jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Guillaume Fraux | Siwar Chibani | François‐Xavier Coudert
Source: Philosophical Transactions of the Royal Society A Mathematical Physical and Engineering Sciences (article, 2019-05-27)
Field: Chemistry — Inorganic Chemistry

Modelling of framework materials at multiple scales: current practices and open questions The last decade has seen an explosion of the family of framework materials and their study, from both the experimental and computational points of view. We propose here a short highlight of the current state of methodologies for modelling framework materials at multiple scales, putting together a brief review of new methods and recent endeavours in this area, as well as outlining some of the open challenges in this field. We will detail advances in atomistic simulation methods, the development of material databases and the growing use of machine learning for the prediction of properties. This article is part of the theme issue 'Mineralomimesis: natural and synthetic frameworks in science and technology'. Computer science ; Field (mathematics) ; Data science ; Theme (computing) ; Management science ; Current (fluid) ; Open research ; Engineering ; Mathematics Metal-Organic Frameworks: Synthesis and Applications ; Machine Learning in Materials Science ; X-ray Diffraction in Crystallography
Authors: Noam Bernstein | Bishal Bhattarai | Gábor Cśanyi | D. A. Drabold | Stephen R. Elliott | Volker L. Deringer
Source: Angewandte Chemie (article, 2019-03-06)
Field: Materials Science — Materials Chemistry

Quantifying Chemical Structure and Machine‐Learned Atomic Energies in Amorphous and Liquid Silicon Abstract Amorphous materials are being described by increasingly powerful computer simulations, but new approaches are still needed to fully understand their intricate atomic structures. Here, we show how machine‐learning‐based techniques can give new, quantitative chemical insight into the atomic‐scale structure of amorphous silicon ( a ‐Si). We combine a quantitative description of the nearest‐ and next‐nearest‐neighbor structure with a quantitative description of local stability. The analysis is applied to an ensemble of a ‐Si networks in which we tailor the degree of ordering by varying the quench rates down to 10 10 K s −1 . Our approach associates coordination defects in a ‐Si with distinct stability regions and it has also been applied to liquid Si, where it traces a clear‐cut transition in local energies during vitrification. The method is straightforward and inexpensive to apply, and therefore expected to have more general significance for developing a quantitative understanding of liquid and amorphous states of matter. Amorphous solid ; Silicon ; Amorphous silicon ; Atomic units ; Stability (learning theory) ; Chemical physics ; k-nearest neighbors algorithm ; Materials science ; Coordination number ; Vitrification ; Statistical physics ; Nanotechnology ; Computer science ; Chemistry ; Physics ; Crystalline silicon ; Crystallography ; Artificial intelligence ; Machine learning ; Optoelectronics Machine Learning in Materials Science ; Advanced Electron Microscopy Techniques and Applications ; Enzyme Structure and Function
Authors: Hiromi Oda | Shin Kiyohara | Teruyasu Mizoguchi
Source: Journal of Physics Materials (article, 2019-04-03)
Field: Materials Science — Materials Chemistry

Machine learning for structure determination and investigating the structure-property relationships of interfaces Interfaces, in which the atomic structures are greatly different from those in the bulk, play a crucial role in the material properties. Therefore, determination of a central structure that is involved with the interface properties is an important task in materials research. However, determination of the interface structure requires a huge number of calculations. We previously proposed a powerful machine learning technique based on virtual screening (VS) to determine interface structures (Kiyohara et al 2016 Sci. Adv. 2 e1600746). Here, we discuss the feasibility, versatility, and robustness of the prediction model for VS. Through this study, the prediction model constructed using only 5 types of grain boundaries determines the energies and structures of the 52 grain boundaries. Furthermore, based on the constructed prediction models, we investigated the geometrical differences between the grain boundaries of different rotation axes. We also investigated the structure-property relationship at the grain boundary (GB). We found that a short bond at the GB is the key factor for preferential vacancy formation at the GB. Grain boundary ; Robustness (evolution) ; Interface (matter) ; Property (philosophy) ; Materials science ; Computer science ; Vacancy defect ; Rotation (mathematics) ; Biological system ; Artificial intelligence ; Crystallography ; Chemistry Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides ; Nuclear Materials and Properties
Authors: Hao Yang | Yaoyao Liu | Zhixin Xiong | Long Liang
Source: Analytical Letters (article, 2019-12-08)
Field: Chemistry — Analytical Chemistry

Rapid Determination of Holocellulose and Lignin in Wood by Near Infrared Spectroscopy and Kernel Extreme Learning Machine To improve the production efficiency in the pulp and paper industry, the chemical composition of pulp wood species has to be measured in real-time, especially the holocellulose and acid insoluble lignin contents. Near infrared (NIR) spectroscopy, as a promising rapid and on-line technology, is an attractive and promising tool to determine holocellulose and lignin contents in pulp wood. Due to the high complexity and nonlinearity of the spectra of pulp wood, it is significant to select suitable chemometric methods. In this study, in order to eliminate noise and irrelevant information of the original spectra collected by a portable spectrometer, four methods were used to preprocess the original spectra, including the first derivative, moving average filtering, multiplicative scatter correction and standard normal variate transformation. Next a comparison was conducted using four modeling approaches, including partial least squares (PLS) regression, least square support vector machine (LSSVM), back-propagation neural network (BPNN), and kernel extreme learning machine (KELM). The last three approaches were calibrated using spectral features that reduced the dimensions by principal component analysis (PCA). Furthermore, regularization parameter and kernel function parameter of LSSVM and KELM were optimized by a particle swarm optimization (PSO) algorithm. The results indicated that multiplicative scatter correction efficiently eliminated the spectral noise and irrelative information, and that KELM displayed the best prediction performance compared to the other approaches. Therefore, an inexpensive and portable NIR spectrometer has been employed to accurately and efficiently determine the chemical composition of pulp wood when combined with multiplicative scatter correction and the KELM method. Partial least squares regression ; Pulp (tooth) ; Extreme learning machine ; Principal component analysis ; Biological system ; Lignin ; Hyperspectral imaging ; Spectrometer ; Second derivative ; Chemometrics ; Support vector machine ; Chemistry ; Artificial neural network ; Artificial intelligence ; Computer science ; Mathematics ; Statistics ; Optics ; Chromatography Spectroscopy and Chemometric Analyses ; Wood and Agarwood Research ; Cultural Heritage Materials Analysis
Authors: Yufei Ma | Yaxuan Zheng | Yanwu Zhu
Source: Science China Materials (article, 2019-07-09)
Field: Materials Science — Materials Chemistry

Towards industrialization of graphene oxide Graphene ; Industrialisation ; Materials science ; Oxide ; Nanotechnology ; Scale (ratio) ; Manufacturing engineering ; Engineering ; Metallurgy ; Political science Graphene research and applications ; Supercapacitor Materials and Fabrication ; Graphene and Nanomaterials Applications
Authors: Dana Abdeen | Muataz Ali Atieh | Belabbes Merzougui | Walid Khalfaoui
Source: Materials (article, 2019-05-18)
Field: Materials Science — Materials Chemistry

Corrosion Evaluation of 316L Stainless Steel in CNT-Water Nanofluid: Effect of CNTs Loading Polarization resistance and potentiodynamic scan testing were performed on 316L stainless steel (SS) at room temperature in carbon nanotube (CNT)-water nanofluid. Different CNT loadings of 0.05, 0.1, 0.3 and 0.5 wt% were suspended in deionized water using gum arabic (GA) surfactant. Corrosion potential, Tafel constants, corrosion rates and pitting potential values indicated better corrosion performance in the presence of CNTs with respect to samples tested in GA-water solutions. According to Gibbs free energy of adsorption, CNTs were physically adsorbed into the surface of the metal, and this adsorption followed Langmuir adsorption isotherm type II. Samples tested in CNT nanofluid revealed a corrosion performance comparable to that of tap water and better than that for GA-water solutions. Among all samples tested in CNT nanofluids, the lowest corrosion rate was attained with 0.1 wt% CNT nanofluid, while the highest value was obtained with 0.5 wt% CNT nanofluid. At higher CNT concentrations, accumulated CNTs might form active anodic sites and increase the corrosion rate. SEM images for samples of higher CNT loadings were observed to have higher pit densities and diameters. Nanofluid ; Materials science ; Tafel equation ; Corrosion ; Adsorption ; Carbon nanotube ; Langmuir adsorption model ; Polarization (electrochemistry) ; Composite material ; Metallurgy ; Chemical engineering ; Electrochemistry ; Nanotechnology ; Nanoparticle ; Chemistry ; Electrode ; Organic chemistry Corrosion Behavior and Inhibition ; Hydrogen embrittlement and corrosion behaviors in metals ; Metal and Thin Film Mechanics
Authors: Jesús Carrete | Miquel López-Suárez | Martí Raya-Moreno | Anton Bochkarev | Miquel Royo | Georg K. H. Madsen | Xavier Cartoixà | Natalio Mingo | Riccardo Rurali
Source: Nanoscale (article, 2019-01-01)
Field: Materials Science — Materials Chemistry

Phonon transport across crystal-phase interfaces and twin boundaries in semiconducting nanowires We combine state-of-the-art Green's-function methods and nonequilibrium molecular dynamics calculations to study phonon transport across the unconventional interfaces that make up crystal-phase and twinning superlattices in nanowires. We focus on two of their most paradigmatic building blocks: cubic (diamond/zinc blende) and hexagonal (lonsdaleite/wurtzite) polytypes of the same group-IV or III-V material. Specifically, we consider InP, GaP and Si, and both the twin boundaries between rotated cubic segments and the crystal-phase boundaries between different phases. We reveal the atomic-scale mechanisms that give rise to phonon scattering in these interfaces, quantify their thermal boundary resistance and illustrate the failure of common phenomenological models in predicting those features. In particular, we show that twin boundaries have a small but finite interface thermal resistance that can only be understood in terms of a fully atomistic picture. Crystal twinning ; Phonon ; Nanowire ; Condensed matter physics ; Superlattice ; Materials science ; Non-equilibrium thermodynamics ; Phase (matter) ; Molecular dynamics ; Crystal (programming language) ; Nanotechnology ; Physics ; Microstructure ; Quantum mechanics Thermal properties of materials ; Advancements in Semiconductor Devices and Circuit Design ; Advanced Thermoelectric Materials and Devices
Authors: Wenyi Wang | Xiliang Yan | Linlin Zhao | Daniel P. Russo | Shenqing Wang | Yin Liu | Alexander Sedykh | Xiaoli Zhao | Bing Yan | Hao Zhu
Source: Journal of Cheminformatics (article, 2019-01-18)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Universal nanohydrophobicity predictions using virtual nanoparticle library To facilitate the development of new nanomaterials, especially nanomedicines, a novel computational approach was developed to precisely predict the hydrophobicity of gold nanoparticles (GNPs). The core of this study was to develop a large virtual gold nanoparticle (vGNP) library with computational nanostructure simulations. Based on the vGNP library, a nanohydrophobicity model was developed and then validated against externally synthesized and tested GNPs. This approach and resulted model is an efficient and effective universal tool to visualize and predict critical physicochemical properties of new nanomaterials before synthesis, guiding nanomaterial design. Nanomaterials ; Colloidal gold ; Nanotechnology ; Computer science ; Nanoparticle ; Virtual screening ; Materials science ; Drug discovery ; Chemistry Gold and Silver Nanoparticles Synthesis and Applications ; Computational Drug Discovery Methods ; Nanoparticles: synthesis and applications
Authors: Ahmad Said | Surajit Tekasakul | Khamphe Phoungthong
Source: Polish Journal of Environmental Studies (article, 2019-08-09)
Field: Chemistry — Organic Chemistry

Investigation of Hydrochar Derived from MaleOil Palm Flower: Characteristics and Applicationfor Dye Removal Male oil palm flower (MOPF) is an abundant and otherwise useless waste from oil palm cultivation. This work aimed to transform MOPF to hydrochar and use it as an adsorbent for the removal of methylene blue (MB) dye in water systems, thus dealing with two existing environmental issues simultaneously. The raw biomass was converted to hydrochar at 180C for 8 h in an air atmosphere. Characterization of the hydrochar using FTIR resulted in a change of functional groups after hydrothermal carbonization (HTC) as well as after adsorption, which might involve in MB adsorption. The small increase of pores in hydrochar detected in the BET isotherm analysis might also facilitate MB adsorption. SEM images confirmed the existence of pores on the hydrochar in comparison with the raw biomass. The equilibrium MB adsorption followed a pseudo second-order and a Langmuir isotherm model with the maximum adsorption capacity of 42.92 mgg -1 at 30C. These findings suggest that MOPF could be a low-cost valueadded material and an alternative eco-friendlyhydrochar-based adsorbent for MB removal from aqueous solution. Palm oil ; Palm ; Environmental science ; Chemistry ; Environmental chemistry ; Pulp and paper industry ; Botany ; Biology ; Food science ; Engineering Edible Oils Quality and Analysis
Authors: Abdullah S. Mondol | Milind D. Patel | Jan Rüger | Clara Stiebing | Andreas Kleiber | Thomas Henkel | Jürgen Popp | Iwan W. Schie
Source: Sensors (article, 2019-10-12)
Field: Chemistry — Analytical Chemistry

Application of High-Throughput Screening Raman Spectroscopy (HTS-RS) for Label-Free Identification and Molecular Characterization of Pollen Pollen studies play a critical role in various fields of science. In the last couple of decades, replacement of manual identification of pollen by image-based methods using pollen morphological features was a great leap forward, but challenges for pollen with similar morphology remain, and additional approaches are required. Spectroscopy approaches for identification of pollen, such as Raman spectroscopy has potential benefits over traditional methods, due to the investigation of the intrinsic molecular composition of a sample. However, current Raman-based characterization of pollen is complex and time-consuming, resulting in low throughput and limiting the statistical significance of the acquired data. Previously demonstrated high-throughput screening Raman spectroscopy (HTS-RS) eliminates the complexity as well as human interaction by incorporation full automation of the data acquisition process. Here, we present a customization of HTS-RS for pollen identification, enabling sampling of a large number of pollen in comparison to other state-of-the-art Raman pollen investigations. We show that using Raman spectra we are able to provide a preliminary estimation of pollen types based on growth habits using hierarchical cluster analysis (HCA) as well as good taxonomy of 37 different Pollen using principal component analysis-support vector machine (PCA-SVM) with good accuracy even for the pollen specimens sharing similar morphological features. Our results suggest that HTS-RS platform meets the demands for automated pollen detection making it an alternative method for research concerning pollen. Pollen ; Raman spectroscopy ; Computer science ; Principal component analysis ; Identification (biology) ; Support vector machine ; Biological system ; Artificial intelligence ; Pattern recognition (psychology) ; Biology ; Botany ; Optics ; Physics Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Essential Oils and Antimicrobial Activity
Authors: Safar Saeed Mohammed | Mediha Kök | İbrahim Nazem Qader | Fethi Dağdelen
Source: European Journal of Science and Technology (article, 2019-12-21)
Field: Materials Science — Materials Chemistry

The Developments of piezoelectric Materials and Shape Memory Alloys in Robotic Actuator Systems There is a high demand for functional smart materials, especially for new material groups in advanced Technologies. These materials are used in the actuator, sensor, control systems, and robotic systems, in addition, they can be hybridized with traditional material to create a particular function. Piezoelectric materials and shape memory alloys are the most important families among these groups. This review includes an overview of shape memory alloys (SMAs) and piezoelectric material actuator systems in terms of robotic applications. The theoretical background of each SMAs and piezoelectric materials is well explained. Different types of each system are interpreted. Using actuator-based SMAs and piezoelectricity in the robotic area is extensively overviewed. Some weaknesses and challenges facing such systems have discussed through recent studies in the literature. Actuator ; Shape-memory alloy ; Piezoelectricity ; Smart material ; Mechanical engineering ; Computer science ; Materials science ; Control engineering ; Nanotechnology ; Engineering ; Artificial intelligence ; Composite material Shape Memory Alloy Transformations
Authors: Yupeng Song | Fazhi He | Xiying Zhang
Source: IEEE Access (article, 2019-01-01)
Field: Chemistry — Organic Chemistry

To Identify Tree Species With Highly Similar Leaves Based on a Novel Attention Mechanism for CNN Image identification technology has great significance for forestry production and forestry management. Highly similar object identification tasks, such as tree species with similar leaves, are extremely challenging. Simply using typical Convolutional Neural Networks (CNNs) or simply adding more convolutional layers still performs poorly in the above tasks. In this paper, we present a novel attention mechanism to enhance the CNN for identification of tree species with highly similar leaves. This paper presents a highly discriminative network, namely attention branch based convolutional neural networks (ABCNN), to better distinguish the differences between leaves features. Firstly, we proposed a novel structure, in which an attention branch is added in all block layers of network besides the typical normal branch. Secondly, our attention branch adopts a condensation process to obtain a region of interest (ROI) from global information of input and designs a reconstruction process to amplify the features difference to focus on the ROI. Thirdly, we design a fusion process, which carefully combines the attention branch with a normal branch to improve the network performance in the training process. The proposed ABCNN is tested on special dataset of Leafsnap with highly similar tree leaves. Our approach achieved 91.43% classification accuracy, which is higher than previous methods. Furthermore, ABCNN is also tested on general data set of SVHN and obtains 98.27% classification accuracy, which is the most competitive when considering the lower computational resources for ordinary applications. Both above experiments demonstrate the discrimination and robustness of the proposed method. Computer science ; Discriminative model ; Convolutional neural network ; Artificial intelligence ; Robustness (evolution) ; Pattern recognition (psychology) ; Tree (set theory) ; Block (permutation group theory) ; Identification (biology) ; Tree structure ; Process (computing) ; Quadtree ; Machine learning ; Data mining ; Algorithm ; Binary tree ; Mathematics Wood and Agarwood Research ; Remote Sensing and LiDAR Applications ; Smart Agriculture and AI
Authors: Zhi Chen | Guande Wang | Xiong Wang
Source: IEEE photonics journal (article, 2019-01-31)
Field: Materials Science — Materials Chemistry

Physical Mechanism and Response Characteristics of Unsaturated Optical Stopping-Based Amorphous Arsenic Sulfide Thin-Film Waveguides Optical stopping can serve as a light-controlled solution to implementing photonic components. In contrast to saturated optical stopping that provides enough excited electrons to entirely absorb signal lights, unsaturated optical stopping introduces a photon competition mechanism for signal lights and pump lights due to insufficient excited electrons. This photon competition mechanism produces a kind of regular signals, which is very similar to the signals applied in pulse-coupled neural network. However, due to its unclear physical mechanism, it is impossible to further improve performances of the unsaturated optical stopping-based components. Here, we propose a kinetic model to reveal physical mechanism of the unsaturated optical stopping and establish a corresponding formalism that enables calculation of time response of the unsaturated optical stopping-based amorphous arsenic sulfide thin-film waveguides. Our work provides a new insight into the physical phenomenon of unsaturated optical stopping and builds a theoretical foundation for further research on response characteristics of amorphous arsenic sulfide thin-film waveguides. Arsenic ; Thin film ; Optoelectronics ; Amorphous solid ; Mechanism (biology) ; Materials science ; Sulfide ; Nanotechnology ; Chemistry ; Physics ; Metallurgy Phase-change materials and chalcogenides ; Photonic Crystals and Applications ; Nonlinear Optical Materials Studies
Authors: Paul Schanda
Source: Journal of Magnetic Resonance (review, 2019-07-11)
Field: Chemistry — Spectroscopy

Relaxing with liquids and solids – A perspective on biomolecular dynamics Molecular dynamics ; Dynamics (music) ; Relaxation (psychology) ; Chemical physics ; Anisotropy ; Dispersion (optics) ; Perspective (graphical) ; Nuclear magnetic resonance ; Materials science ; Chemistry ; Statistical physics ; Physics ; Nanotechnology ; Computational chemistry ; Computer science ; Optics Advanced NMR Techniques and Applications ; NMR spectroscopy and applications ; Advanced MRI Techniques and Applications