jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Baptiste Thomas | Kai‐Cheng Yan | Xi‐Le Hu | Marion Donnier‐Maréchal | Guo‐Rong Chen | Xiao‐Peng He | Sébastien Vidal
Source: Chemical Society Reviews (review, 2020-01-01)
Field: Materials Science — Materials Chemistry

Fluorescent glycoconjugates and their applications Glycoconjugates and their applications as lectin ligands in biology have been thoroughly investigated in the past decades. Meanwhile, the intrinsic properties of such multivalent molecules were limited essentially to their ability to bind to their receptors with high selectivity and/or avidity. The present review will focus on multivalent glycoconjugates displaying an additional capability such as fluorescence properties not only for applications toward imaging of cancer cells and detection of proteins or pathogens but also for drug delivery systems toward targeted cancer therapy. This review is a collection of research articles discussed in the context of the structural features of fluorescent glycoconjugates organized according to their fluorescent core scaffold and with their representative applications. Glycoconjugate ; Fluorescence ; Chemistry ; Combinatorial chemistry ; Biochemistry ; Physics ; Optics Luminescence and Fluorescent Materials ; Molecular Sensors and Ion Detection ; Porphyrin and Phthalocyanine Chemistry
Authors: Jonathan Barrios-Rivera | Yingjian Xu | Martin Wills | Vijyesh K. Vyas
Source: Organic Chemistry Frontiers (article, 2020-01-01)
Field: Chemistry — Inorganic Chemistry

A diversity of recently reported methodology for asymmetric imine reduction This review describes recent developments in enantioselective imine reduction, including related substrates in which a CN bond is the target for reduction, and <italic>in situ</italic> methods. Imine ; Chemistry ; Enantioselective synthesis ; Reduction (mathematics) ; Combinatorial chemistry ; Organic chemistry ; Mathematics ; Catalysis Asymmetric Hydrogenation and Catalysis ; Chemical Synthesis and Analysis ; Surface Chemistry and Catalysis
Authors: Diaa‐Eldin A. Mansour | Nagat M. K. Abdel‐Gawad | Adel Z. El Dein | Hanaa M. Ahmed | Mohamed M. F. Darwish | Matti Lehtonen
Source: Materials (review, 2020-12-25)
Field: Materials Science — Materials Chemistry

Recent Advances in Polymer Nanocomposites Based on Polyethylene and Polyvinylchloride for Power Cables Polymer nanocomposites used in underground cables have been of great interest to researchers over the past 10 years. Their preparation and the dispersion of the nanoparticles through the polymer host matrix are the key factors leading to their enhanced dielectric properties. Their important dielectric properties are breakdown strength, permittivity, conductivity, dielectric loss, space charge accumulation, tracking, and erosion, and partial discharge. An overview of recent advances in polymer nanocomposites based on LDPE, HDPE, XLPE, and PVC is presented, focusing on their preparation and electrical properties. Materials science ; Nanocomposite ; Dielectric ; Composite material ; Polyethylene ; Low-density polyethylene ; Polymer nanocomposite ; High-density polyethylene ; Polymer ; Space charge ; Dielectric strength ; Dispersion (optics) ; Partial discharge ; Dielectric loss ; Permittivity ; Electrical engineering ; Voltage ; Optoelectronics High voltage insulation and dielectric phenomena ; Dielectric materials and actuators ; Polymer Nanocomposite Synthesis and Irradiation
Authors: Jin-Ah Hwang | Moonhee Choi | Hyo-Soon Shin | Byeong‐Kwon Ju | Myoung‐Pyo Chun
Source: Applied Sciences (article, 2020-09-09)
Field: Materials Science — Materials Chemistry

Structural and Magnetic Properties of NiZn Ferrite Nanoparticles Synthesized by a Thermal Decomposition Method Ni1−xZnxFe2O4 (x = 0.5, 0.6, 0.7) nanoparticles were synthesized by a thermal decomposition method. The synthesized particles were identified as pure spinel ferrite structures by X-ray diffraction analysis, and they were calculated to be 46–51 nm in diameter by the Scherrer equation, depending on the composition. In the FE-SEM image, the ferrite nanoparticles have spherical shapes with slight agglomeration, and the particle size is about 50 nm, which was consistent with the value obtained by the Scherrer equation. The lattice parameter of the ferrite nanoparticles monotonically increased from 8.34 to 8.358 Å as the Zn concentration increased from 0.5 to 0.7. Initially, the saturation magnetization value slowly decreases from 81.44 to 83.97 emu/g, then quickly decreases to 71.84 emu/g as the zinc content increases from x = 0.5, through 0.6, to 0.7. Ni1−xZnxFe2O4 toroidal samples were prepared by sintering ferrite nanoparticles at 1250 °C and exhibited faceted grain morphologies in the FE-SEM images with their grain sizes being around 5 µm regardless of the Zinc content. The real magnetic permeability (μ′) of the toroidal samples measured at 5 MHz was monotonically increased from 106, through 150, to 217 with increasing the Zinc content from x = 0.5, through 0.6, to 0.7. The cutoff frequency of the ferrite toroidal samples was estimated to be about 20 MHz from the broad maximum point in the plot of imaginary magnetic permeability (μ″) vs. frequencies, which seemed to be associated with domain wall resonance. Scherrer equation ; Materials science ; Zinc ferrite ; Ferrite (magnet) ; Grain size ; Nanoparticle ; Analytical Chemistry (journal) ; Particle size ; Thermal decomposition ; Composite material ; Chemical engineering ; Nanotechnology ; Chemistry ; Chromatography Magnetic Properties and Synthesis of Ferrites ; Iron oxide chemistry and applications ; Magnetic properties of thin films
Authors: Ming‐Xiao Zhu | Heng-Gao Song | Qiu-Cheng Yu | Jiming Chen | Hongyu Zhang
Source: International Journal of Heat and Mass Transfer (article, 2020-09-04)
Field: Materials Science — Materials Chemistry

Machine-learning-driven discovery of polymers molecular structures with high thermal conductivity Thermal conductivity ; Polymer ; Materials science ; Computer science ; Benchmark (surveying) ; Thermal ; Computation ; Biological system ; Thermodynamics ; Composite material ; Algorithm ; Physics Machine Learning in Materials Science ; Thermal properties of materials ; Fuel Cells and Related Materials
Authors: Yunqi Shao | Matti Hellström | Pavlin D. Mitev | Lisanne Knijff | Chao Zhang
Source: Journal of Chemical Information and Modeling (article, 2020-01-14)
Field: Materials Science — Materials Chemistry

PiNN: A Python Library for Building Atomic Neural Networks of Molecules and Materials Atomic neural networks (ANNs) constitute a class of machine learning methods for predicting potential energy surfaces and physicochemical properties of molecules and materials. Despite many successes, developing interpretable ANN architectures and implementing existing ones efficiently are still challenging. This calls for reliable, general-purpose, and open-source codes. Here, we present a python library named PiNN as a solution toward this goal. In PiNN, we designed a new interpretable and high-performing graph convolutional neural network variant, PiNet, as well as implemented the established Behler-Parrinello neural network. These implementations were tested using datasets of isolated small molecules, crystalline materials, liquid water, and an aqueous alkaline electrolyte. PiNN comes with a visualizer called PiNNBoard to extract chemical insight "learned" by ANNs. It provides analytical stress tensor calculations and interfaces to both the atomic simulation environment and a development version of the Amsterdam Modeling Suite. Moreover, PiNN is highly modularized, which makes it useful not only as a standalone package but also as a chain of tools to develop and to implement novel ANNs. The code is distributed under a permissive BSD license and is freely accessible at https://github.com/Teoroo-CMC/PiNN/ with full documentation and tutorials. Python (programming language) ; MIT License ; Artificial neural network ; Convolutional neural network ; Source code ; Documentation ; Implementation ; Compiler Machine Learning in Materials Science ; Crystallography and molecular interactions ; Inorganic Chemistry and Materials
Authors: Oleg V. Prezhdo
Source: The Journal of Physical Chemistry Letters (article, 2020-11-05)
Field: Materials Science — Materials Chemistry

Advancing Physical Chemistry with Machine Learning ADVERTISEMENT RETURN TO ISSUEEditorialNEXTAdvancing Physical Chemistry with Machine LearningOleg V. PrezhdoOleg V. PrezhdoMore by Oleg V. PrezhdoCite this: J. Phys. Chem. Lett. 2020, 11, 22, 9656–9658Publication Date (Web):November 5, 2020Publication History Published online5 November 2020Published inissue 19 November 2020https://pubs.acs.org/doi/10.1021/acs.jpclett.0c03130https://doi.org/10.1021/acs.jpclett.0c03130editorialACS PublicationsCopyright © Published 2020 by American Chemical Society. This publication is available under these Terms of Use. Request reuse permissions This publication is free to access through this site. Learn MoreArticle Views10442Altmetric-Citations45LEARN ABOUT THESE METRICSArticle Views are the COUNTER-compliant sum of full text article downloads since November 2008 (both PDF and HTML) across all institutions and individuals. These metrics are regularly updated to reflect usage leading up to the last few days.Citations are the number of other articles citing this article, calculated by Crossref and updated daily. Find more information about Crossref citation counts.The Altmetric Attention Score is a quantitative measure of the attention that a research article has received online. Clicking on the donut icon will load a page at altmetric.com with additional details about the score and the social media presence for the given article. Find more information on the Altmetric Attention Score and how the score is calculated. Share Add toView InAdd Full Text with ReferenceAdd Description ExportRISCitationCitation and abstractCitation and referencesMore Options Share onFacebookTwitterWechatLinked InRedditEmail PDF (513 KB) Get e-AlertscloseSUBJECTS:Algorithms,Electronic structure,Materials,Molecular structure,Physical chemistry Get e-Alerts Computer science ; Chemistry ; Nanotechnology ; Materials science Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Various Chemistry Research Topics
Authors: Zachary L. Glick | Derek P. Metcalf | Alexios Koutsoukas | Steven A. Spronk | Daniel L. Cheney | C. David Sherrill
Source: The Journal of Chemical Physics (article, 2020-07-27)
Field: Materials Science — Materials Chemistry

AP-Net: An atomic-pairwise neural network for smooth and transferable interaction potentials Intermolecular interactions are critical to many chemical phenomena, but their accurate computation using ab initio methods is often limited by computational cost. The recent emergence of machine learning (ML) potentials may be a promising alternative. Useful ML models should not only estimate accurate interaction energies but also predict smooth and asymptotically correct potential energy surfaces. However, existing ML models are not guaranteed to obey these constraints. Indeed, systemic deficiencies are apparent in the predictions of our previous hydrogen-bond model as well as the popular ANI-1X model, which we attribute to the use of an atomic energy partition. As a solution, we propose an alternative atomic-pairwise framework specifically for intermolecular ML potentials, and we introduce AP-Net-a neural network model for interaction energies. The AP-Net model is developed using this physically motivated atomic-pairwise paradigm and also exploits the interpretability of symmetry adapted perturbation theory (SAPT). We show that in contrast to other models, AP-Net produces smooth, physically meaningful intermolecular potentials exhibiting correct asymptotic behavior. Initially trained on only a limited number of mostly hydrogen-bonded dimers, AP-Net makes accurate predictions across the chemically diverse S66x8 dataset, demonstrating significant transferability. On a test set including experimental hydrogen-bonded dimers, AP-Net predicts total interaction energies with a mean absolute error of 0.37 kcal mol<sup>-1</sup>, reducing errors by a factor of 2-5 across SAPT components from previous neural network potentials. The pairwise interaction energies of the model are physically interpretable, and an investigation of predicted electrostatic energies suggests that the model "learns" the physics of hydrogen-bonded interactions. Pairwise comparison ; Artificial neural network ; Intermolecular force ; Chemistry ; Interaction energy ; Computation ; Ab initio ; Statistical physics ; Interpretability ; Computer science ; Hydrogen bond ; Computational chemistry ; Artificial intelligence ; Algorithm ; Molecule ; Physics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Crystallography and molecular interactions
Authors: Mikhail Elyashberg | Dimitris Argyropoulos
Source: Magnetic Resonance in Chemistry (article, 2020-12-20)
Field: Chemistry — Spectroscopy

Computer Assisted Structure Elucidation (CASE): Current and future perspectives The first efforts for the development of methods for Computer-Assisted Structure Elucidation (CASE) were published more than 50 years ago. CASE expert systems based on one-dimensional (1D) and two-dimensional (2D) Nuclear Magnetic Resonance (NMR) data have matured considerably by now. The structures of a great number of complex natural products have been elucidated and/or revised using such programs. In this article, we discuss the most likely directions in which CASE will evolve. We act on the premise that a synergistic interaction exists between CASE, new NMR experiments, and methods of computational chemistry, which are continuously being improved. The new developments in NMR experiments (long-range correlation experiments, pure-shift methods, coupling constants measurement and prediction, residual dipolar couplings [RDCs]), and residual chemical shift anisotropies [RCSAs], evolution of density functional theory (DFT), and machine learning algorithms will have an influence on CASE systems and vice versa. This is true also for new techniques for chemical analysis (Atomic Force Microscopy [AFM], "crystalline sponge" X-ray analysis, and micro-Electron Diffraction [micro-ED]), which will be used in combination with expert systems. We foresee that CASE will be utilized widely and become a routine tool for NMR spectroscopists and analysts in academic and industrial laboratories. We believe that the "golden age" of CASE is still in the future. Chemistry ; Premise ; Residual ; Chemical shift ; Computer science ; Algorithm ; Epistemology ; Physical chemistry Molecular spectroscopy and chirality ; Metabolomics and Mass Spectrometry Studies ; Protein Structure and Dynamics
Authors: Ali Raza | Árni Sturluson | Cory M. Simon | Xiaoli Z. Fern
Source: The Journal of Physical Chemistry C (article, 2020-08-06)
Field: Chemistry — Inorganic Chemistry

Message Passing Neural Networks for Partial Charge Assignment to Metal–Organic Frameworks Virtual screenings can accelerate and reduce the cost of discovering metal–organic frameworks (MOFs) for their applications in gas storage, separation, and sensing. In molecular simulations of gas adsorption/diffusion in MOFs, the adsorbate–MOF electrostatic interaction is typically modeled by placing partial point charges on the atoms of the MOF. For the virtual screening of large libraries of MOFs, it is critical to develop computationally inexpensive methods to assign atomic partial charges to MOFs that accurately reproduce the electrostatic potential in their pores. Herein, we design and train a message passing neural network (MPNN) to predict the atomic partial charges on MOFs under a charge neutral constraint. A set of ca. 2250 MOFs labeled with high-fidelity partial charges, derived from periodic electronic structure calculations, serves as training examples. In an end-to-end manner, from charge-labeled crystal graphs representing MOFs, our MPNN machine-learns features of the local bonding environments of the atoms and, from these features, learns to predict partial atomic charges. Our trained MPNN assigns high-fidelity partial point charges to MOFs with orders of magnitude lower computational cost (runtime: ∼3 s) than electronic structure calculations. To enhance the accuracy of virtual screenings of large libraries of MOFs for their adsorption-based applications, we make our trained MPNN model and MPNN-charge-assigned computation-ready, experimental MOF structures publicly available. Partial charge ; Charge (physics) ; Artificial neural network ; Computer science ; Fidelity ; Computation ; Set (abstract data type) ; Constraint (computer-aided design) ; Partial oxidation ; Point (geometry) ; Electrostatics ; Chemical physics ; Materials science ; Nanotechnology ; Computational science ; Chemistry ; Algorithm ; Physics ; Artificial intelligence ; Physical chemistry ; Quantum mechanics ; Mathematics ; Catalysis Metal-Organic Frameworks: Synthesis and Applications ; Machine Learning in Materials Science ; X-ray Diffraction in Crystallography
Authors: Jing Ning | Maoyang Xia | Dong Wang | Xinliang Feng | Hong Zhou | Jincheng Zhang | Yue Hao
Source: Nano-Micro Letters (article, 2020-10-27)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Superior Pseudocapacitive Storage of a Novel Ni3Si2/NiOOH/Graphene Nanostructure for an All-Solid-State Supercapacitor Recent developments in the synthesis of graphene-based structures focus on continuous improvement of porous nanostructures, doping of thin films, and mechanisms for the construction of three-dimensional architectures. Herein, we synthesize creeper-like Ni<sub>3</sub>Si<sub>2</sub>/NiOOH/graphene nanostructures via low-pressure all-solid melting-reconstruction chemical vapor deposition. In a carbon-rich atmosphere, high-energy atoms bombard the Ni and Si surface, and reduce the free energy in the thermodynamic equilibrium of solid Ni-Si particles, considerably catalyzing the growth of Ni-Si nanocrystals. By controlling the carbon source content, a Ni<sub>3</sub>Si<sub>2</sub> single crystal with high crystallinity and good homogeneity is stably synthesized. Electrochemical measurements indicate that the nanostructures exhibit an ultrahigh specific capacity of 835.3 C g<sup>-1</sup> (1193.28 F g<sup>-1</sup>) at 1 A g<sup>-1</sup>; when integrated as an all-solid-state supercapacitor, it provides a remarkable energy density as high as 25.9 Wh kg<sup>-1</sup> at 750 W kg<sup>-1</sup>, which can be attributed to the free-standing Ni<sub>3</sub>Si<sub>2</sub>/graphene skeleton providing a large specific area and NiOOH inhibits insulation on the electrode surface in an alkaline solution, thereby accelerating the electron exchange rate. The growth of the high-performance composite nanostructure is simple and controllable, enabling the large-scale production and application of microenergy storage devices. Materials science ; Graphene ; Nanostructure ; Crystallinity ; Nanotechnology ; Supercapacitor ; Chemical vapor deposition ; Chemical engineering ; Energy storage ; Electrochemistry ; Electrode ; Composite material Supercapacitor Materials and Fabrication ; Graphene research and applications ; Advancements in Battery Materials
Authors: Jingjing Shen | Jianwei Liang | Xinfeng Lin | Hongjian Lin | Jing Yu | Zhaogang Yang
Source: International Journal of Polymer Science (article, 2020-12-01)
Field: Materials Science — Polymers and Plastics

Recent Progress in Polymer-Based Building Materials With the development of human society, the requirements for building materials are becoming higher. The development of polymer materials and their application in the field of architecture have greatly enhanced and broadened the functions of building materials. With the development of material science and technology, many functional materials have been developed. Polymer materials have many excellent properties compared with inorganic materials, and they can also be improved to enhance functional properties by blending or adding various additives (such as flame retardants, antistatic agents, and antioxidants). In this paper, polymer-based building materials are introduced with three classes according to the applications, that is, substrates, coatings, and binders, and their recent signs of progress in the preparations and applications are carefully demonstrated. Materials science ; Antistatic agent ; Polymer ; Nanotechnology ; Building material ; Polymer science ; Composite material Polymer composites and self-healing ; Polymer Nanocomposites and Properties ; Asphalt Pavement Performance Evaluation
Authors: Ahmad Jahanbakhshi | Kamran Kheiralipour
Source: Food Science & Nutrition (article, 2020-05-18)
Field: Chemistry — Analytical Chemistry

Evaluation of image processing technique and discriminant analysis methods in postharvest processing of carrot fruit The most important process before packaging and preserving agricultural products is sorting operation. Sort of carrot by human labor is involved in many problems such as high cost and product waste. Image processing is a modern method, which has different applications in agriculture including classification and sorting. The aim of this study was to classify carrot based on shape using image processing technique. For this, 135 samples with different regular and irregular shapes were selected. After image acquisition and preprocessing, some features such as length, width, breadth, perimeter, elongation, compactness, roundness, area, eccentricity, centroid, centroid nonhomogeneity, and width nonhomogeneity were extracted. After feature selection, linear discriminant analysis (LDA) and quadratic discriminant analysis (QDA) methods were used to classify the features. The classification accuracies of the methods were 92.59 and 96.30, respectively. It can be stated that image processing is an effective way in improving the traditional carrot sorting techniques. Linear discriminant analysis ; Centroid ; Image processing ; Sorting ; Artificial intelligence ; Pattern recognition (psychology) ; Mathematics ; sort ; Preprocessor ; Computer science ; Feature extraction ; Computer vision ; Image (mathematics) ; Algorithm Spectroscopy and Chemometric Analyses ; Date Palm Research Studies ; Smart Agriculture and AI