine applications This paper describes the monitoring of pitting corrosion in ferritic and austenitic stainless steel under the simulated acidic conditions of marine applications. The monitoring dealt with corrosion weight loss and metallurgical investigation. The results indicated that stainless steel with passive film shows good corrosion resistance to the simulated corrosive environment for all test periods. In contrast, stainless steel without passive film cannot provide an inhibiting effect for the whole test period. After 24 h. of testing, both of the stainless steels were locally attacked. Pitting corrosion was observed on the surface of both stainless steels. In small pits, self-accelerating corrosion can occur. Therefore, longer immersion times were attributed to the higher corrosion rates of both stainless steel in simulated acidic conditions. Corrosion ; Metallurgy ; Materials science ; Pitting corrosion ; Austenitic stainless steel ; Austenite ; Immersion (mathematics) ; Intergranular corrosion ; Microstructure Hydrogen embrittlement and corrosion behaviors in metals ; Concrete Corrosion and Durability ; Corrosion Behavior and Inhibition
Authors: Christian Trachsel | Christian Panse | Tobias Kockmann | Witold Wolski | Jonas Grossmann | Ralph Schlapbach
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2018-04-24)
Field: Chemistry — Spectroscopy

<tt>rawDiag</tt> - an R package supporting rational LC-MS method optimization for bottom-up proteomics Abstract Optimizing methods for liquid chromatography coupled to mass spectrometry (LC-MS) is a non-trivial task. Here we present rawDiag , a software tool supporting rational method optimization by providing MS operator-tailored diagnostic plots of scan level metadata. rawDiag is implemented as R package and can be executed on the command line, or through a graphical user interface (GUI) for less experienced users. The code runs platform independent and can process a hundred raw files in less than three minutes on current consumer hardware as we show by our benchmark. In order to demonstrate the functionality of our package, we included a real-world example taken from our daily core facility business. Computer science ; Benchmark (surveying) ; Task (project management) ; Graphical user interface ; Software ; Metadata ; R package ; Interface (matter) ; Process (computing) ; Code (set theory) ; Embedded system ; Operating system ; Programming language ; Set (abstract data type) ; Engineering Advanced Proteomics Techniques and Applications ; Mass Spectrometry Techniques and Applications ; Metabolomics and Mass Spectrometry Studies
Authors: Kirsten T. Winther | Max J. Hoffmann | Osman Mamun | Jacob R. Boes | Jens K. Nørskov | Michal Bajdich | Thomas Bligaard
Source: ChemRxiv (preprint, 2018-11-19)
Field: Materials Science — Materials Chemistry

Catalysis-hub.org: An Open Electronic Structure Database for Surface Reactions We present a new open repository for chemical reactions on catalytic surfaces, available at https://www.catalysis-hub.org. The featured Surface Reactions database contains more than 100,000 chemical adsorption and reaction energies obtained from electronic structure calculations, and is continuously being updated with new datasets. In addition to providing quantum-mechanical results for a broad range of reactions and surfaces from different publications, the database features a systematic, large-scale study of chemical adsorption and hydrogenation on bimetallic alloy surfaces. The database contains reaction specific information, such as the surface composition and reaction energy for each reaction, as well as the atomic surface geometries used in the calculations together with the calculation parameters and output, which are essential for data reproducibility. Data can be accessed from the web-interface as well as from a Python API providing direct access from a local workstation. This enables researchers to efficiently use the data as a basis for further calculations and to generate surrogate models for accelerating the discovery of catalytic materials for sustainable energy applications. Catalysis ; Bimetallic strip ; Database ; Python (programming language) ; Adsorption ; Computer science ; Workstation ; Materials science ; Chemistry ; Nanotechnology ; Computational science ; Physical chemistry ; Organic chemistry Machine Learning in Materials Science ; Catalytic Processes in Materials Science ; Electrocatalysts for Energy Conversion
Authors: D. Crevillén-García | Puiki Leung | A.A. Shah
Source: Journal of Physics Conference Series (article, 2018-07-01)
Field: Materials Science — Surfaces, Coatings and Films

An emulator for kinetic Monte Carlo simulations of kinetically controlled metal electrodeposition In recent years there has been an increasing interest in developing models for atomistic- scale simulations of electrochemical metal deposition processes. One the most interesting and challenging features of such models is their capability to reproduce the physical evolution of the final deposition shape in a practical time scale. To date, most of the available models are limited to the simulation of a few nanoseconds of the physical phenomenon, for instance molecular dynamics, and there are very few methods, including kinetic Monte Carlo, that can reach to reproduce some seconds due to the requirement of an enormous computational cost. In this paper we present a surrogate-assisted kinetic Monte Carlo method based on Gaussian process emulation as a tool for predicting the final electrodeposition shape in a kinetically controlled copper electrodeposition on a gold substrate. The main advantage of this method is its ability to dramatically reduce the computational cost of the kinetic Monte Carlo simulation while yielding accurate results. Kinetic Monte Carlo ; Monte Carlo method ; Emulation ; Statistical physics ; Dynamic Monte Carlo method ; Kinetic energy ; Molecular dynamics ; Deposition (geology) ; Monte Carlo molecular modeling ; Computer science ; Materials science ; Chemistry ; Physics ; Computational chemistry ; Markov chain Monte Carlo ; Mathematics Electron and X-Ray Spectroscopy Techniques ; Advancements in Photolithography Techniques ; Machine Learning in Materials Science
Authors: Lider S. Leon | Ralph C. Smith | William S. Oates | Paul Miles
Source:  (article, 2018-03-22)
Field: Materials Science — Materials Chemistry

Active subspace uncertainty quantification for a polydomain ferroelectric phase-field model Quantum-informed ferroelectric phase field models capable of predicting material behavior, are necessary for facilitating the development and production of many adaptive structures and intelligent systems. Uncertainty is present in these models, given the quantum scale at which calculations take place. A necessary analysis is to determine how the uncertainty in the response can be attributed to the uncertainty in the model inputs or parameters. A second analysis is to identify active subspaces within the original parameter space, which quantify directions in which the model response varies most dominantly, thus reducing sampling effort and computational cost. In this investigation, we identify an active subspace for a poly-domain ferroelectric phase-field model. Using the active variables as our independent variables, we then construct a surrogate model and perform Bayesian inference. Once we quantify the uncertainties in the active variables, we obtain uncertainties for the original parameters via an inverse mapping. The analysis provides insight into how active subspace methodologies can be used to reduce computational power needed to perform Bayesian inference on model parameters informed by experimental or simulated data. Subspace topology ; Computer science ; Bayesian inference ; Uncertainty quantification ; Inference ; Field (mathematics) ; Bayesian probability ; Surrogate model ; Machine learning ; Artificial intelligence ; Mathematics Solidification and crystal growth phenomena ; Model Reduction and Neural Networks ; Probabilistic and Robust Engineering Design
Authors: David J. Hall | Charles G. Martin | Michael Welford | Stefan L. Debbert
Source: Biochemistry and Molecular Biology Education (article, 2018-09-01)
Field: Chemistry — Physical and Theoretical Chemistry

An inquiry‐based exercise in medicinal chemistry: Synthesis of a molecular library and screening for potential antimalarial and anti‐inflammatory compounds The development of new medicines holds particular fascination for chemistry, biochemistry, and biology students interested in a career in medicine or the life sciences. The identification and refinement of lead compounds to treat diseases requires researchers to be facile in a number of different disciplines including organic synthesis, biochemistry, cell biology, and molecular biology. We have developed an interdisciplinary, inquiry-based laboratory spanning both organic chemistry and biochemistry classes that acquaints students with research in medicinal chemistry. The first part of the exercise takes place in the second semester of organic chemistry, where pairs of students design and execute their own multistep synthesis of a novel compound with anti-inflammatory and/or antimalarial potential. Later, in first semester biochemistry, many of the same students then test these synthesized compounds for cytotoxicity, inhibition of the enzyme nitric oxide synthase, and inhibition of the transcription factor NF-kB. Learning outcomes, measured by the Classroom Undergraduate Research Experience (CURE) survey, suggest that students participating in both classes had higher gains than an average student. © 2018 International Union of Biochemistry and Molecular Biology, 46(5):424-434, 2018. Chemistry ; Biochemistry ; Chemical biology ; Pharmacology ; Biology Various Chemistry Research Topics ; ATP Synthase and ATPases Research ; Bioactive Compounds and Antitumor Agents
Authors: Francis Nascimento | Baraquízio Braga do Nascimento | Luiz E.M. Cardoso | Rodrigo Albuquerquer | N. M. Oliveira-Neto
Source: Química Nova (article, 2018-01-01)
Field: Chemistry — Physical and Theoretical Chemistry

AN APPROACH TO THE KINETICS AND THERMODYNAMICS OF ELEMENTARY CHEMICAL REACTIONS USING A STOCHASTIC MODEL A mathematical model is proposed to investigate the kinetics and equilibrium of homogeneous elementary first-and second-order chemical reactions. The dynamics is defined by the Monte Carlo method (MCM), with the Metropolis update, and the Ehrenfest urn model (EUM). MCM is an important step that accesses the kinetic and thermodynamic properties of the system, while the EUM defines the orders of the reactions studied in this work. The main parameters, such as temperature, the activation energy and the steric factor, were taken into account in the calculation of the transition probabilities between the reactants and products. It is thus possible to reproduce the kinetic profiles of the reactions and to evaluate the influence of temperature and the steric factor. Furthermore, it is possible to simulate the behaviour of the system by modifying the activation energy barrier, thus simulating a catalytic process. The effect of the addition of molecules was also investigated, with the system returning to a thermodynamic equilibrium condition after partial consumption of the added reactant, as predicted by Le Chatelier's principle. All the simulated data are in agreement with the theoretical results present in physical chemistry textbooks. Thermodynamics ; Kinetics ; Chemical thermodynamics ; Chemical kinetics ; Chemistry ; Statistical physics ; Non-equilibrium thermodynamics ; Physics ; Classical mechanics History and advancements in chemistry ; Various Chemistry Research Topics ; Advanced Physical and Chemical Molecular Interactions