jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Tomotaroh Granzier-Nakajima | Kazunori Fujisawa | V. Anil | Mauricio Terrones | Yin‐Ting Yeh
Source: Nanomaterials (review, 2019-03-12)
Field: Materials Science — Materials Chemistry

Controlling Nitrogen Doping in Graphene with Atomic Precision: Synthesis and Characterization Graphene provides a unique platform for the detailed study of its dopants at the atomic level. Previously, doped materials including Si, and 0D-1D carbon nanomaterials presented difficulties in the characterization of their dopants due to gradients in their dopant concentration and agglomeration of the material itself. Graphene's two-dimensional nature allows for the detailed characterization of these dopants via spectroscopic and atomic resolution imaging techniques. Nitrogen doping of graphene has been well studied, providing insights into the dopant bonding structure, dopant-dopant interaction, and spatial segregation within a single crystal. Different configurations of nitrogen within the carbon lattice have different electronic and chemical properties, and by controlling these dopants it is possible to either n- or p-type dope graphene, grant half-metallicity, and alter nitrogen doped graphene's (NG) catalytic and sensing properties. Thus, an understanding and the ability to control different types of nitrogen doping configurations allows for the fine tuning of NG's properties. Here we review the synthesis, characterization, and properties of nitrogen dopants in NG beyond atomic dopant concentration. Dopant ; Graphene ; Materials science ; Doping ; Nanotechnology ; Characterization (materials science) ; Carbon fibers ; Chemical physics ; Optoelectronics ; Chemistry ; Composite number ; Composite material Graphene research and applications ; Advanced Memory and Neural Computing ; Supercapacitor Materials and Fabrication
Authors: Yuanbin Liu | Weixiang Hong | Bing Cao
Source: Energy (article, 2019-09-09)
Field: Materials Science — Materials Chemistry

Machine learning for predicting thermodynamic properties of pure fluids and their mixtures Component (thermodynamics) ; Computer science ; Equation of state ; Ternary operation ; Support vector machine ; Thermodynamic state ; Thermodynamic process ; Thermodynamics ; Machine learning ; Material properties ; Artificial intelligence ; Statistical physics ; Physics Machine Learning in Materials Science ; Phase Equilibria and Thermodynamics ; Computational Drug Discovery Methods
Authors: Prabudhya Roy Chowdhury | Colleen Reynolds | Adam Garrett | Tianli Feng | Shashishekar P. Adiga | Xiulin Ruan
Source: Nano Energy (article, 2019-12-28)
Field: Materials Science — Materials Chemistry

Machine learning maximized Anderson localization of phonons in aperiodic superlattices Thermal conductivity ; Superlattice ; Phonon ; Randomness ; Aperiodic graph ; Materials science ; Condensed matter physics ; Thermoelectric effect ; Thermoelectric materials ; Statistical physics ; Optoelectronics ; Physics ; Mathematics ; Thermodynamics ; Statistics ; Composite material Thermal properties of materials ; Advanced Thermoelectric Materials and Devices ; Thermal Radiation and Cooling Technologies
Authors: Tommaso Casalini | Vittorio Limongelli | Mélanie Schmutz | Claudia Som | Olivier Jordan | Peter Wick | Gerrit Borchard | Giuseppe Perale
Source: Frontiers in Bioengineering and Biotechnology (review, 2019-10-17)
Field: Materials Science — Biomaterials

Molecular Modeling for Nanomaterial–Biology Interactions: Opportunities, Challenges, and Perspectives Injection of nanoparticles (NP) into the bloodstream leads to the formation of a so-called "nano-bio" interface where dynamic interactions between nanoparticle surfaces and blood components take place. A common consequence is the formation of the protein corona, that is, a network of adsorbed proteins that can strongly alter the surface properties of the nanoparticle. The protein corona and the resulting structural changes experienced by adsorbed proteins can lead to substantial deviations from the expected cellular uptake as well as biological responses such as NP aggregation and NP-induced protein fibrillation, NP interference with enzymatic activity, or the exposure of new antigenic epitopes. Achieving a detailed understanding of the nano-bio interface is still challenging due to the synergistic effects of several influencing factors like pH, ionic strength, and hydrophobic effects, to name just a few. Because of the multiscale complexity of the system, modeling approaches at a molecular level represent the ideal choice for a detailed understanding of the driving forces and, in particular, the early events at the nano-bio interface. This review aims at exploring and discussing the opportunities and perspectives offered by molecular modeling in this field through selected examples from literature. Nanoparticle ; Nanotechnology ; Nanomaterials ; Molecular dynamics ; Biophysics ; Protein adsorption ; Interface (matter) ; Computational biology ; Materials science ; Chemistry ; Computer science ; Adsorption ; Biology Nanoparticle-Based Drug Delivery ; Polymer Surface Interaction Studies ; Nanoparticles: synthesis and applications
Authors: Hua Tong | Hajime Tanaka
Source: Nature Communications (article, 2019-12-06)
Field: Materials Science — Materials Chemistry

Structural order as a genuine control parameter of dynamics in simple glass formers Glass transition is characterised by drastic dynamical slowing down upon cooling, accompanied by growing spatial heterogeneity. Its rationalisation by subtle changes in the liquid structure has been long debated but remains elusive, due to intrinsic difficulty in detecting the underlying complex structural ordering. Here we report that structural order parameter characterising local packing capability can well describe the glassy dynamics not only macroscopically but also microscopically, no matter whether it is driven by temperature or density. A Vogel-Fulcher-Tammann (VFT)-like relation is universally identified between the structural relaxation time and the order parameter for supercooled liquids with isotropic interactions. More importantly, we find such an intriguing VFT-like relation to be statistically valid even at a particle level, between spatially coarse-grained structural order and microscopic particle-level dynamics. Such a unified description of glassy dynamics based solely on structural order is expected to contribute to the ultimate understanding of the long-standing glass-transition problem. Simple (philosophy) ; Dynamics (music) ; Order (exchange) ; Control (management) ; Statistical physics ; Computer science ; Physics ; Artificial intelligence Material Dynamics and Properties ; Structural Analysis of Composite Materials
Authors: Yayuan He | Pian Wu | Wen Xiao | Guiyin Li | Jiecan Yi | Yafei He | Cuimei Chen | Ping Ding | Yanying Duan
Source: PLoS ONE (article, 2019-03-27)
Field: Chemistry — Analytical Chemistry

Efficient removal of Pb(II) from aqueous solution by a novel ion imprinted magnetic biosorbent: Adsorption kinetics and mechanisms It is vital to understand the adsorption mechanisms and identify the adsorption kinetics when applying an adsorbent to remove heavy metals from aqueous solution. A Pb(II) imprinted magnetic biosorbent (Pb(II)-IMB) was developed for the removal of Pb2+ via lead ion imprinting technology and crosslinking reactions among chitosan (CTS), Serratia marcescens and Fe3O4. The effect of different parameters such as solution pH, adsorbent dosage, selectivity sorption and desorption were investigated on the absorption of lead ion by Pb(II)-IMB. The adsorbent was characterized by a Brunauer-Emmett Teller (BET) analysis, X-ray diffraction (XRD), vibrating sample magnetometry (VSM), scanning electron microscopy (SEM) and energy dispersive spectrometry (EDS). The adsorption kinetics, equilibrium and thermodynamics of Pb(II)-IMB for Pb(II) were studied. The results of the abovementioned analyses showed that the adsorption kinetic process fit well with the second-order equation. The adsorption isotherm process of Pb(II) on the Pb(II)-IMB was closely related to the Langmuir model. Thermodynamic studies suggested the spontaneous and endothermic nature of adsorption of Pb(II) by Pb(II)-IMB. The adsorption mechanism of Pb(II)-IMB was studied by Fourier transform infrared spectroscopy (FTIR) and X-ray photoelectron spectroscopy (XPS). The results indicated that the nitrogen in the amino group and the oxygen in the hydroxyl group of Pb(II)-IMB were coordination atoms. Adsorption ; Chemistry ; Aqueous solution ; Endothermic process ; Desorption ; Fourier transform infrared spectroscopy ; Langmuir adsorption model ; Kinetics ; X-ray photoelectron spectroscopy ; Sorption ; Nuclear chemistry ; Inorganic chemistry ; Physical chemistry ; Chemical engineering Analytical chemistry methods development ; Adsorption and biosorption for pollutant removal ; Electrochemical Analysis and Applications
Authors: Krešimir Molčanov | B. Kojić-Prodić
Source: IUCrJ (review, 2019-02-02)
Field: Chemistry — Organic Chemistry

Towards understanding π-stacking interactions between non-aromatic rings The first systematic study of π interactions between non-aromatic rings, based on the authors' own results from an experimental X-ray charge-density analysis assisted by quantum chemical calculations, is presented. The landmark (non-aromatic) examples include quinoid rings, planar radicals and metal-chelate rings. The results can be summarized as: (i) non-aromatic planar polyenic rings can be stacked, (ii) interactions are more pronounced between systems or rings with little or no π-electron delocalization (<i>e.g.</i> quinones) than those involving delocalized systems (<i>e.g.</i> aromatics), and (iii) the main component of the interaction is electrostatic/multipolar between closed-shell rings, whereas (iv) interactions between radicals involve a significant covalent contribution (multicentric bonding). Thus, stacking covers a wide range of interactions and energies, ranging from weak dispersion to unlocalized two-electron multicentric covalent bonding ('pancake bonding'), allowing a face-to-face stacking arrangement in some chemical species (quinone anions). The predominant interaction in a particular stacked system modulates the physical properties and defines a strategy for crystal engineering of functional materials. Stacking ; Aromaticity ; Computer science ; Data science ; Chemistry ; Molecule Synthesis of heterocyclic compounds ; Organic and Molecular Conductors Research ; Synthesis and Characterization of Pyrroles
Authors: Mary R. Healy | Alexander S. Ivanov | Yana Karslyan | Vyacheslav S. Bryantsev | Bruce A. Moyer | Santa Jansone‐Popova
Source: Chemistry - A European Journal (article, 2019-02-26)
Field: Chemistry — Inorganic Chemistry

Efficient Separation of Light Lanthanides(III) by Using Bis‐Lactam Phenanthroline Ligands Due to the ever-increasing demand for high-purity individual rare-earth elements, novel and highly selective separation processes are increasingly sought after. Herein, we report a separation protocol that employs shape-persistent 2,9-bis-lactam-1,10-phenanthroline (BLPhen) ligands exhibiting unparalleled selectivity for light trivalent lanthanides. The highly preorganised binding pockets of the ligands allowed for the separation of lanthanides with high fidelity, even in the presence of competing transition metals, in a biphasic separation system. Notably, the selectivity trends of the BLPhen ligands towards metal ions across the lanthanide series can be chemically modulated by altering the molecular rigidity of the extractant. Lanthanide ; Phenanthroline ; Selectivity ; Chemistry ; Combinatorial chemistry ; Metal ions in aqueous solution ; Metal ; Rare earth ; Inorganic chemistry ; Ion ; Organic chemistry ; Mineralogy ; Catalysis Radioactive element chemistry and processing ; Lanthanide and Transition Metal Complexes ; Extraction and Separation Processes
Authors: Chenru Duan | Jon Paul Janet | Fang Liu | Aditya Nandy | Heather J. Kulik
Source: Journal of Chemical Theory and Computation (article, 2019-03-12)
Field: Materials Science — Materials Chemistry

Learning from Failure: Predicting Electronic Structure Calculation Outcomes with Machine Learning Models High-throughput computational screening for chemical discovery mandates the automated and unsupervised simulation of thousands of new molecules and materials. In challenging materials spaces, such as open shell transition metal chemistry, characterization requires time-consuming first-principles simulation that often necessitates human intervention. These calculations can frequently lead to a null result, e.g., the calculation does not converge or the molecule does not stay intact during a geometry optimization. To overcome this challenge toward realizing fully automated chemical discovery in transition metal chemistry, we have developed the first machine learning models that predict the likelihood of successful simulation outcomes. We train support vector machine and artificial neural network classifiers to predict simulation outcomes (i.e., geometry optimization result and degree of ⟨ S<sup>2</sup>⟩ deviation) for a chosen electronic structure method based on chemical composition. For these static models, we achieve an area under the curve of at least 0.95, minimizing computational time spent on nonproductive simulations and therefore enabling efficient chemical space exploration. We introduce a metric of model uncertainty based on the distribution of points in the latent space to systematically improve model prediction confidence. In a complementary approach, we train a convolutional neural network classification model on simulation output electronic and geometric structure time series data. This dynamic model generalizes more readily than the static classifier by becoming more predictive as input simulation length increases. Finally, we describe approaches for using these models to enable autonomous job control in transition metal complex discovery. Computer science ; Machine learning ; Artificial intelligence ; Data science Machine Learning in Materials Science ; Software Engineering Research ; Computational Drug Discovery Methods
Authors: Yang Liu | Nicholas Marcella | Janis Timoshenko | Avik Halder | Yang Bing | Lakshmi Kolipaka | Michael J. Pellin | Sönke Seifert | Štefan Vajda | Ping Liu | Anatoly I. Frenkel
Source: The Journal of Chemical Physics (article, 2019-10-22)
Field: Materials Science — Materials Chemistry

Mapping XANES spectra on structural descriptors of copper oxide clusters using supervised machine learning Understanding the origins of enhanced reactivity of supported, subnanometer in size, metal oxide clusters is challenging due to the scarcity of methods capable to extract atomic-level information from the experimental data. Due to both the sensitivity of X-ray absorption near edge structure (XANES) spectroscopy to the local geometry around metal ions and reliability of theoretical spectroscopy codes for modeling XANES spectra, supervised machine learning approach has become a powerful tool for extracting structural information from the experimental spectra. Here, we present the application of this method to grazing incidence XANES spectra of size-selective Cu oxide clusters on flat support, measured in operando conditions of the methanation reaction. We demonstrate that the convolution neural network can be trained on theoretical spectra and utilized to "invert" experimental XANES data to obtain structural descriptors-the Cu-Cu coordination numbers. As a result, we were able to distinguish between different structural motifs (Cu<sub>2</sub>O-like and CuO-like) of Cu oxide clusters, transforming in reaction conditions, and reliably evaluate average cluster sizes, with important implications for the understanding of structure, composition, and function relationships in catalysis. XANES ; Spectroscopy ; Cluster (spacecraft) ; Spectral line ; Oxide ; Copper oxide ; Materials science ; Chemistry ; Computer science ; Physics Catalytic Processes in Materials Science ; Copper-based nanomaterials and applications ; Catalysis and Oxidation Reactions
Authors: Stefano Sanvito | James Nelson
Source: Arrow@dit (Dublin Institute of Technology) (article, 2019-01-01)
Field: Materials Science — Materials Chemistry

Predicting the Curie temperature of ferromagnets using machine learning The magnetic properties of a material are determined by a subtle balance between the various interactions at play, a fact that makes the design of new magnets a daunting task. High-throughput electronic structure theory may help to explore the vast chemical space available and offers a design tool to the experimental synthesis. This method efficiently predicts the elementary magnetic properties of a compound and its thermodynamical stability, but it is blind to information concerning the magnetic critical temperature. Here we introduce a range of machine-learning models to predict the Curie temperature TC of ferromagnets. The models are constructed by using experimental data for about 2500 known magnets and consider the chemical composition of a compound as the only feature determining TC. Thus we are able to establish a one-to-one relation between the chemical composition and the critical temperature. We show that the best model can predict TC's with an accuracy of about 50 K. Most importantly our model is able to make predictions in regions of the chemical space, where only a small fraction of the data was considered for training. Furthermore, it is able to assess the uncertainty of such predictions. This is demonstrated by tracing the TC of binary intermetallic alloys along their composition space and for the Al-Co-Fe ternary system. Curie temperature ; Intermetallic ; Ternary operation ; Materials science ; Ferromagnetism ; Magnet ; Thermodynamics ; Condensed matter physics ; Computer science ; Physics ; Metallurgy ; Quantum mechanics Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Surface and Thin Film Phenomena
Authors: Eldar M. Khabushev | Dmitry V. Krasnikov | Orysia Zaremba | Alexey P. Tsapenko | Anastasia E. Goldt | Albert G. Nasibulin
Source: The Journal of Physical Chemistry Letters (article, 2019-10-22)
Field: Materials Science — Materials Chemistry

Machine Learning for Tailoring Optoelectronic Properties of Single-Walled Carbon Nanotube Films A machine learning technique, namely, support vector regression, is implemented to enhance single-walled carbon nanotube (SWCNT) thin-film performance for transparent and conducting applications. We collected a comprehensive data set describing the influence of synthesis parameters (temperature and CO<sub>2</sub> concentration) on the equivalent sheet resistance (at 90% transmittance in the visible light range) for SWCNT films obtained by a semi-industrial aerosol (floating-catalyst) CVD with CO as a carbon source and ferrocene as a catalyst precursor. The predictive model trained on the data set shows principal applicability of the method for refining synthesis conditions toward the advanced optoelectronic performance of multiparameter processes such as nanotube growth. Further doping of the improved carbon nanotube films with HAuCl<sub>4</sub> results in the equivalent sheet resistance of 39 Ω/□-one of the lowest values achieved so far for SWCNT films. Carbon nanotube ; Materials science ; Nanotechnology ; Optoelectronics Carbon Nanotubes in Composites ; Nanowire Synthesis and Applications ; Graphene research and applications
Authors: H.J.N. van Eck | Gijs Akkermans | S. Alonso van der Westen | D.U.B. Aussems | M. van Berkel | S. Brons | I. G. J. Classen | H.J. van der Meiden | T.W. Morgan | M. J. van de Pol | J. Scholten | J.W.M. Vernimmen | E.G.P. Vos | M.R. de Baar
Source: Fusion Engineering and Design (article, 2019-04-13)
Field: Materials Science — Materials Chemistry

High-fluence and high-flux performance characteristics of the superconducting Magnum-PSI linear plasma facility Divertor ; Plasma ; Heat flux ; Fluence ; Materials science ; Fusion power ; Nuclear engineering ; Electron temperature ; Atomic physics ; Transient (computer programming) ; Thermonuclear fusion ; Superconducting magnet ; Plasma parameters ; Mechanics ; Superconductivity ; Nuclear physics ; Physics ; Heat transfer ; Irradiation ; Tokamak ; Condensed matter physics ; Computer science Fusion materials and technologies ; Magnetic confinement fusion research ; Nuclear reactor physics and engineering
Authors: Xiaosheng Peng | Jinshu Li | Ganjun Wang | Yijiang Wu | Lee Li | Zhaohui Li | Ashfaque Ahmed Bhatti | Chengke Zhou | D.M. Hepburn | Alistair Reid | M.D. Judd | Wah Hoon Siew
Source: IEEE Transactions on Power Delivery (article, 2019-05-22)
Field: Materials Science — Materials Chemistry

Random Forest Based Optimal Feature Selection for Partial Discharge Pattern Recognition in HV Cables Optimal selection of features of Partial Discharge (PD) signals recorded from defects in High Voltage (HV) cables will contribute not only to the improvement of PD pattern recognition accuracy and efficiency but also to PD parameter visualization in HV cable condition monitoring and diagnostics. This paper presents a novel Random Forest (RF)-based feature-selection algorithm for PD pattern recognition of HV cables. The algorithm is applied to feature selection of both PD signals and interference signals with the aim of obtaining the optimal features for data processing. First, the experimental data acquisition and feature extraction processes are introduced. PD signals were captured from faults created in a cable to obtain the raw PD data, and then, a set of 3500 transient PD pulses and a set of 3500 typical interference pulses were extracted, based on which 34 PD features were extracted for further processing. Furthermore, 119 two-dimensional features and 1082 three-dimensional features were generated. The paper then discusses the basic principle of the RF algorithm. Finally, RF-based feature selection was implemented to determine the optimal features for PD pattern recognition. The results were obtained and evaluated with the Back Propagation Neural Network and Support Vector Machine. Results show that the proposed RF-based method is effective for PD feature selection of HV cables with the potential for application to additional HV power apparatus. Random forest ; Feature selection ; Feature extraction ; Pattern recognition (psychology) ; Partial discharge ; Artificial intelligence ; Computer science ; Feature (linguistics) ; Interference (communication) ; Support vector machine ; Engineering ; Artificial neural network ; Electromagnetic interference ; Electronic engineering ; Voltage ; Electrical engineering ; Telecommunications High voltage insulation and dielectric phenomena ; Power Transformer Diagnostics and Insulation ; Power Systems Fault Detection
Authors: Elyas Goli | Nil A. Parikh | Mostafa Yourdkhani | N.G. Hibbard | Jeffrey S. Moore | Nancy R. Sottos | Philippe H. Geubelle
Source: Composites Part A Applied Science and Manufacturing (article, 2019-12-23)
Field: Chemistry — Organic Chemistry

Frontal polymerization of unidirectional carbon-fiber-reinforced composites Materials science ; Composite material ; Thermosetting polymer ; Dicyclopentadiene ; Volume fraction ; Polymerization ; Fiber ; Exothermic reaction ; Composite number ; Thermal conductivity ; Front velocity ; Volume (thermodynamics) ; Polymer ; Front (military) ; Thermodynamics Photopolymerization techniques and applications ; Additive Manufacturing and 3D Printing Technologies ; Dyeing and Modifying Textile Fibers