jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Lina Zhang | Liang‐Yu Chen | Cuihua Zhao | Yujing Liu | Lai‐Chang Zhang
Source: Metals (article, 2019-08-02)
Field: Materials Science — Materials Chemistry

Calculation of Oxygen Diffusion Coefficients in Oxide Films Formed on Low-Temperature Annealed Zr Alloys and Their Related Corrosion Behavior The growth of oxide film, which results from the inward oxygen diffusion from a corrosive environment, is a critical consideration for the corrosion resistance of zirconium alloys. This work calculates the oxygen diffusion coefficients in the oxide films formed on zirconium alloys annealed at 400~500 °C and investigates the related corrosion behavior. The annealed samples have a close size for the second-phase particles but a distinctive hardness, indicating the difference in substrate conditions. The weight gain of all samples highly follows parabolic laws. The weight gain of the sample annealed at 400 °C has the fastest increase rate at the very beginning of the corrosion test, but its oxide film has the slowest growth rate as the corrosion proceeds. By contrast, the sample annealed at 500 °C shows the lowest weight gain but the highest corrosion rate constant. Such a corrosion behavior is attributed to the amount of defects existing in the oxide film formed on the annealed samples; fewer defects would provide a lower fraction of short-circuit diffusion in total diffusion, resulting in a lower diffusion coefficient of oxygen in the oxide film, thereby producing better corrosion resistance. This is consistent with the calculated diffusion coefficients of oxygen in the oxide films: 3.252 × 10−11 cm2/s, 3.464 × 10−11 cm2/s and 3.740 × 10−11 cm2/s for the samples annealed at 400 °C, 450 °C, and 500 °C, respectively. Corrosion ; Materials science ; Oxide ; Diffusion ; Oxygen ; Annealing (glass) ; Metallurgy ; Analytical Chemistry (journal) ; Chemistry ; Thermodynamics Nuclear Materials and Properties ; Fusion materials and technologies ; High-Temperature Coating Behaviors
Authors: Sandeep Chandrashekharappa | Katharigatta N. Venugopala | Rashmi Venugopala | Basavaraj Padmashali
Source: Journal of Applied Pharmaceutical Science (article, 2019-02-01)
Field: Chemistry — Organic Chemistry

Qualitative anti-tubercular activity of synthetic ethyl 7-acetyl2-substituted-3-(4-substituted benzoyl) indolizine-1-carboxylate analogues Both the emergence of multidrug-resistant and extensively drug-resistant tuberculosis (TB) are currently the major challenges in the treatment of TB. Only delamanid and bedaquiline have been recently approved as anti-TB drugs in the past 40 years. In an attempt to search for active anti-TB compounds against the sensitive strain of Mycobacterium tuberculosis, H37Rv-a series of synthetic ethyl 7-acetyl-2-substituted-3-(4-substituted benzoyl)indolizine-1carboxylates (2a-r)-have been screened for in vitro qualitative anti-TB activity using an agar dilution method. It was found that compounds 2a, 2b, 2c, 2f, 2g, 2i, 2j, 2l, 2o, 2p, and 2r, which have various functional groups on the indolizine nucleus, were active against the H37Rv strain. Indolizine ; Chemistry ; Carboxylate ; Stereochemistry ; Combinatorial chemistry ; Organic chemistry Synthesis and Reactivity of Heterocycles ; Synthesis and pharmacology of benzodiazepine derivatives ; Synthesis and bioactivity of alkaloids
Authors: Daria Drozdenko | Jan Čapek | B. Clausen | Alexei Vinogradov | Kristián Máthis
Source: Journal of Alloys and Compounds (article, 2019-01-31)
Field: Materials Science — Biomaterials

Influence of the solute concentration on the anelasticity in Mg-Al alloys: A multiple-approach study Crystal twinning ; Materials science ; Slip (aerodynamics) ; Deformation (meteorology) ; Neutron diffraction ; In situ ; Dislocation ; Acoustic emission ; Deformation mechanism ; Diffraction ; Metallurgy ; Composite material ; Microstructure ; Thermodynamics ; Optics ; Chemistry ; Physics Magnesium Alloys: Properties and Applications ; Aluminum Alloys Composites Properties ; Metal and Thin Film Mechanics
Authors: Philipp Müller | Benjamin J. Bucior | Giulia Tuci | Lapo Luconi | Jürgen Getzschmann | Stefan Kaskel | Randall Q. Snurr | Giuliano Giambastiani | Andrea Rossin
Source: Molecular Systems Design & Engineering (article, 2019-01-01)
Field: Chemistry — Inorganic Chemistry

Computational screening, synthesis and testing of metal–organic frameworks with a bithiazole linker for carbon dioxide capture and its green conversion into cyclic carbonates A combined theoretical–experimental approach has been exploited for the design of a zirconium bithiazole-based MOF for CO<sub>2</sub> adsorption and its reaction with epoxides under green conditions. Metal-organic framework ; Adsorption ; Zirconium ; Carbon dioxide ; Linker ; Materials science ; Zirconium dioxide ; Metal ; Chemical engineering ; Chemistry ; Organic chemistry ; Computer science ; Metallurgy ; Engineering Metal-Organic Frameworks: Synthesis and Applications ; Carbon dioxide utilization in catalysis ; Covalent Organic Framework Applications
Authors: Derong Lu | Jiajing Zhou | Shuai Hou | Qirong Xiong | Yonghao Chen | Kanyi Pu | Jinghua Ren | Hongwei Duan
Source: Advanced Materials (review, 2019-08-29)
Field: Chemistry — Organic Chemistry

Functional Macromolecule‐Enabled Colloidal Synthesis: From Nanoparticle Engineering to Multifunctionality The synthesis of well-defined inorganic colloidal nanostructures using functional macromolecules is an enabling technology that offers the possibility of fine-tuning the physicochemical properties of nanomaterials and has contributed to a broad range of practical applications. The utilization of functional reactive polymers and their colloidal assemblies leads to a high level of control over structural parameters of inorganic nanoparticles that are not easily accessible by conventional methods based on small-molecule ligands. Recent advances in polymerization techniques for synthetic polymers and newly exploited functions of natural biomacromolecules have opened up new avenues to monodisperse and multifunctional nanostructures consisting of integrated components with distinct chemistries but complementary properties. Here, the evolution of colloidal synthesis of inorganic nanoparticles is revisited. Then, the new developments of colloidal synthesis enabled by functional macromolecules and practical applications associated with the resulting optical, catalytic, and structural properties of colloidal nanostructures are summarized. Finally, a perspective on new and promising pathways to novel colloidal nanostructures built upon the continuous development of polymer chemistry, colloidal science, and nanochemistry is provided. Nanotechnology ; Materials science ; Macromolecule ; Nanochemistry ; Nanomaterials ; Dispersity ; Nanoparticle ; Polymer ; Nanoreactor ; Colloid ; Nanostructure ; Polymerization ; Functional polymers ; Chemistry ; Organic chemistry ; Polymer chemistry Advanced Polymer Synthesis and Characterization ; Polymer Surface Interaction Studies ; Gold and Silver Nanoparticles Synthesis and Applications
Authors: Masahito Murai | Takuya Ogita | Kazuhiko Takai
Source: Chemical Communications (article, 2019-01-01)
Field: Chemistry — Organic Chemistry

Regioselective arene homologation through rhenium-catalyzed deoxygenative aromatization of 7-oxabicyclo[2.2.1]hepta-2,5-dienes Combined use of oxorhenium catalysts with triphenyl phosphite as an oxygen acceptor allowed efficient deoxygenative aromatization of oxabicyclic dienes. The reaction proceeded under neutral conditions and was compatible with various functional groups. Combining this deoxygenation with regioselective bromination and trapping of the generated aryne with furan resulted in benzannulative π-extension at the periphery of the PAHs. This enabled direct use of unfunctionalized PAHs for extension of π-conjugation. Iteration of the transformations increased the number of fused-benzene rings one at a time, which has the potential to alter the properties of PAHs by fine-tuning the degree of π-conjugation, shape, and edge topology. Aromatization ; Deoxygenation ; Chemistry ; Regioselectivity ; Catalysis ; Halogenation ; Furan ; Triphenyl phosphite ; Aryne ; Rhenium ; Organic chemistry ; Combinatorial chemistry Cyclization and Aryne Chemistry ; Synthesis and Properties of Aromatic Compounds ; Catalytic Alkyne Reactions
Authors: Büşra Uzun | Ömer Cívalek
Source: Mathematical and Computational Applications (article, 2019-04-06)
Field: Materials Science — Materials Chemistry

Nonlocal FEM Formulation for Vibration Analysis of Nanowires on Elastic Matrix with Different Materials In this study, free vibration behaviors of various embedded nanowires made of different materials are investigated by using Eringen’s nonlocal elasticity theory. Silicon carbide nanowire (SiCNW), silver nanowire (AgNW), and gold nanowire (AuNW) are modeled as Euler–Bernoulli nanobeams with various boundary conditions such as simply supported (S-S), clamped simply supported (C-S), clamped–clamped (C-C), and clamped-free (C-F). The interactions between nanowires and medium are simulated by the Winkler elastic foundation model. The Galerkin weighted residual method is applied to the governing equations to gain stiffness and mass matrices. The results are given by tables and graphs. The effects of small-scale parameters, boundary conditions, and foundation parameters on frequencies are examined in detail. In addition, the influence of temperature change on the vibrational responses of the nanowires are also pursued as a case study. Nanowire ; Materials science ; Vibration ; Boundary value problem ; Stiffness ; Elasticity (physics) ; Galerkin method ; Finite element method ; Euler's formula ; Composite material ; Mathematical analysis ; Physics ; Mathematics ; Nanotechnology ; Thermodynamics ; Acoustics Nonlocal and gradient elasticity in micro/nano structures ; Numerical methods in engineering ; Composite Structure Analysis and Optimization
Authors: S. Arumugam | Manikandan Krishnan | Kento Ishigaki | Jun Gouchi | Rukshana Pervin | G. Selvan | Parasharam M. Shirage | Yoshiya Uwatoko
Source: Scientific Reports (article, 2019-01-23)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Enhancement of superconducting properties and flux pinning mechanism on Cr0.0005NbSe2 single crystal under Hydrostatic pressure Superconducting properties of Cr<sub>0.0005</sub>NbSe<sub>2</sub> (T<sub>c</sub>~6.64 K) single crystals have been investigated through the temperature dependent resistivity (~8 GPa) and DC magnetization (~1 GPa) measurements. Further, the critical current density (J<sub>c</sub>) as a function of applied magnetic field has been studied from magnetic isotherms. The vortex pinning mechanisms have also been systematically analyzed using weak collective pinning theory as a function of pressure. The J<sub>c</sub> corresponds to the flux flow enhanced by the application of pressure due to increase of T<sub>c</sub> and vortex changes. We found that the pressure is responsible for the spatial variations in the charge carrier mean free path (δl pinning). We find that core point pinning is more dominant than surface pinning which is caused by the application of pressure. In addition, J<sub>c</sub>(H = 0) increases from 3.9 × 10<sup>5</sup> (0 GPa) to 1.3 × 10<sup>6</sup> (1.02 GPa) A/cm<sup>2</sup> at 2 K as the pressure is increased from normal pressure to 1.02 GPa. The pressure dependence of T<sub>c</sub> (dT<sub>c</sub>/dP) becomes 0.91 K/GPa and 0.75 K/GPa from magnetization and resistivity measurements respectively. We found that the pressure promotes the anisotropy nature, and decrease of coherence length and resulting in pathetic interface of the vortex core with pinning centers. Condensed matter physics ; Flux pinning ; Superconductivity ; Hydrostatic pressure ; Pinning force ; Coherence length ; Electrical resistivity and conductivity ; Materials science ; Vortex ; Magnetization ; Anisotropy ; Magnetic field ; High-temperature superconductivity ; Critical current ; Physics ; Thermodynamics Iron-based superconductors research ; Physics of Superconductivity and Magnetism ; Magnetic and transport properties of perovskites and related materials
Authors: Kareem Abdelfatah | Wenqiang Yang | Rajadurai Vijay Solomon | Biplab Rajbanshi | Asif Chowdhury | Mehdi Zare | Subrata Kumar Kundu | Adam Yonge | Andreas Heyden | Gabriel Terejanu
Source: The Journal of Physical Chemistry C (article, 2019-11-14)
Field: Materials Science — Materials Chemistry

Prediction of Transition-State Energies of Hydrodeoxygenation Reactions on Transition-Metal Surfaces Based on Machine Learning Computational catalyst discovery involves identification of a meaningful model and suitable descriptors that determine the catalyst properties. We study the impact of combining various descriptors (e.g., reaction energies, metal descriptors, and bond counts) for modeling transition-state energies (TS) based on a database of adsorption and TS energies across transition-metal surfaces for the decarboxylation and decarbonylation of propionic acid, a chemistry characteristic for biomass conversion. Results of different machine learning models for more than 1572 descriptor combinations suggest that there is no statistically significant difference between linear and nonlinear models when using the right combination of reactant energies, metal descriptors, and bond counts. However, linear models are inferior when not including bond count and metal descriptors. Furthermore, when there are missing data for reaction steps on all metals, conventional linear scaling is inferior to linear and nonlinear models with proper choice of descriptors that are surprisingly robust. Decarbonylation ; Hydrodeoxygenation ; Transition metal ; Chemistry ; Decarboxylation ; Catalysis ; Nonlinear system ; Metal ; Computational chemistry ; Thermodynamics ; Organic chemistry ; Physics ; Quantum mechanics Machine Learning in Materials Science ; Catalysis and Hydrodesulfurization Studies ; Electrocatalysts for Energy Conversion
Authors: Xiaolong Zheng | Peng Zheng | Liang Zheng | Yang Zhang | Ruizhi Zhang
Source: Computational Materials Science (article, 2019-12-09)
Field: Materials Science — Materials Chemistry

Multi-channel convolutional neural networks for materials properties prediction Convolutional neural network ; Convolution (computer science) ; Computer science ; Preprocessor ; Channel (broadcasting) ; Algorithm ; Artificial neural network ; Mean squared prediction error ; Enthalpy ; Artificial intelligence ; Pattern recognition (psychology) ; Atom (system on chip) ; Physics ; Thermodynamics Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Computational Drug Discovery Methods
Authors: Thejani M. Gunaratne | Claudia Gonzalez Viejo | Nadeesha M. Gunaratne | Damir D. Torrico | Frank R. Dunshea | Sigfredo Fuentes
Source: Foods (article, 2019-09-20)
Field: Chemistry — Analytical Chemistry

Chocolate Quality Assessment Based on Chemical Fingerprinting Using Near Infra-red and Machine Learning Modeling Chocolates are the most common confectionery and most popular dessert and snack across the globe. The quality of chocolate plays a major role in sensory evaluation. In this study, a rapid and non-destructive method was developed to predict the quality of chocolate based on physicochemical data, and sensory properties, using the five basic tastes. Data for physicochemical analysis (pH, Brix, viscosity, and color), and sensory properties (basic taste intensities) of chocolate were recorded. These data and results obtained from near-infrared spectroscopy were used to develop two machine learning models to predict the physicochemical parameters (Model 1) and sensory descriptors (Model 2) of chocolate. The results show that the models developed had high accuracy, with <i>R</i> = 0.99 for Model 1 and <i>R</i> = 0.93 for Model 2. The thus-developed models can be used as an alternative to consumer panels to determine the sensory properties of chocolate more accurately with lower cost using the chemical parameters. Sensory system ; Taste ; Sensory analysis ; Food science ; Near-infrared spectroscopy ; Quality (philosophy) ; Artificial intelligence ; Pattern recognition (psychology) ; Chemometrics ; Computer science ; Chemistry ; Mathematics ; Biological system ; Machine learning ; Physics ; Psychology ; Biology ; Cognitive psychology Spectroscopy and Chemometric Analyses ; Meat and Animal Product Quality ; Advanced Chemical Sensor Technologies
Authors: Maxim A. Makeev | Nav Nidhi Rajput
Source: Current Opinion in Chemical Engineering (article, 2019-03-01)
Field: Materials Science — Materials Chemistry

Computational screening of electrolyte materials: status quo and open problems Status quo ; Computer science ; Context (archaeology) ; Battery (electricity) ; Electrolyte ; Computational model ; Nanotechnology ; Construct (python library) ; Systems engineering ; Simulation ; Materials science ; Engineering ; Chemistry ; Physics Machine Learning in Materials Science ; Fuel Cells and Related Materials ; Advanced Battery Materials and Technologies
Authors: Levi C. Lentz | Alexie M. Kolpak
Source: Journal of Physics Condensed Matter (article, 2019-12-05)
Field: Materials Science — Materials Chemistry

Predicting HSE band gaps from PBE charge densities via neural network functionals Density functional theory (DFT) has become a standard method for ab initio calculations of material properties. However, it has a number of shortcomings, particularly in predicting key properties, such as band gap and optical spectra, which are dependent on excited states. To treat such properties, more accurate approaches such as GW or DFT with hybrid functionals (including HSE, PBE0, and B3LYP, to name a few) can be employed; however, these approaches are unfeasible for many large and/or complex systems due to their high computational cost and large memory requirements. In this work, we investigate the ability to train neural networks of the traditional DFT charge density computed with a standard PBE functional to accurately predict HSE band gaps. We show that a single network PBE charge density functional can predict the HSE band gap of seven different materials-silicon, gallium arsenide, molybdenum disulfide, germanium, tin phosphate, titanium phosphate, and zirconium phosphate-under a wide variety of conditions with an RSME of 172.6 meV, which is 34% better accuracy than standard regression between the PBE and HSE band gaps. This approach, which, in principle, can be used to map PBE charge densities to band gaps or other properties computed with any higher accuracy method, has the potential to decrease computational costs, increase prediction accuracy, and enable accurate high-throughput screening for a wide variety of complex materials systems. Density functional theory ; Hybrid functional ; Band gap ; Materials science ; Artificial neural network ; Excited state ; Ab initio ; Charge (physics) ; Computational chemistry ; Molecular physics ; Computational physics ; Computer science ; Chemistry ; Atomic physics ; Physics ; Machine learning ; Optoelectronics ; Quantum mechanics Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides ; 2D Materials and Applications
Authors: Alexander Kovacs | Johann Fischbacher | Markus Gusenbauer | Harald Oezelt | Heike C. Herper | Olga Yu. Vekilova | P. Nieves | S. Arapan | T. Schrefl
Source: Engineering (article, 2019-11-20)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Computational Design of Rare-Earth Reduced Permanent Magnets Multiscale simulation is a key research tool in the quest for new permanent magnets. Starting with first principles methods, a sequence of simulation methods can be applied to calculate the maximum possible coercive field and expected energy density product of a magnet made from a novel magnetic material composition. Iron (Fe)-rich magnetic phases suitable for permanent magnets can be found by means of adaptive genetic algorithms. The intrinsic properties computed by ab initio simulations are used as input for micromagnetic simulations of the hysteresis properties of permanent magnets with a realistic structure. Using machine learning techniques, the magnet’s structure can be optimized so that the upper limits for coercivity and energy density product for a given phase can be estimated. Structure property relations of synthetic permanent magnets were computed for several candidate hard magnetic phases. The following pairs (coercive field (T), energy density product (kJ·m −3 )) were obtained for iron-tin-antimony (Fe 3 Sn 0.75 Sb 0.25 ): (0.49, 290), L1 0 -ordered iron-nickel (L1 0 FeNi): (1, 400), cobalt-iron-tantalum (CoFe 6 Ta): (0.87, 425), and manganese-aluminum (MnAl): (0.53, 80). Magnet ; Rare earth ; Geology ; Computer science ; Mechanical engineering ; Engineering ; Earth science Magnetic Properties of Alloys ; Superconducting Materials and Applications ; Electric Motor Design and Analysis
Authors: Sebastian Dick | Mariví Fernández-Serra
Source: ChemRxiv (preprint, 2019-11-19)
Field: Materials Science — Materials Chemistry

Machine Learning Accurate Exchange and Correlation Functionals of the Electronic Density Density Functional Theory (DFT) is the standard formalism to study the electronic structure of matterat the atomic scale. The balance between accuracy and computational cost thatDFT-based simulations provide allows researchers to understand the structural and dynamical properties of increasingly large and complex systems at the quantum mechanical level.In Kohn-Sham DFT, this balance depends on the choice of exchange and correlation functional, which only existsin approximate form. Increasing the non-locality of this functional and climbing the figurative Jacob's ladder of DFT, one can systematically reduce the amount of approximation involved and thus approach the exact functional. Doing this, however, comes at the price of increased computational cost, and so, for extensive systems, the predominant methods of choice can still be found within the lower-rung approximations. Here we propose a framework to create highly accurate density functionals by using supervised machine learning, termed NeuralXC. These machine-learned functionals are designed to lift the accuracy of local and semilocal functionals to that provided by more accurate methods while maintaining their efficiency. We show that the functionals learn a meaningful representation of the physical information contained in the training data, making them transferable across systems. We further demonstrate how a functional optimized on water can reproduce experimental results when used in molecular dynamics simulations. Finally, we discuss the effects that our method has on self-consistent electron densities by comparing these densities to benchmark coupled-cluster results. Density functional theory ; Orbital-free density functional theory ; Computer science ; Statistical physics ; Hybrid functional ; Benchmark (surveying) ; Lift (data mining) ; Locality ; Representation (politics) ; Artificial intelligence ; Machine learning ; Physics ; Quantum mechanics Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Advanced Chemical Physics Studies