jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: D. R. M. Williams | Wolfgang Eisfeld
Source: The Journal of Chemical Physics (article, 2018-11-28)
Field: Materials Science — Materials Chemistry

Neural network diabatization: A new <i>ansatz</i> for accurate high-dimensional coupled potential energy surfaces A new diabatization method based on artificial neural networks (ANNs) is presented, which is capable of reproducing high-quality <i>ab initio</i> data with excellent accuracy for use in quantum dynamics studies. The diabatic potential matrix is expanded in terms of a set of basic coupling matrices and the expansion coefficients are made geometry-dependent by the output neurons of the ANN. The ANN is trained with respect to <i>ab initio</i> data using a modified Marquardt-Levenberg back-propagation algorithm. Due to its setup, this approach combines the stability and straightforwardness of a standard low-order vibronic coupling model with the accuracy by the ANN, making it particularly advantageous for problems with a complicated electronic structure. This approach combines the stability and straightforwardness of a standard low-order vibronic coupling model with the accuracy by the ANN, making it particularly advantageous for problems with a complicated electronic structure. This novel ANN diabatization approach has been applied to the low-lying electronic states of NO<sub>3</sub> as a prototypical and notoriously difficult Jahn-Teller system in which the accurate description of the very strong non-adiabatic coupling is of paramount importance. Thorough tests show that an ANN with a single hidden layer is sufficient to achieve excellent results and the use of a "deeper" layering shows no clear benefit. The newly developed diabatic ANN potential energy surface (PES) model accurately reproduces a set of more than 90 000 Multi-configuration Reference Singles and Doubles Configuration Interaction (MR-SDCI) energies for the five lowest PES sheets. Diabatic ; Ansatz ; Maxima and minima ; Artificial neural network ; Adiabatic process ; Coupling (piping) ; Potential energy ; Ab initio ; Stability (learning theory) ; Electronic structure ; Physics ; Computer science ; Statistical physics ; Quantum mechanics ; Mathematics ; Artificial intelligence ; Materials science ; Machine learning ; Mathematical analysis Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Spectroscopy and Quantum Chemical Studies
Authors: Muhammad Shafiq | Kimmo Kauhaniemi | Guillermo Robles | Muzamir Isa | Lauri Kumpulainen
Source: Electric Power Systems Research (article, 2018-11-10)
Field: Materials Science — Materials Chemistry

Online condition monitoring of MV cable feeders using Rogowski coil sensors for PD measurements Condition monitoring is a highly effective prognostic tool for incipient insulation degradation to avoid sudden failures of electrical components and to keep the power network in operation. Improved operational performance of the sensors and effective measurement techniques could enable the development of a robust monitoring system. This paper addresses two main aspects of condition monitoring: an enhanced design of an induction sensor that has the capability of measuring partial discharge (PD) signals emerging simultaneously from medium voltage cables and transformers, and an integrated monitoring system that enables the monitoring of a wider part of the cable feeder. Having described the conventional practices along with the authors’ own experiences and research on non-intrusive solutions, this paper proposes an optimum design of a Rogowski coil that can measure the PD signals from medium voltage cables, its accessories, and the distribution transformers. The proposed PD monitoring scheme is implemented using the directional sensitivity capability of Rogowski coils and a suitable sensor installation scheme that leads to the development of an integrated monitoring model for the components of a MV cable feeder. Furthermore, the paper presents forethought regarding huge amount of PD data from various sensors using a simplified and practical approach. In the perspective of today’s changing grid, the presented idea of integrated monitoring practices provide a concept towards automated condition monitoring. Rogowski coil ; Partial discharge ; Transformer ; Electromagnetic coil ; Electrical engineering ; Voltage ; Engineering ; Current transformer ; Condition monitoring ; Electronic engineering ; Current sensor ; Continuous monitoring ; Reliability engineering ; Computer science High voltage insulation and dielectric phenomena ; Power Transformer Diagnostics and Insulation ; Magnetic Field Sensors Techniques
Authors: Lintao Bu | Mark R. Nimlos | David J. Robichaud | Seonah Kim
Source: Catalysis Today (article, 2018-02-08)
Field: Chemistry — Inorganic Chemistry

Diffusion of aromatic hydrocarbons in hierarchical mesoporous H-ZSM-5 zeolite Mesoporous material ; Microporous material ; Zeolite ; Diffusion ; Molecular sieve ; ZSM-5 ; Chemical engineering ; Knudsen diffusion ; Thermal diffusivity ; Chemistry ; Materials science ; Catalysis ; Organic chemistry ; Porosity ; Thermodynamics Zeolite Catalysis and Synthesis ; Thermochemical Biomass Conversion Processes ; Catalysis for Biomass Conversion
Authors: Sidra Iftekhar | Varsha Srivastava | Deepika Lakshmi Ramasamy | Waqar Ahmad Naseer | Mika Sillanpää
Source: Chemical Engineering Journal (article, 2018-04-22)
Field: Materials Science — Materials Chemistry

A novel approach for synthesis of exfoliated biopolymeric-LDH hybrid nanocomposites via in-stiu coprecipitation with gum Arabic: Application towards REEs recovery Delamination and exfoliation of layered double hydroxides (LDH) is an interesting way for the synthesis of novel nanocomposites. Herein, we report the synthesis of various exfoliated biopolymeric-LDH nanocomposites via in-situ coprecipitation method with gum Arabic (GA). The influence of various divalent ions on the morphological characteristics of GAnXA (n = wt% of GA and X = Mg, Ca, Ba, Sr) was explored Transmission Electron Microscopy (TEM), Atomic Force Microscopy (AFM), and Scanning Electron Microscopy (SEM). X-ray diffraction pattern was used for the determination of phase composition of synthesized nanocomposites. The surface functional groups of GAnXA were investigated by Fourier Transform Spectroscopy (FTIR). Furthermore, the surface charge characteristics was explored by zeta potential analysis. Specific surface area of GAnXA was determined by BET analysis. The overall adsorption of REEs decreased with an increase in ionic size of divalent ions and the noticeable difference in the morphologies of exfoliated GAnXA was observed. In a single component system, the REEs adsorption capacities followed the order: Sc > Y > Nd > Ce > Eu > La, whereas, in multicomponent system, adsorption seems to be competitive and presence of competing ions affect the overall REEs removal. Moreover, HREEs removal superseded over LREEs and the nanocomposites (GA5CA, GA5SA, GA5BA) were highly selective for Sc recovery. The post-adsorption FTIR and SEM results revealed the importance of surface hydroxyl and carboxyl functional groups enacting as the principal REE binding sites. Overall, ability to extract REEs at pH 4 (slightly acidic) presented a facile route for the recovery and separation of REEs from aqueous medium and enhances the possibility of its use in many industrial applications. Coprecipitation ; Adsorption ; Nanocomposite ; Fourier transform infrared spectroscopy ; Exfoliation joint ; Chemistry ; Scanning electron microscope ; Aqueous solution ; Chemical engineering ; Nuclear chemistry ; Zeta potential ; Materials science ; Inorganic chemistry ; Nanoparticle ; Nanotechnology ; Organic chemistry ; Graphene ; Composite material Layered Double Hydroxides Synthesis and Applications ; Magnesium Oxide Properties and Applications ; Aerogels and thermal insulation
Authors: Janio Venturini | Rúbia Young Sun Zampiva | Sabrina Arcaro | Carlos Pérez Bergmann
Source: Ceramics International (article, 2018-04-05)
Field: Materials Science — Materials Chemistry

Sol-gel synthesis of substoichiometric cobalt ferrite (CoFe2O4) spinels: Influence of additives on their stoichiometry and magnetic properties Spinel ; Materials science ; Coercivity ; Stoichiometry ; Remanence ; Cobalt ; Polyvinylpyrrolidone ; Chemical engineering ; Metal ; Citric acid ; Ferrite (magnet) ; Inorganic chemistry ; Magnetization ; Metallurgy ; Composite material ; Chemistry ; Physical chemistry ; Magnetic field ; Organic chemistry ; Polymer chemistry Magnetic Properties and Synthesis of Ferrites ; Multiferroics and related materials ; Copper-based nanomaterials and applications
Authors: Adrián Badía | Julie Movellan | Marı́a J. Barandiaran | José R. Leiza
Source: Industrial & Engineering Chemistry Research (article, 2018-10-04)
Field: Chemistry — Organic Chemistry

High Biobased Content Latexes for Development of Sustainable Pressure Sensitive Adhesives Waterborne pressure sensitive adhesives with biobased contents up to 72% and adhesive performance comparable to pure oil-based formulations have been developed. For that, partially biobased commercial 2-octyl acrylate and isobornyl methacrylate monomers (derived from castor oil and pine resin, respectively) are copolymerized by emulsion polymerization, an environmentally friendly procedure which allows fine-tuning the microstructure of the copolymer at high solids content as well as notably reducing the VOCs in the final product. Formulations with well balance adhesiveness and cohesiveness are achieved, and the reasons are thoroughly discussed. Furthermore, the moderate reduction of the biocontent by the substitution of part of 2-octyl acrylate by the oil-based 2-ethylhexyl acrylate provides trade off waterborne formulations with very promising adhesive properties. Adhesive ; Acrylate ; Castor oil ; Copolymer ; Emulsion ; Materials science ; Methacrylate ; Monomer ; Environmentally friendly ; Miniemulsion ; Polymerization ; Chemical engineering ; Emulsion polymerization ; Polymer chemistry ; Polymer science ; Pulp and paper industry ; Organic chemistry ; Polymer ; Chemistry ; Composite material Advanced Polymer Synthesis and Characterization ; biodegradable polymer synthesis and properties ; Synthetic Organic Chemistry Methods
Authors: Ismael F. Díaz‐Ortega | Juan Manuel Herrera | Daniel Aravena | Eliseo Ruíz | Tulika Gupta | Gopalan Rajaraman | Hiroyuki Nojiri | Enrique Colacio
Source: Inorganic Chemistry (article, 2018-05-18)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Designing a Dy<sub>2</sub> Single-Molecule Magnet with Two Well-Differentiated Relaxation Processes by Using a Nonsymmetric Bis-bidentate Bipyrimidine-<i>N</i>-Oxide Ligand: A Comparison with Mononuclear Counterparts Herein we report a dinuclear [(μ-mbpymNO){(tmh)<sub>3</sub>Dy}<sub>2</sub>] (1) single-molecule magnet (SMM) showing two nonequivalent Dy<sup>III</sup> centers, which was rationally prepared from the reaction of Dy(tmh)<sub>3</sub> moieties (tmh = 2,2,6,6-tetramethyl-3,5-heptanedionate) and the asymmetric bis-bidentate bridging ligand 4-methylbipyrimidine (mbpymNO). Depending on whether the Dy<sup>III</sup> ions coordinate to the N^O or N^N bidentate donor sets, the Dy<sup>III</sup> sites present a NO<sub>7</sub> ( D<sub>2 d</sub> geometry) or N<sub>2</sub>O<sub>6</sub> ( D<sub>4 d</sub>) coordination sphere. As a consequence, two different thermally activated magnetic relaxation processes are observed with anisotropy barriers of 47.8 and 54.7 K. Ab initio calculations confirm the existence of two different relaxation phenomena and allow one to assign the 47.8 and 54.7 K energy barriers to the Dy(N<sub>2</sub>O<sub>6</sub>) and Dy(NO<sub>7</sub>) sites, respectively. Two mononuclear complexes, [Dy(tta)<sub>3</sub>(mbpymNO)] (2) and [Dy(tmh)<sub>3</sub>(phenNO)] (3), have also been prepared for comparative purposes. In both cases, the Dy<sup>III</sup> center shows a NO<sub>7</sub> coordination sphere and SMM behavior is observed with U<sub>eff</sub> values of 71.5 K (2) and 120.7 K (3). In all three cases, ab initio calculations indicate that relaxation of the magnetization takes place mainly via the first excited-state Kramers doublet through Orbach, Raman, and thermally assisted quantum-tunnelling mechanisms. Pulse magnetization measurements reveal that the dinuclear and mononuclear complexes exhibit hysteresis loops with double- and single-step structures, respectively, thus supporting their SMM behavior. Chemistry ; Denticity ; Ligand (biochemistry) ; Magnet ; Molecule ; Crystallography ; Oxide ; Relaxation (psychology) ; Lanthanide ; Nuclear magnetic resonance ; Crystal structure ; Organic chemistry ; Receptor Magnetism in coordination complexes ; Lanthanide and Transition Metal Complexes ; Electron Spin Resonance Studies
Authors: Julian Kranz | Maximilian Kubillus | Raghunathan Ramakrishnan | O. Anatole von Lilienfeld | Marcus Elstner
Source: Journal of Chemical Theory and Computation (article, 2018-03-26)
Field: Materials Science — Materials Chemistry

Generalized Density-Functional Tight-Binding Repulsive Potentials from Unsupervised Machine Learning We combine the approximate density-functional tight-binding (DFTB) method with unsupervised machine learning. This allows us to improve transferability and accuracy, make use of large quantum chemical data sets for the parametrization, and efficiently automatize the parametrization process of DFTB. For this purpose, generalized pair-potentials are introduced, where the chemical environment is included during the learning process, leading to more specific effective two-body potentials. We train on energies and forces of equilibrium and nonequilibrium structures of 2100 molecules, and test on ∼130 000 organic molecules containing O, N, C, H, and F atoms. Atomization energies of the reference method can be reproduced within an error of ∼2.6 kcal/mol, indicating drastic improvement over standard DFTB. Parametrization (atmospheric modeling) ; Tight binding ; Transferability ; Quantum ; Process (computing) ; Molecule ; Statistical physics ; Computer science ; Unsupervised learning ; Density functional theory ; Quantum chemical ; Non-equilibrium thermodynamics ; Physics ; Chemical physics ; Computational chemistry ; Biological system ; Artificial intelligence ; Machine learning ; Chemistry ; Electronic structure ; Quantum mechanics Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Computational Drug Discovery Methods
Authors: Marek Rychter | Anna Baranowska‐Korczyc | Bartłomiej Milanowski | Marcin Jarek | Barbara M. Maciejewska | Emerson Coy | Janina Lulek
Source: Pharmaceutical Research (article, 2018-01-16)
Field: Materials Science — Biomaterials

Cilostazol-Loaded Poly(ε-Caprolactone) Electrospun Drug Delivery System for Cardiovascular Applications The release profile of CIL and nanomechanical properties of all formulations of PCL fibers demonstrate that the cilostazol loaded PCL fibers are an efficient delivery system for vascular implant application. Electrospinning ; Differential scanning calorimetry ; Materials science ; Polycaprolactone ; Fourier transform infrared spectroscopy ; Polymer ; Caprolactone ; Chemical engineering ; Drug delivery ; Nanofiber ; Composite material ; Nanotechnology ; Polymerization Electrospun Nanofibers in Biomedical Applications ; Polymer Nanocomposite Synthesis and Irradiation ; Tribology and Wear Analysis
Authors: Timothy Usher | Kimberley Cousins | Renwu Zhang | Stephen Ducharme
Source: Polymer International (article, 2018-03-15)
Field: Materials Science — Materials Chemistry

The promise of piezoelectric polymers Abstract Recent advances provide new opportunities in the field of polymer piezoelectric materials. Piezoelectric materials provide unique insights to the fundamental understanding of the solid state. In addition, piezoelectric materials have a wide range of applications, representing billions of dollars of commercial applications. However, inorganic piezoelectric materials have limitations that polymer ferroelectric materials can overcome, if certain challenges can be addressed. This mini‐review is a practical summary of the current research and future directions in the investigation and application of piezoelectric materials with an emphasis on polymeric piezoelectric materials. We will assume that the reader is well versed in the subject of polymers, but not as familiar with piezoelectric materials. © 2018 Society of Chemical Industry Piezoelectricity ; Materials science ; Polymer ; Ferroelectricity ; Nanotechnology ; Ferroelectric polymers ; Engineering physics ; Composite material ; Engineering ; Optoelectronics Ferroelectric and Piezoelectric Materials ; Acoustic Wave Resonator Technologies ; Advanced Sensor and Energy Harvesting Materials
Authors: Abdul‐Hackam Ranneh | Hiroyasu Takemoto | Shunya Sakuma | Aziz Awaad | Takahiro Nomoto | Yuki Mochida | Makoto Matsui | Keishiro Tomoda | Mitsuru Naito | Nobuhiro Nishiyama
Source: Angewandte Chemie International Edition (article, 2018-03-07)
Field: Materials Science — Biomaterials

An Ethylenediamine‐based Switch to Render the Polyzwitterion Cationic at Tumorous pH for Effective Tumor Accumulation of Coated Nanomaterials Polyzwitterions are employed as coating polymers for biomaterials to induce an antifouling property on the surface. Fine-tuning the betaine structure switches the antifouling property to be interactive with anionic tissue constituents in response to a tumorous pH gradient. The ethylenediamine moiety in the carboxybetaine enabled stepwise protonation and initiated the di-protonation process around tumorous pH (6.5). The net charge of the developed polyzwitterion (PGlu(DET-Car)) was thus neutral at pH 7.4 for antifouling, but was cationic at pH 6.5 for interaction with anionic constituents. Quantum dots coated with PGlu(DET-Car) exhibited comparable stealth and enhanced tumor accumulation relative to the PEG system. The present study provides a novel design of smart switchable polyzwitterion based on a precise control of the net charge. Ethylenediamine ; Cationic polymerization ; Nanomaterials ; Chemistry ; Nanotechnology ; Materials science ; Combinatorial chemistry ; Inorganic chemistry ; Polymer chemistry Nanoparticle-Based Drug Delivery ; Dendrimers and Hyperbranched Polymers ; Nanoplatforms for cancer theranostics
Authors: Daniel J. Raines | Justin E. Clarke | E.V. Blagova | E.J. Dodson | Keith S. Wilson | Anne‐Kathrin Duhme‐Klair
Source: Nature Catalysis (article, 2018-08-17)
Field: Chemistry — Organic Chemistry

Redox-switchable siderophore anchor enables reversible artificial metalloenzyme assembly Chemistry ; Siderophore ; Combinatorial chemistry ; Biocatalysis ; Catalysis ; Redox ; Artificial enzyme ; Hydrogenase ; Imine ; Selectivity ; Biochemistry ; Organic chemistry ; Reaction mechanism Cyclopropane Reaction Mechanisms ; Metalloenzymes and iron-sulfur proteins ; Electrocatalysts for Energy Conversion
Authors: Pablo Llano‐Suárez | Marta García-Cortés | Marı́a Teresa Fernández-Argüelles | Jorge Ruíz Encinar | Marta Valledor | Francisco Ferrero | J.C. Campo | José M. Costa‐Fernández
Source: Analytica Chimica Acta (review, 2018-08-09)
Field: Materials Science — Materials Chemistry

Functionalized phosphorescent nanoparticles in (bio)chemical sensing and imaging – A review Chemistry ; Phosphorescence ; Nanoparticle ; Nanotechnology ; Environmental chemistry ; Fluorescence Luminescence and Fluorescent Materials ; Analytical Chemistry and Sensors ; Luminescence Properties of Advanced Materials
Authors: Tian Xie | Jeffrey C. Grossman
Source: The Journal of Chemical Physics (article, 2018-11-06)
Field: Materials Science — Materials Chemistry

Hierarchical visualization of materials space with graph convolutional neural networks The combination of high throughput computation and machine learning has led to a new paradigm in materials design by allowing for the direct screening of vast portions of structural, chemical, and property spaces. The use of these powerful techniques leads to the generation of enormous amounts of data, which in turn calls for new techniques to efficiently explore and visualize the materials space to help identify underlying patterns. In this work, we develop a unified framework to hierarchically visualize the compositional and structural similarities between materials in an arbitrary material space with representations learned from different layers of graph convolutional neural networks. We demonstrate the potential for such a visualization approach by showing that patterns emerge automatically that reflect similarities at different scales in three representative classes of materials: perovskites, elemental boron, and general inorganic crystals, covering material spaces of different compositions, structures, and both. For perovskites, elemental similarities are learned that reflects multiple aspects of atom properties. For elemental boron, structural motifs emerge automatically showing characteristic boron local environments. For inorganic crystals, the similarity and stability of local coordination environments are shown combining different center and neighbor atoms. The method could help transition to a data-centered exploration of materials space in automated materials design. Visualization ; Graph ; Space (punctuation) ; Convolutional neural network ; Computation ; Property (philosophy) ; Similarity (geometry) ; Chemical space Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Electrocatalysts for Energy Conversion
Authors: Lukas Turcani | Rebecca L. Greenaway | Kim E. Jelfs
Source: Chemistry of Materials (article, 2018-12-10)
Field: Materials Science — Materials Chemistry

Machine Learning for Organic Cage Property Prediction We use machine learning to predict shape persistence and cavity size in porous organic cages. The majority of hypothetical organic cages suffer from a lack of shape persistence and as a result lack intrinsic porosity, rendering them unsuitable for many applications. We have created the largest computational database of these molecules to date, numbering 63,472 cages, formed through a range of reaction chemistries and in multiple topologies. We study our database and identify features which lead to the formation of shape persistent cages. We find that the imine condensation of trialdehydes and diamines in a [4 + 6] reaction is the most likely to result in shape persistent cages, whereas thiol reactions are most likely to give collapsed cages. Using this database, we develop machine learning models capable of predicting shape persistence with an accuracy of up to 93%, reducing the time taken to predict this property to milliseconds, and removing the need for specialist software. In addition, we develop machine learning models for two other key properties of these molecules, cavity size and symmetry. We provide open-source implementations of our models, together with the accompanying data sets, and an online tool giving users access to our models to easily obtain predictions for a hypothetical cage prior to a synthesis attempt. Computer science ; Machine learning ; Artificial intelligence ; Property (philosophy) ; Algorithm Machine Learning in Materials Science ; Supramolecular Chemistry and Complexes ; Metal-Organic Frameworks: Synthesis and Applications