jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Nicole Balasco | Giovanni Smaldone | Marilisa Vigorita | Pompea Del Vecchio | Giuseppe Graziano | Alessia Ruggiero | Luigi Vitagliano
Source: Scientific Reports (article, 2019-04-29)
Field: Materials Science — Materials Chemistry

The characterization of Thermotoga maritima Arginine Binding Protein variants demonstrates that minimal local strains have an important impact on protein stability The Ramachandran plot is a versatile and valuable tool that provides fundamental information for protein structure determination, prediction, and validation. The structural/thermodynamic effects produced by forcing a residue to adopt a conformation predicted to be forbidden were here explored using Thermotoga maritima Arginine Binding Protein (TmArgBP) as model. Specifically, we mutated TmArgBP Gly52 that assumes a conformation believed to be strictly disallowed for non-Gly residues. Surprisingly, the crystallographic characterization of Gly52Ala TmArgBP indicates that the structural context forces the residue to adopt a non-canonical conformation never observed in any of the high-medium resolution PDB structures. Interestingly, the inspection of this high resolution structure demonstrates that only minor alterations occur. Nevertheless, experiments indicate that Gly52 replacements in TmArgBP produce destabilizations comparable to those observed upon protein truncation or dissection in domains. Notably, we show that force-fields commonly used in computational biology do not reproduce this non-canonical state. Using TmArgBP as model system we here demonstrate that the structural context may force residues to adopt conformations believed to be strictly forbidden and that barely detectable alterations produce major destabilizations. Present findings highlight the role of subtle strains in governing protein stability. A full understanding of these phenomena is essential for an exhaustive comprehension of the factors regulating protein structures. Thermotoga maritima ; Ramachandran plot ; Protein stability ; Context (archaeology) ; Protein Data Bank (RCSB PDB) ; Protein structure ; Computational biology ; Chemistry ; Biochemistry ; Biology ; Crystallography ; Biological system ; Biophysics ; Gene ; Escherichia coli Enzyme Structure and Function ; Protein Structure and Dynamics ; Biofuel production and bioconversion
Authors: M. De Santis | Aude Bailly | Ian D. Coates | S. Grenier | O. Heckmann | K. Hricovíni | Yves Joly | V. Langlais | Aline Y. Ramos | Christine Richter | X. Torrelles | S. Garaudée | Olivier Geaymond | Olivier Ulrich
Source: Acta Crystallographica Section B Structural Science Crystal Engineering and Materials (article, 2019-01-18)
Field: Materials Science — Materials Chemistry

Epitaxial growth and structure of cobalt ferrite thin films with large inversion parameter on Ag(001) Cobalt ferrite ultrathin films with the inverse spinel structure are among the best candidates for spin filtering at room temperature. High-quality epitaxial CoFe<sub>2</sub>O<sub>4</sub> films about 4 nm thick have been fabricated on Ag(001) following a three-step method: an ultrathin metallic CoFe<sub>2</sub> alloy was first grown in coherent epitaxy on the substrate and then treated twice with O<sub>2</sub>, first at room temperature and then during annealing. The epitaxial orientation and the surface, interface and film structure were resolved using a combination of low-energy electron diffraction, scanning tunnelling microscopy, Auger electron spectroscopy and in situ grazing-incidence X-ray diffraction. A slight tetragonal distortion was observed, which should drive the easy magnetization axis in-plane due to the large magneto-elastic coupling of such a material. The so-called inversion parameter, i.e. the Co fraction occupying octahedral sites in the ferrite spinel structure, is a key element for its spin-dependent electronic gap. It was obtained through in situ resonant X-ray diffraction measurements collected at both the Co and Fe K edges. The data analysis was performed using FDMNES, an ab initio program already extensively used to simulate X-ray absorption spectroscopy, and shows that the Co ions are predominantly located on octahedral sites with an inversion parameter of 0.88 (5). Ex situ X-ray photoelectron spectroscopy gives an estimation in accordance with the values obtained through diffraction analysis. Materials science ; Auger electron spectroscopy ; Epitaxy ; Tetragonal crystal system ; Electron diffraction ; Ferromagnetism ; Analytical Chemistry (journal) ; X-ray photoelectron spectroscopy ; Low-energy electron diffraction ; Diffraction ; Thin film ; Spinel ; Magnetization ; Crystallography ; Condensed matter physics ; Optics ; Nuclear magnetic resonance ; Chemistry ; Crystal structure ; Nanotechnology ; Magnetic field ; Metallurgy ; Physics Magnetic Properties and Synthesis of Ferrites ; Magnetic properties of thin films ; Surface and Thin Film Phenomena
Authors: Lei Wang | Mayu Fujii | Misa Namba | Minoru Yamaji | Hideki Okamoto
Source: Tetrahedron Letters (article, 2019-09-20)
Field: Chemistry — Physical and Theoretical Chemistry

Fluorescence properties of amido-substituted 2,3-naphthalimides: Excited-state intramolecular proton transfer (ESIPT) fluorescence and responses to Ca2+ ions Chemistry ; Fluorescence ; Intramolecular force ; Photochemistry ; Excited state ; Ion ; Metal ions in aqueous solution ; Proton ; Amide ; Stereochemistry ; Organic chemistry Photochemistry and Electron Transfer Studies ; Luminescence and Fluorescent Materials ; Radical Photochemical Reactions
Authors: Laboratorio de Geoquímica Ambiental, Laboratorio Nacional de Geoquímica y Mineralogía (LANGEM), Instituto de Geología, Universidad Nacional Autómoma de México | Yusniel Cruz-Hernández | Mario Villalobos | José L. González-Chávez | Departamento de Química Analítica, Facultad de Química, Universidad Nacional Autónoma de México | Nadia Martínez‐Villegas | Instituto Potosino de Investigación Científica y Tecnológica, División de Geociencias
Source: Revista Internacional de Contaminación Ambiental (article, 2019-05-01)
Field: Chemistry — Analytical Chemistry

OPTIMIZING THE DIFFERENTIAL PULSE ANODIC STRIPPING VOLTAMMETRY METHOD WITH A HANGING MERCURY ELECTRODE FOR THALLIUM (I) DETERMINATION IN THE PRESENCE OF LEAD (II) AND COPPER (II) FOR APPLICATION IN CONTAMINATED SOILS Optimizacion del mtodo voltampermetrico de redisolucin andica de pulso diferencial Thallium ; Anodic stripping voltammetry ; Mercury (programming language) ; Copper ; Electrode ; Anode ; Stripping (fiber) ; Chemistry ; Contamination ; Analytical Chemistry (journal) ; Materials science ; Environmental chemistry ; Electrochemistry ; Inorganic chemistry ; Metallurgy ; Computer science ; Physical chemistry Analytical chemistry methods development ; Thallium and Germanium Studies ; Electrochemical Analysis and Applications
Authors: Tushar C. Jagadale | Dhanya S Murali | Shi‐Wei Chu
Source: Beilstein Journal of Nanotechnology (article, 2019-11-06)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Nonlinear absorption and scattering of a single plasmonic nanostructure characterized by <i>x</i> -scan technique Nonlinear nanoplasmonics is a largely unexplored research area that paves the way for many exciting applications, such as nanolasers, nanoantennas, and nanomodulators. In the field of nonlinear nanoplasmonics, it is highly desirable to characterize the nonlinearity of the optical absorption and scattering of single nanostructures. Currently, the common method to quantify optical nonlinearity is the <i>z</i>-scan technique, which yields real and imaginary parts of the permittivity by moving a thin sample with a laser beam. However, <i>z</i>-scan typically works with thin films, and thus acquires nonlinear responses from ensembles of nanostructures, not from single ones. In this work, we present an <i>x</i>-scan technique that is based on a confocal laser scanning microscope equipped with forward and backward detectors. The two-channel detection offers the simultaneous quantification for the nonlinear behavior of scattering, absorption and total attenuation by a single nanostructure. At low excitation intensities, both scattering and absorption responses are linear, thus confirming the linearity of the detection system. At high excitation intensities, we found that the nonlinear response can be derived directly from the point spread function of the <i>x</i>-scan images. Exceptionally large nonlinearities of both scattering and absorption are unraveled simultaneously for the first time. The present study not only provides a novel method for characterizing nonlinearity of a single nanostructure, but also reports surprisingly large plasmonic nonlinearities. Scattering ; Materials science ; Nanostructure ; Plasmon ; Optics ; Absorption (acoustics) ; Laser ; Nonlinear system ; Excitation ; Z-scan technique ; Nonlinear optics ; Optoelectronics ; Physics ; Nanotechnology Gold and Silver Nanoparticles Synthesis and Applications ; Nonlinear Optical Materials Studies ; Plasmonic and Surface Plasmon Research
Authors: Neffer Arvey Gomez Gomez | Gabriel Machado Silva | Helena Maria Wilhelm | Fernando Wypych
Source: Journal of the Brazilian Chemical Society (article, 2019-11-21)
Field: Materials Science — Materials Chemistry

Zn2Al Layered Double Hydroxides Intercalated with Nitrate and p-Aminobenzoate as Ultraviolet Protective Agents in Low-Density Polyethylene Nanocomposites and Natural Insulating Oils Zn 2 Al-Cl and Zn 2 Al-NO 3 layered double hydroxides (LDHs) were synthesized through the fixed pH co-precipitation method and used as precursor materials for the intercalation of p-aminobenzoate through anion exchange reactions. Since chloride in Zn 2 Al -Cl was only partially exchanged with p-aminobenzoate, the phases Zn 2 Al -NO 3 and Zn 2 Al-N -AB (nitrate exchanged with p-aminobenzoate) were used in different percentages as functional fillers to produce nanocomposites of low-density polyethylene doped with maleic anhydride (LDPE). Analysis of thermal properties showed that the melting and recrystallization temperatures were slightly increased in the nanocomposite containing 5 wt.% Zn 2 Al-N-AB. Weathering tests performed with neat LDPE and the nanocomposite films showed that the addition of the LDHs improved ultraviolet (UV) absorption and reduced the LDPE degradation by 25 and 76% when using Zn 2 Al-NO 3 and Zn 2 Al-N-AB, respectively, as fillers, showing a promising UV-shielding effect. Preliminary results also showed a potential application of Zn 2 Al-N-AB as antioxidant in natural insulating oils (natural ester FR3 and castor oil), opening new opportunity of applications. Layered double hydroxides ; Nanocomposite ; Polyethylene ; Materials science ; Ultraviolet ; Chemical engineering ; Toughening ; Polymer chemistry ; Chemistry ; Composite material ; Organic chemistry ; Toughness ; Hydroxide Layered Double Hydroxides Synthesis and Applications ; Synthesis and properties of polymers ; Aerogels and thermal insulation
Authors: Morné Roman | Robert Van Zyl | Nishanth Parus | Nishal Mahatho
Source: SAIEE Africa Research Journal (article, 2019-02-18)
Field: Materials Science — Materials Chemistry

In-Situ Monitoring of Leakage Current on Composite and Glass Insulators of the Cahora Bassa HVDC Transmission Line High voltage transmission line design requires careful insulator selection to ensure good operational performance. This paper reports on the in-situ measurements of leakage current (LC) on composite and glass insulators of the Cahora Bassa high voltage direct current (HVDC) transmission line in South Africa over a 6 - month period. The influence of temperature, humidity, dew, rain and the HVDC line's voltage and current on LC are investigated. The results show that the composite and glass insulator LC behaviour is similar, except in cases of high humidity or rain. At the commencement of rainfall and humidity (>90%), elevated LC levels are observed on glass insulators, while composite insulators demonstrate lower LC levels under these conditions. Under nominal weather conditions of no rain and low humidity, the LC measurements exhibit an almost square-wave behaviour with LC switching between lower (≈20 μA) and higher values (≈60 μA) with relatively short transitions on a daily basis. This phenomenon can be ascribed to condensation on the insulators, which is a primary determinant of the LC levels on contaminated insulators. The line current and voltage fluctuations do not influence the LC level. Insulator (electricity) ; Humidity ; Dew ; Transmission line ; Materials science ; Voltage ; Composite number ; Relative humidity ; Leakage (economics) ; Direct current ; Electric power transmission ; Composite material ; Environmental science ; Electrical engineering ; Optoelectronics ; Meteorology ; Condensation ; Engineering ; Physics High voltage insulation and dielectric phenomena ; Lightning and Electromagnetic Phenomena ; Thermal Analysis in Power Transmission
Authors: Kristof T. Schütt | Michael Gastegger | Alexandre Tkatchenko | Klaus‐Robert Müller
Source: Lecture notes in computer science (preprint, 2019-01-01)
Field: Materials Science — Materials Chemistry

Quantum-Chemical Insights from Interpretable Atomistic Neural Networks Interpretability ; Chemical space ; Quantum chemical ; Artificial neural network ; Computer science ; Interpretation (philosophy) ; Atom (system on chip) ; Statistical physics ; Artificial intelligence ; Chemistry ; Physics ; Quantum mechanics ; Molecule ; Drug discovery Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Siddhartha Laghuvarapu | Yashaswi Pathak | U. Deva Priyakumar
Source: ChemRxiv (preprint, 2019-09-05)
Field: Materials Science — Materials Chemistry

BAND NN: A Deep Learning Framework For Energy Prediction and Geometry Optimization of Organic Small Molecules Recent advances in artificial intelligence along with development of large datasets of energies calculated using quantum mechanical (QM)/density functional theory (DFT) methods have enabled prediction of accurate molecular energies at reasonably low computational cost. However, machine learning models that have been reported so far requires the atomic positions obtained from geometry optimizations using high level QM/DFT methods as input in order to predict the energies, and do not allow for geometry optimization. In this paper, a transferable and molecule-size independent machine learning model (BAND NN) based on a chemically intuitive representation inspired by molecular mechanics force fields is presented. The model predicts the atomization energies of equilibrium and non-equilibrium structures as sum of energy contributions from bonds (B), angles (A), nonbonds (N) and dihedrals (D) at remarkable accuracy. The robustness of the proposed model is further validated by calculations that span over the conformational, configurational and reaction space. The transferability of this model on systems larger than the ones in the dataset is demonstrated by performing calculations on select large molecules. Importantly, employing the BAND NN model, it is possible to perform geometry optimizations starting from non-equilibrium structures along with predicting their energies. Energy minimization ; Robustness (evolution) ; Density functional theory ; Transferability ; Computer science ; Molecular geometry ; Quantum ; Molecule ; Statistical physics ; Geometry ; Physics ; Chemistry ; Machine learning ; Quantum mechanics ; Mathematics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Chemical Physics Studies
Authors: James P. Horwath | Dmitri N. Zakharov | Rémi Mégret | Eric A. Stach
Source: arXiv (Cornell University) (preprint, 2019-12-12)
Field: Materials Science — Materials Chemistry

Understanding Important Features of Deep Learning Models for Transmission Electron Microscopy Image Segmentation Cutting edge deep learning techniques allow for image segmentation with great speed and accuracy. However, application to problems in materials science is often difficult since these complex models may have difficultly learning physical parameters. In situ electron microscopy provides a clear platform for utilizing automated image analysis. In this work we consider the case of studying coarsening dynamics in supported nanoparticles, which is important for understanding e.g. the degradation of industrial catalysts. By systematically studying dataset preparation, neural network architecture, and accuracy evaluation, we describe important considerations in applying deep learning to physical applications, where generalizable and convincing models are required. Deep learning ; Artificial intelligence ; Segmentation ; Computer science ; Image segmentation ; Artificial neural network ; Image (mathematics) ; Transmission electron microscopy ; Enhanced Data Rates for GSM Evolution ; Machine learning ; Computer vision ; Pattern recognition (psychology) ; Nanotechnology ; Materials science Machine Learning in Materials Science ; Electron and X-Ray Spectroscopy Techniques ; Advanced Electron Microscopy Techniques and Applications
Authors: Gianmarc Grazioli | Saswata Roy | Carter T. Butts
Source: Journal of Chemical Information and Modeling (article, 2019-05-07)
Field: Materials Science — Materials Chemistry

Predicting Reaction Products and Automating Reactive Trajectory Characterization in Molecular Simulations with Support Vector Machines A machine learning-based methodology for the prediction of chemical reaction products, along with automated elucidation of mechanistic details via phase space analysis of reactive trajectories, is introduced using low-dimensional heuristic models and then applied to ab initio computer simulations of the photodissociation of acetaldehyde, an important chemical system in atmospheric chemistry. Our method is centered around training Support Vector Machines (SVMs) to identify optimal separatrices that delineate the regions of phase space that lead to different chemical reaction products. In contrast to the more common "black box" type machine learning methodologies for analyzing chemical simulation data, this SVM-based methodology allows for mechanistic insight to be gleaned from further analysis of the positioning of the phase space points used to train the SVM with respect to the separatrices. For example, a pair of phase space points that are in close proximity to each other but on opposite sides of a separatrix may be situated on opposite sides of a transition state, while phase space points occurring early in a simulation that are distant from a separatrix are likely to belong to trajectories that are highly biased toward the product state associated with the basin of attraction to which they belong. In addition to inferring mechanistic details about multiple-pathway chemical reactions, our method can also be used to increase reactive trajectory sampling efficiency in molecular simulations via rejection sampling, with trajectories leading to undesired product states being identified and terminated early in the simulation rather than being carried to completion. Chemical space ; Heuristic ; Support vector machine ; Trajectory ; Phase space ; Computer science ; Chemical Dynamics ; Product (mathematics) ; Sampling (signal processing) ; Biological system ; Chemical reaction ; Black box ; Algorithm ; Chemistry ; Artificial intelligence ; Mathematics ; Physics ; Chemical physics ; Computer vision ; Thermodynamics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Chemical Physics Studies
Authors: Yanyan Zhang | Wenbo Liu | Kaiyu Li | Ping Wang | Hang Cheng | Yang Chen | Xiao Han | Wenjuan Gao
Source: Insight - Non-Destructive Testing and Condition Monitoring (article, 2019-02-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Application of a back-propagation neural network for mechanical properties prediction of ferromagnetic materials by magnetic Barkhausen noise technique Barkhausen effect ; Materials science ; Ferromagnetism ; Artificial neural network ; Noise (video) ; Acoustics ; Computer science ; Condensed matter physics ; Physics ; Magnetic field ; Magnetization ; Artificial intelligence Magnetic Properties and Applications ; Non-Destructive Testing Techniques ; Neural Networks and Applications
Authors: Sirodom Mongkhonthanaphon | Yachai Limpiyakorn
Source: Journal of Physics Conference Series (article, 2019-04-01)
Field: Materials Science — Materials Chemistry

Classification of titanium microstructure with fully convolutional neural networks Titanium and its alloy exhibit excellent properties for biomedical applications, especially in implant surgery. Classification of Titanium microstructure is the process in material inspection that reveals background of the material. Generally, microstructure classification is manually performed. Due to the complexity of microstructure features, expertise is required for process operation. The traditional classification by humans is time consuming and possibly error prone if the inspection is not performed by titanium microstructure experts. Deep learning is considered the revolution of computer vision to enable computers to see and perceive like humans. The technique is widely used for automatically classifying images with high accuracy. In order to reduce human inspection time during quality control, this research presents the use of a type of deep learning, Fully Convolutional Neural Networks, for pixel-wise classification in the titanium microstructure images. The dataset contains private images of titanium samples taken by SEM microscopes. As the available training dataset is small, data augmentation using elastic deformations is applied for increasing the accuracy of the model. Constructed with the U-net architecture, the network achieves good performance with the pixel accuracy of 92.67% and mean IoU of 71.30%. Convolutional neural network ; Microstructure ; Artificial intelligence ; Computer science ; Pixel ; Artificial neural network ; Titanium ; Pattern recognition (psychology) ; Process (computing) ; Computer vision ; Deep learning ; Materials science ; Metallurgy Machine Learning in Materials Science ; Industrial Vision Systems and Defect Detection ; Welding Techniques and Residual Stresses