jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Hayoung Song | Hyunho Kim | Eunsung Lee
Source: Angewandte Chemie (article, 2018-05-16)
Field: Chemistry — Organic Chemistry

Coumaraz‐2‐on‐4‐ylidene: Ambiphilic N‐Heterocyclic Carbenes with a Tunable Electronic Structure Abstract Herein, a coumaraz‐2‐on‐4‐ylidene ( 1 ) as a new example of an ambiphilic N‐heterocyclic carbene, having electronic properties that can be fine‐tuned, is reported. The N‐carbamic and aryl groups on the carbene carbon center provide exceptionally high electrophilicity and nucleophilicity simultaneously to the carbene center, as evidenced by the 77 Se NMR chemical shifts of their selenoketone derivatives and the CO stretching strengths of their rhodium carbonyl complexes. Since the precursors of 1 could be synthesized from various functionalized Schiff bases in a practical and scalable manner, the electronic properties of 1 can be fine‐tuned in a quantitative and predictable way by using the Hammett σ constant of the functional groups on aryl ring. The facile electronic tuning capability of 1 may be applicable to eliciting novel properties in main‐group and transition‐metal chemistry. Carbene ; Chemistry ; Electrophile ; Aryl ; Transition metal carbene complex ; Electronic effect ; Nucleophile ; Combinatorial chemistry ; Computational chemistry ; Reactivity (psychology) ; Rhodium ; Chemical shift ; Photochemistry ; Organic chemistry ; Physical chemistry ; Catalysis N-Heterocyclic Carbenes in Organic and Inorganic Chemistry ; Catalytic Cross-Coupling Reactions ; Organoboron and organosilicon chemistry
Authors: Tao Xiong | Radosław Włodarczyk | Peter Saalfrank
Source: Chemical Physics (article, 2018-06-19)
Field: Chemistry — Physical and Theoretical Chemistry

Vibrationally resolved absorption and fluorescence spectra of perylene and N-substituted derivatives from autocorrelation function approaches Perylene ; Chemistry ; Heteroatom ; Absorption (acoustics) ; Fluorescence ; Molecule ; Density functional theory ; Absorption spectroscopy ; Acetonitrile ; Spectral line ; Autocorrelation ; Photochemistry ; Time-dependent density functional theory ; Computational chemistry ; Analytical Chemistry (journal) ; Organic chemistry ; Optics ; Physics Photochemistry and Electron Transfer Studies ; Molecular Junctions and Nanostructures ; Spectroscopy and Quantum Chemical Studies
Authors: Norbert Rácz | Róbert Kormány
Source: Chromatographia (article, 2018-01-03)
Field: Chemistry — Spectroscopy

Retention Modeling in an Extended Knowledge Space Chemical space ; Range (aeronautics) ; Space (punctuation) ; Software ; Cover (algebra) ; Chemistry ; Computer science ; Aerospace engineering ; Drug discovery ; Mechanical engineering ; Engineering Analytical Chemistry and Chromatography ; Microfluidic and Capillary Electrophoresis Applications ; Protein purification and stability
Authors: Damián Grillo | Juan M. R. Albano | Esteban Mocskos | Julio C. Facelli | Mónica Pickholz | Marta B. Ferraro
Source: The Journal of Chemical Physics (article, 2018-06-05)
Field: Chemistry — Organic Chemistry

Mechanical properties of drug loaded diblock copolymer bilayers: A molecular dynamics study In this work, we present results of coarse-grained simulations to study the encapsulation of prilocaine (PLC), both neutral and protonated, on copolymer bilayers through molecular dynamics simulations. Using a previously validated membrane model, we have simulated loaded bilayers at different drug concentrations and at low (protonated PLC) and high (neutral PLC) pH levels. We have characterized key structural parameters of the loaded bilayers in order to understand the effects of encapsulation of PLC on the bilayer structure and mechanical properties. Neutral PLC was encapsulated in the hydrophobic region leading to a thickness increase, while the protonated species partitioned between the water phase and the poly(ethylene oxide)-poly(butadiene) (PBD) interface, relaxing the PBD region and leading to a decrease in the thickness. The tangential pressures of the studied systems were calculated, and their components were decomposed in order to gain insights on their compensation. In all cases, it is observed that the loading of the membrane does not significantly decrease the stability of the bilayer, indicating that the system could be used for drug delivery. Bilayer ; Molecular dynamics ; Protonation ; Lipid bilayer ; Copolymer ; Membrane ; Materials science ; Drug delivery ; Ethylene oxide ; Chemical engineering ; Chemical physics ; Chemistry ; Nanotechnology ; Polymer ; Computational chemistry ; Organic chemistry ; Composite material ; Ion Surfactants and Colloidal Systems ; Lipid Membrane Structure and Behavior ; Electrostatics and Colloid Interactions
Authors: Josephine F. L. Lox | Franziska Eichler | Talha Erdem | Marcus Adam | Nikolai Gaponik | Hilmi Volkan Demir | Vladimir Lesnyak | Alexander Eychmüller
Source: Zeitschrift für Physikalische Chemie (article, 2018-02-15)
Field: Materials Science — Materials Chemistry

Brightly Luminescent Cu-Zn-In-S/ZnS Core/Shell Quantum Dots in Salt Matrices Abstract In the past decades cadmium-free quantum dots (QDs), among which are quaternary colloidal Cu-Zn-In-S/ZnS (CZIS/ZnS) core/shell nanocrystals (NCs), have attracted great scientific interest. Particularly, their low toxicity and the possibility to tune their photoluminescence (PL) properties by varying the composition in the multicomponent system make them highly attractive for applications in light-emitting diodes (LEDs). Thus, the demands for high quality CZIS/ZnS QDs and methods to process them into bulk materials stimulate investigations of these nanomaterials. Herein, we demonstrate the synthesis of CZIS/ZnS core/shell NCs via a surfactant induced nucleation process, which emit in various colors covering the range from 520 nm to 620 nm possessing high photoluminescence quantum yields (PLQYs) up to 47%. Furthermore, the as synthesized NCs were successfully integrated into two different salt matrices [Na 2 B 4 O 7 (Borax) and LiCl] using two different approaches. The commonly used incorporation of the NCs into Borax salt led to salt crystals emitting from 540 nm to 600 nm with PLQYs up to 24%. By encapsulating the QDs into LiCl, brightly emitting NCs-in-LiCl powders with the PL covering a range from 520 nm to 650 nm with PLQYs of up to 14% were obtained. As a proof of concept, the fabrication of a color conversion LED using NCs encapsulated into LiCl demonstrated the applicability of the encapsulated NCs. Photoluminescence ; Materials science ; Quantum dot ; Nanocrystal ; Luminescence ; Light-emitting diode ; Nucleation ; Nanomaterials ; Salt (chemistry) ; Nanotechnology ; Colloid ; Diode ; Nanoparticle ; Borax ; Chemical engineering ; Optoelectronics ; Chemistry ; Organic chemistry Quantum Dots Synthesis And Properties ; Chalcogenide Semiconductor Thin Films ; Nanocluster Synthesis and Applications
Authors: Antonio J. Martínez‐Galera | José M. Gómez‐Rodríguez
Source: Nanoscale (article, 2018-12-27)
Field: Materials Science — Materials Chemistry

Pseudo-ordered distribution of Ir nanocrystals on h-BN A 2D material consisting of a pseudo-ordered distribution of Ir nanocrystals supported on a h-BN/Rh(111) surface is presented here. The particular spatial distribution of the Ir nanoparticles is achieved thanks to the existence of a large variety of adsorption positions within the pores of the h-BN/Rh(111) nanomesh template with hexagonal symmetry. The resulting deviations of nanoparticle positions with respect to a perfect hexagonal lattice, which make this material of special interest in the field of optics, can be tuned by the temperature and the amount of Ir. Upon annealing, this material undergoes slight structural changes in the temperature range of 370-570 K and much more drastic ones, due to cluster coalescence, between 670 and 770 K. This relatively high onset of coalescence is encouraging for using this 2D material as a catalyst for reactions such as the oxidation of carbon monoxide or of nitrogen monoxide, which are especially relevant in the field of environmental science. Finally, metal nanostructures exhibiting regular geometries have been created from this material using a scanning tunneling microscope tip. Because of the insulating character of h-BN, these nanostructures could be very promising to use in the design of conductive nanotracks. Nanocrystal ; Materials science ; Distribution (mathematics) ; Crystallography ; Nanotechnology ; Chemistry ; Mathematics ; Mathematical analysis Graphene research and applications ; Boron and Carbon Nanomaterials Research ; 2D Materials and Applications
Authors: Chiheb Bahrini | Anne-Cécile Grégoire | Dorel Obada | Christian Mun | Christa Fittschen
Source: Optics & Laser Technology (article, 2018-07-25)
Field: Chemistry — Spectroscopy

Incoherent broad-band cavity enhanced absorption spectroscopy for sensitive and rapid molecular iodine detection in the presence of aerosols and water vapour Vaporization ; Iodine ; Scanning mobility particle sizer ; Materials science ; Spectroscopy ; Particle (ecology) ; Absorption (acoustics) ; Analytical Chemistry (journal) ; Absorption spectroscopy ; Aerosol ; Optics ; Particle size ; Particle-size distribution ; Chemistry ; Environmental chemistry ; Physical chemistry Spectroscopy and Laser Applications ; Atmospheric chemistry and aerosols ; Atmospheric Ozone and Climate
Authors: Sujuan Wang | Zhang‐Wen Wei | Jianyong Zhang | Long Jiang | Dingxin Liu | Ji‐Jun Jiang | Rui Si | Cheng‐Yong Su
Source: IUCrJ (article, 2018-12-10)
Field: Chemistry — Inorganic Chemistry

Framework disorder and its effect on selective hysteretic sorption of a T-shaped azole-based metal–organic framework Metal-organic frameworks with highly ordered porosity have been studied extensively. In this paper, the effect of framework (pore) disorder on the gas sorption of azole-based isoreticular Cu(II) MOFs with <b>rtl</b> topology and characteristic 1D tubular pore channels is investigated for the first time. In contrast to other isoreticular <b>rtl</b> metal-organic frameworks, the Cu(II) metal-organic framework based on 5-(1<i>H</i>-imidazol-1-yl)isophthalate acid has a crystallographically identifiable disordered framework without open N-donor sites. The framework provides a unique example for investigating the effect of pore disorder on gas sorption that can be systematically evaluated. It exhibits remarkable temperature-dependent hysteretic CO<sub>2</sub> sorption up to room temperature, and shows selectivity of CO<sub>2</sub> over H<sub>2</sub>, CH<sub>4</sub> and N<sub>2</sub> at ambient temperature. The unique property of the framework is its disordered structure featuring distorted 1D tubular channels and DMF-guest-remediated defects. The results imply that structural disorder (defects) may play an important role in the modification of the performance of the material. Sorption ; Azole ; Chemistry ; Metal-organic framework ; Computer science ; Combinatorial chemistry ; Adsorption ; Organic chemistry ; Antifungal ; Medicine Metal-Organic Frameworks: Synthesis and Applications ; Advanced NMR Techniques and Applications ; Supramolecular Self-Assembly in Materials
Authors: F. De Luca | Paul Sunday Nnamchi | A. Younes | A.T. Fry | S. González
Source: Journal of Alloys and Compounds (article, 2018-12-07)
Field: Materials Science — Materials Chemistry

Stress-induced martensitic transformation of Cu50Zr50 shape memory alloy optimized through microalloying and co-microalloying Materials science ; Shape-memory alloy ; Annealing (glass) ; Nanoindentation ; Alloy ; Metallurgy ; Diffusionless transformation ; Differential scanning calorimetry ; Crystal twinning ; Martensite ; Microstructure Shape Memory Alloy Transformations ; High Entropy Alloys Studies ; Titanium Alloys Microstructure and Properties
Authors: Seongok Ryu | Jaechang Lim | Woo Youn Kim
Source: arXiv (Cornell University) (article, 2018-05-28)
Field: Materials Science — Materials Chemistry

Deeply learning molecular structure-property relationships using graph attention neural network. Molecular structure-property relationships are key to molecular engineering for materials and drug discovery. The rise of deep learning offers a new viable solution to elucidate the structure-property relationships directly from chemical data. Here we show that the performance of graph convolutional networks (GCNs) for the prediction of molecular properties can be improved by incorporating attention and gate mechanisms. The attention mechanism enables a GCN to identify atoms in different environments. The gated skip-connection further improves the GCN by updating feature maps at an appropriate rate. We demonstrate that the resulting attention- and gate-augmented GCN could extract better structural features related to a target molecular property such as solubility, polarity, synthetic accessibility and photovoltaic efficiency compared to the vanilla GCN. More interestingly, it identified two distinct parts of molecules as essential structural features for high photovoltaic efficiency, and each of them coincided with the areas of donor and acceptor orbitals for charge-transfer excitations, respectively. As a result, the new model could accurately predict molecular properties and place molecules with similar properties close to each other in a well-trained latent space, which is critical for successful molecular engineering. Chemical space ; Computer science ; Graph ; Molecular graph ; Convolutional neural network ; Molecular engineering ; Property (philosophy) ; Deep learning ; Artificial intelligence ; Biological system ; Materials science ; Drug discovery ; Chemistry ; Theoretical computer science ; Nanotechnology ; Biology Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Edward Kim | Zach Jensen | Alexander van Grootel | Kevin Huang | Matthew Staib | Sheshera Mysore | Haw-Shiuan Chang | Emma Strubell | Andrew McCallum | Stefanie Jegelka | Elsa Olivetti
Source: arXiv (Cornell University) (preprint, 2018-12-31)
Field: Materials Science — Materials Chemistry

Inorganic Materials Synthesis Planning with Literature-Trained Neural Networks Leveraging new data sources is a key step in accelerating the pace of materials design and discovery. To complement the strides in synthesis planning driven by historical, experimental, and computed data, we present an automated method for connecting scientific literature to synthesis insights. Starting from natural language text, we apply word embeddings from language models, which are fed into a named entity recognition model, upon which a conditional variational autoencoder is trained to generate syntheses for arbitrary materials. We show the potential of this technique by predicting precursors for two perovskite materials, using only training data published over a decade prior to their first reported syntheses. We demonstrate that the model learns representations of materials corresponding to synthesis-related properties, and that the model's behavior complements existing thermodynamic knowledge. Finally, we apply the model to perform synthesizability screening for proposed novel perovskite compounds. Computer science ; Key (lock) ; Autoencoder ; Complement (music) ; Pace ; Artificial intelligence ; Word (group theory) ; Artificial neural network ; Language model ; Natural language processing ; Machine learning ; Theoretical computer science ; Chemistry Machine Learning in Materials Science ; Topic Modeling ; Natural Language Processing Techniques
Authors: Yuta Suzuki | Hideitsu Hino | Yasuo Takeichi | Takafumi Hawai | Masato Kotsugi | Kanta Ono
Source: Microscopy and Microanalysis (article, 2018-08-01)
Field: Materials Science — Materials Chemistry

Machine Learning-based Crystal Structure Prediction for X-Ray Microdiffraction An abstract is not available for this content so a preview has been provided. As you have access to this content, a full PDF is available via the 'Save PDF' action button. X-ray ; Materials science ; Content (measure theory) ; Crystal (programming language) ; Crystallography ; Artificial intelligence ; Computer science ; Optics ; Physics ; Chemistry ; Mathematics Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Enzyme Structure and Function
Authors: Liyu Gong | Qiang Cheng
Source: arXiv (Cornell University) (preprint, 2018-09-07)
Field: Materials Science — Materials Chemistry

Exploiting Edge Features in Graph Neural Networks Edge features contain important information about graphs. However, current state-of-the-art neural network models designed for graph learning, e.g. graph convolutional networks (GCN) and graph attention networks (GAT), adequately utilize edge features, especially multi-dimensional edge features. In this paper, we build a new framework for a family of new graph neural network models that can more sufficiently exploit edge features, including those of undirected or multi-dimensional edges. The proposed framework can consolidate current graph neural network models; e.g. graph convolutional networks (GCN) and graph attention networks (GAT). The proposed framework and new models have the following novelties: First, we propose to use doubly stochastic normalization of graph edge features instead of the commonly used row or symmetric normalization approches used in current graph neural networks. Second, we construct new formulas for the operations in each individual layer so that they can handle multi-dimensional edge features. Third, for the proposed new framework, edge features are adaptive across network layers. As a result, our proposed new framework and new models can exploit a rich source of graph information. We apply our new models to graph node classification on several citation networks, whole graph classification, and regression on several molecular datasets. Compared with the current state-of-the-art methods, i.e. GCNs and GAT, our models obtain better performance, which testify to the importance of exploiting edge features in graph neural networks. Computer science ; Exploit ; Normalization (sociology) ; Graph ; Theoretical computer science ; Convolutional neural network ; Artificial neural network ; Artificial intelligence Machine Learning in Materials Science ; Advanced Graph Neural Networks ; Computational Drug Discovery Methods
Authors: Nicodemo Di Pasquale | Stuart J. Davie | Paul L. A. Popelier
Source: The Journal of Chemical Physics (article, 2018-04-04)
Field: Materials Science — Materials Chemistry

The accuracy of <i>ab initio</i> calculations without <i>ab initio</i> calculations for charged systems: Kriging predictions of atomistic properties for ions in aqueous solutions Using the machine learning method kriging, we predict the energies of atoms in ion-water clusters, consisting of either Cl<sup>-</sup> or Na<sup>+</sup> surrounded by a number of water molecules (i.e., without Na<sup>+</sup>Cl<sup>-</sup> interaction). These atomic energies are calculated following the topological energy partitioning method called Interacting Quantum Atoms (IQAs). Kriging predicts atomic properties (in this case IQA energies) by a model that has been trained over a small set of geometries with known property values. The results presented here are part of the development of an advanced type of force field, called FFLUX, which offers quantum mechanical information to molecular dynamics simulations without the limiting computational cost of ab initio calculations. The results reported for the prediction of the IQA components of the energy in the test set exhibit an accuracy of a few kJ/mol, corresponding to an average error of less than 5%, even when a large cluster of water molecules surrounding an ion is considered. Ions represent an important chemical system and this work shows that they can be correctly taken into account in the framework of the FFLUX force field. Ab initio ; Ion ; Force field (fiction) ; Kriging ; Ab initio quantum chemistry methods ; Quantum ; Chemistry ; Molecular dynamics ; Molecule ; Field (mathematics) ; Molecular physics ; Atomic physics ; Computational chemistry ; Physics ; Computer science ; Quantum mechanics ; Machine learning ; Mathematics Machine Learning in Materials Science ; Spectroscopy and Quantum Chemical Studies ; Computational Drug Discovery Methods
Authors: Bi Zhao | Joel A. Greenberg | Scott D. Wolter
Source:  (article, 2018-05-04)
Field: Materials Science — Materials Chemistry

Application of machine learning to x-ray diffraction-based classification X-ray diffraction-based baggage screening provides the potential for the material sensitivity needed to realize high detection probabilities and low false alarm rates. However, the combination of noisy signals, variability in the XRD form factors based on slight material differences, and incomplete material libraries lead to decreased system performance. By using a machine learning classification approach to XRD-based explosives detection, we show that the probability of error can be reduced relative to traditional, correlation-based classifiers. This improved performance exists at a variety of noise levels and degrees of library completeness, and indicates a path toward increased XRD-based classifier robustness. Robustness (evolution) ; Diffraction ; Artificial intelligence ; Computer science ; Classifier (UML) ; Explosive material ; False alarm ; Machine learning ; Pattern recognition (psychology) ; X-ray crystallography ; Optics ; Physics ; Chemistry X-ray Diffraction in Crystallography ; Nuclear Physics and Applications ; Machine Learning in Materials Science
Authors: Guangmin Sun | Hao Liu | Cunfu He | Yu Li | Xiucheng Liu | Zibo Li | Ruihuan Zhang | H. Lu
Source: Journal of Nondestructive Evaluation (article, 2018-10-31)
Field: Materials Science — Electronic, Optical and Magnetic Materials

A Novel Prediction Method for Hardness Using Auto-regressive Spectrum of Barkhausen Noise Barkhausen effect ; Artificial neural network ; Principal component analysis ; Materials science ; Noise (video) ; Ferromagnetism ; Computer science ; Pattern recognition (psychology) ; Artificial intelligence ; Magnetization ; Physics Magnetic Properties and Applications ; Non-Destructive Testing Techniques ; Microstructure and Mechanical Properties of Steels
Authors: Bárbara D. L. Ferreira | Natália R. S. Araújo | Raphael F. Ligório | Fabrício José Pacheco Pujatti | Wagner da Nova Mussel | María Irene Yoshida | Rita C. O. Sebastião
Source: Journal of Thermal Analysis and Calorimetry (article, 2018-07-25)
Field: Materials Science — Materials Chemistry

Kinetic thermal decomposition studies of thalidomide under non-isothermal and isothermal conditions Isothermal process ; Thermogravimetric analysis ; Activation energy ; Chemical process of decomposition ; Thermal decomposition ; Thermodynamics ; Kinetic energy ; Decomposition ; Materials science ; Chemistry ; Physical chemistry ; Organic chemistry ; Physics Thermal and Kinetic Analysis ; Energetic Materials and Combustion ; Chemical Thermodynamics and Molecular Structure