jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Sudip Pan | Gourhari Jana | Gabriel Merino | Pratim Kumar Chattaraj
Source: ChemistryOpen (review, 2019-01-29)
Field: Chemistry — Inorganic Chemistry

Noble‐Noble Strong Union: Gold at Its Best to Make a Bond with a Noble Gas Atom This Review presents the current status of the noble gas (Ng)-noble metal chemistry, which began in 1977 with the detection of AuNe<sup>+</sup> through mass spectroscopy and then grew from 2000 onwards; currently, the field is in a somewhat matured state. On one side, modern quantum chemistry is very effective in providing important insights into the structure, stability, and barrier for the decomposition of Ng compounds and, as a result, a plethora of viable Ng compounds have been predicted. On the other hand. experimental achievement also goes beyond microscopic detection and characterization through spectroscopic techniques and crystal structures at ambient temperature; for example, (AuXe<sub>4</sub>)<sup>2+</sup>(Sb<sub>2</sub>F<sub>11</sub> <sup>-</sup>)<sub>2</sub> have also been obtained. The bonding between two noble elements of the periodic table can even reach the covalent limit. The relativistic effect makes gold a very special candidate to form a strong bond with Ng in comparison to copper and silver. Insertion compounds, which are metastable in nature, depending on their kinetic stability, display an even more fascinating bonding situation. The degree of covalency in Ng-M (M=noble metal) bonds of insertion compounds is far larger than that in non-insertion compounds. In fact, in MNgCN (M=Cu, Ag, Au) molecules, the M-Ng and Ng-C bonds might be represented as classical 2c-2e σ bonds. Therefore, noble metals, particularly gold, provide the opportunity for experimental chemists to obtain sufficiently stable complexes with Ng at room temperature in order to characterize them by using experimental techniques and, with the intriguing bonding situation, to explore them with various computational tools from a theoretical perspective. This field is relatively young and, in the coming years, a lot of advancement is expected experimentally as well as theoretically. Noble metal ; Noble gas ; Chemistry ; Covalent bond ; Metastability ; Nanotechnology ; Chemical physics ; Metal ; Materials science ; Organic chemistry Inorganic Fluorides and Related Compounds ; Advanced Chemical Physics Studies ; Inorganic Chemistry and Materials
Authors: Lucian Chan | Geoffrey Hutchison | Garrett M. Morris
Source: Journal of Cheminformatics (article, 2019-05-21)
Field: Materials Science — Materials Chemistry

Bayesian optimization for conformer generation Generating low-energy molecular conformers is a key task for many areas of computational chemistry, molecular modeling and cheminformatics. Most current conformer generation methods primarily focus on generating geometrically diverse conformers rather than finding the most probable or energetically lowest minima. Here, we present a new stochastic search method called the Bayesian optimization algorithm (BOA) for finding the lowest energy conformation of a given molecule. We compare BOA with uniform random search, and systematic search as implemented in Confab, to determine which method finds the lowest energy. Energetic difference, root-mean-square deviation, and torsion fingerprint deviation are used to quantify the performance of the conformer search algorithms. In general, we find BOA requires far fewer evaluations than systematic or uniform random search to find low-energy minima. For molecules with four or more rotatable bonds, Confab typically evaluates [Formula: see text] (median) conformers in its search, while BOA only requires [Formula: see text] energy evaluations to find top candidates. Despite using evaluating fewer conformers, 20-40% of the time BOA finds lower-energy conformations than a systematic Confab search for molecules with four or more rotatable bonds. Conformational isomerism ; Maxima and minima ; Random search ; Cheminformatics ; Computer science ; Search algorithm ; Molecule ; Algorithm ; Statistical physics ; Mathematics ; Computational chemistry ; Chemistry ; Physics ; Quantum mechanics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Machine Learning and Data Classification
Authors: Gunnar Alexander Schmitz | Denis G. Artiukhin | Ove Christiansen
Source: The Journal of Chemical Physics (article, 2019-04-03)
Field: Materials Science — Materials Chemistry

Approximate high mode coupling potentials using Gaussian process regression and adaptive density guided sampling We present a new efficient approach for potential energy surface construction. The algorithm employs the n-mode representation and combines an adaptive density guided approach with Gaussian process regression for constructing approximate higher-order mode potentials. In this scheme, the n-mode potential construction is conventionally done, whereas for higher orders the data collected in the preceding steps are used for training in Gaussian process regression to infer the energy for new single point computations and to construct the potential. We explore different delta-learning schemes which combine electronic structure methods on different levels of theory. Our benchmarks show that for approximate 2-mode potentials the errors can be adjusted to be in the order of 8 cm<sup>-1</sup>, while for approximate 3-mode and 4-mode potentials the errors fall below 1 cm<sup>-1</sup>. The observed errors are, therefore, smaller than contributions due to missing higher-order electron excitations or relativistic effects. Most importantly, the approximate potentials are always significantly better than those with neglected higher-order couplings. Coupling (piping) ; Gaussian process ; Mode (computer interface) ; Kriging ; Sampling (signal processing) ; Regression ; Gaussian ; Adaptive sampling ; Statistical physics ; Statistics ; Mathematics ; Computer science ; Physics ; Materials science ; Optics ; Quantum mechanics ; Monte Carlo method Machine Learning in Materials Science ; Thermography and Photoacoustic Techniques ; Advanced Semiconductor Detectors and Materials
Authors: Salvador Gutiérrez | Alexander Wendel | James Underwood
Source: Computers and Electronics in Agriculture (article, 2019-07-25)
Field: Chemistry — Analytical Chemistry

Spectral filter design based on in-field hyperspectral imaging and machine learning for mango ripeness estimation Hyperspectral imaging ; Artificial intelligence ; Filter (signal processing) ; Computer science ; Ripeness ; RGB color model ; Dimensionality reduction ; Multispectral image ; Computer vision ; Spectral signature ; Pattern recognition (psychology) ; Remote sensing Spectroscopy and Chemometric Analyses ; Remote Sensing in Agriculture ; Smart Agriculture and AI
Authors: Yuanyuan Shao | Guantao Xuan | Zhichao Hu | Zongmei Gao | Lei Liu
Source: PLoS ONE (article, 2019-09-18)
Field: Chemistry — Analytical Chemistry

Determination of the bruise degree for cherry using Vis-NIR reflection spectroscopy coupled with multivariate analysis Determination and classification of the bruise degree for cherry can improve consumer satisfaction with cherry quality and enhance the industry's competiveness and profitability. In this study, visible and near infrared (Vis-NIR) reflection spectroscopy was used for identifying bruise degree of cherry in 350-2500 nm. Sampling spectral data were extracted from normal, slight and severe bruise samples. Principal component analysis (PCA) was implemented to determine the first few principal components (PCs) for cluster analysis among samples. Optimal wavelengths were selected by loadings of PCs from PCA and successive projection algorithm (SPA) method, respectively. Afterwards, these optimal wavelengths were empolyed to establish the classification models as inputs of least square-support vector machine (LS-SVM). Better performance for qualitative discrimination of the bruise degree for cherry was emerged in LS-SVM model based on five optimal wavelengths (603, 633, 679, 1083, and 1803 nm) selected directly by SPA, which showed acceptable results with the classification accuracy of 93.3%. Confusion matrix illustrated misclassification generally occurred in normal and slight bruise samples. Furthermore, the latent relation between spectral property of cherries in varying bruise degree and its firmness and soluble solids content (SSC) was analyzed. The result showed both colour, firmness and SSC were consistent with the Vis-NIR reflectance of cherries. Overall, this study revealed that Vis-NIR reflection spectroscopy integrated with multivariate analysis can be used as a rapid, intact method to determine the bruise degree of cherry, laying a foundation for cherry sorting and postharvest quality control. Bruise ; Multivariate analysis ; Spectroscopy ; Degree (music) ; Multivariate statistics ; Physics ; Analytical Chemistry (journal) ; Mathematics ; Chemistry ; Statistics ; Medicine ; Chromatography ; Astronomy ; Surgery Spectroscopy and Chemometric Analyses ; Meat and Animal Product Quality ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: Sijia Wang | Yunhao Chen | Mingguo Wang | Yifei Zhao | Jing Li
Source: Remote Sensing (article, 2019-04-23)
Field: Chemistry — Analytical Chemistry

SPA-Based Methods for the Quantitative Estimation of the Soil Salt Content in Saline-Alkali Land from Field Spectroscopy Data: A Case Study from the Yellow River Irrigation Regions The problem of soil salinization has always been a global problem involving resource, environmental, and ecological issues, and is closely related to the sustainable development of the social economy. Remote sensing provides an effective technical means for soil salinity identification and quantification research. This study focused on the estimation of the soil salt content in saline-alkali soils and applied the Successive Projections Algorithm (SPA) method to the estimation model; twelve spectral forms were applied in the estimation model of the spectra and soil salt content. Regression modeling was performed using the Partial Least Squares Regression (PLSR) method. Proximal-field spectral measurements data and soil samples were collected in the Yellow River Irrigation regions of Shizuishan City. A total of 60 samples were collected. The results showed that application of the SPA method improved the modeled determination coefficient (R2) and the ratio of performance to deviation (RPD), and reduced the modeled root mean square error (RMSE) and the percentage root mean square error (RMSE%); the maximum value of R2 increased by 0.22, the maximum value of RPD increased by 0.97, the maximum value of the RMSE decreased by 0.098 and the maximum value of the RMSE% decreased by 8.52%. The SPA–PLSR model, based on the first derivative of reflectivity (FD), the FD–SPA–PLSR model, showed the best results, with an R2 value of 0.89, an RPD value of 2.72, an RMSE value of 0.177, and RMSE% value of 11.81%. The results of this study demonstrated the applicability of the SPA method in the estimation of soil salinity, by using field spectroscopy data. The study provided a reference for a subsequent study of the hyperspectral estimation of soil salinity, and the proximal sensing data from a low distance, in this study, could provide detailed data for use in future remote sensing studies. Mean squared error ; Soil salinity ; Coefficient of determination ; Soil science ; Environmental science ; Irrigation ; Mathematics ; Salinity ; Partial least squares regression ; Correlation coefficient ; Soil water ; Statistics ; Agronomy ; Ecology Spectroscopy and Chemometric Analyses ; Soil Geostatistics and Mapping ; Remote Sensing in Agriculture
Authors: Huixia Xu | Kaiming Cheng | Ming Chen | Lijun Zhang | Karen Brodersen | Yong Du
Source: Journal of Power Sources (article, 2019-10-17)
Field: Materials Science — Materials Chemistry

Interdiffusion between gadolinia doped ceria and yttria stabilized zirconia in solid oxide fuel cells: Experimental investigation and kinetic modeling Yttria-stabilized zirconia ; Materials science ; Solid oxide fuel cell ; Sintering ; Electrolyte ; Cubic zirconia ; Oxide ; Ohmic contact ; Chemical engineering ; Doping ; Layer (electronics) ; Metallurgy ; Composite material ; Chemistry ; Physical chemistry ; Ceramic ; Electrode Advancements in Solid Oxide Fuel Cells ; Electrocatalysts for Energy Conversion ; Chemical Looping and Thermochemical Processes
Authors: Zdeněk Jakub | Jan Hulva | Matthias Meier | Roland Bliem | Florian Kraushofer | Martin Setvín | Michael Schmid | Ulrike Diebold | Cesare Franchini | Gareth S. Parkinson
Source: Angewandte Chemie (article, 2019-07-24)
Field: Materials Science — Materials Chemistry

Local Structure and Coordination Define Adsorption in a Model Ir<sub>1</sub>/Fe<sub>3</sub>O<sub>4</sub> Single‐Atom Catalyst Abstract Single‐atom catalysts (SACs) bridge homo‐ and heterogeneous catalysis because the active site is a metal atom coordinated to surface ligands. The local binding environment of the atom should thus strongly influence how reactants adsorb. Now, atomically resolved scanning‐probe microscopy, X‐ray photoelectron spectroscopy, temperature‐programmed desorption, and DFT are used to study how CO binds at different Ir 1 sites on a precisely defined Fe 3 O 4 (001) support. The two‐ and five‐fold‐coordinated Ir adatoms bind CO more strongly than metallic Ir, and adopt structures consistent with square‐planar Ir I and octahedral Ir III complexes, respectively. Ir incorporates into the subsurface already at 450 K, becoming inactive for adsorption. Above 900 K, the Ir adatoms agglomerate to form nanoparticles encapsulated by iron oxide. These results demonstrate the link between SAC systems and coordination complexes, and that incorporation into the support is an important deactivation mechanism. Catalysis ; Adsorption ; Chemistry ; Octahedron ; Crystallography ; Atom (system on chip) ; Infrared spectroscopy ; X-ray photoelectron spectroscopy ; Metal ; Scanning tunneling microscope ; Coordination number ; Desorption ; Inorganic chemistry ; Physical chemistry ; Materials science ; Nanotechnology ; Crystal structure ; Chemical engineering ; Ion ; Organic chemistry Catalytic Processes in Materials Science ; Electrocatalysts for Energy Conversion ; Nanomaterials for catalytic reactions
Authors: Ye Yuan | Qingkun Liu | Bohdan Senyuk | Ivan I. Smalyukh
Source: Nature (article, 2019-05-29)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Elastic colloidal monopoles and reconfigurable self-assembly in liquid crystals Colloid ; Liquid crystal ; Colloidal particle ; Self-assembly ; Colloidal crystal ; Materials science ; Chemical physics ; Nanotechnology ; Crystallography ; Chemical engineering ; Physics ; Chemistry ; Optoelectronics ; Engineering Liquid Crystal Research Advancements ; Pickering emulsions and particle stabilization ; Advanced Materials and Mechanics
Authors: Anne-Caroline Genix | Vera Bocharova | Bobby Carroll | Michelle Lehmann | Tomonori Saito | Susan Krueger | Lilin He | Philippe Dieudonné-George | Alexei P. Sokolov | Julian Oberdisse
Source: ACS Applied Materials & Interfaces (article, 2019-04-22)
Field: Materials Science — Polymers and Plastics

Understanding the Static Interfacial Polymer Layer by Exploring the Dispersion States of Nanocomposites The dynamic and static properties of the interfacial region between polymer and nanoparticles have wide-ranging consequences on performances of nanomaterials. The thickness and density of the static layer are particularly difficult to assess experimentally due to superimposing nanoparticle interactions. Here, we tune the dispersion of silica nanoparticles in nanocomposites by preadsorption of polymer layers in the precursor solutions, and by varying the molecular weight of the matrix chains. Nanocomposite structures ranging from ideal dispersion to repulsive order or various degrees of aggregation are generated and observed by small-angle scattering. Preadsorbed chains are found to promote ideal dispersion, before desorption in the late stages of nanocomposite formation. The microstructure of the interfacial polymer layer is characterized by detailed modeling of X-ray and neutron scattering. Only in ideally well-dispersed systems a static interfacial layer of reduced polymer density over a thickness of ca. 2 nm is evidenced based on the analysis with a form-free density profile optimized using numerical simulations. This interfacial gradient layer is found to be independent of the thickness of the initially adsorbed polymer, but appears to be generated by out-of-equilibrium packing and folding of the preadsorbed layer. The impact of annealing is investigated to study the approach of equilibrium, showing that initially ideally well-dispersed systems adopt a repulsive hard-sphere structure, while the static interfacial layer disappears. This study thus promotes the fundamental understanding of the interplay between effects which are decisive for macroscopic material properties: polymer-mediated interparticle interactions, and particle interfacial effects on the surrounding polymer. Materials science ; Nanocomposite ; Polymer ; Dispersion (optics) ; Layer (electronics) ; Composite material ; Nanotechnology ; Optics Polymer Nanocomposites and Properties ; Dielectric materials and actuators ; Polymer crystallization and properties
Authors: Mateusz Skałoń | Michael Görtler | Benjamin Meier | Siegfried Arneitz | Nikolaus Urban | Stefan Mitsche | Christian Huber | Jörg Franke | Christof Sommitsch
Source: Materials (article, 2019-12-29)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Influence of Melt-Pool Stability in 3D Printing of NdFeB Magnets on Density and Magnetic Properties The current work presents the results of an investigation focused on the influence of process parameters on the melt-track stability and its consequence to the sample density printed out of NdFeB powder. Commercially available powder of Nd7.5Pr0.7Fe75.4Co2.5B8.8Zr2.6Ti2.5 alloy was investigated at the angle of application in selective laser melting of permanent magnets. Using single track printing the stability of the melt pool was investigated under changing process parameters. The influence of changing laser power, scanning speed, and powder layer thickness on density, porosity structure, microstructure, phase composition, and magnetic properties were investigated. The results showed that energy density coupled with powder layer thickness plays a crucial role in melt-track stability. It was possible to manufacture magnets of both high relative density and high magnetic properties. Magnetization tests showed a significant correlation between the shape of the demagnetization curve and the layer height. While small layer heights are beneficial for sufficient magnetic properties, the remaining main parameters tend to affect the magnetic properties less. A quasi-linear correlation between the layer height and the magnetic properties remanence (J<sub>r</sub>), coercivity (H<sub>cJ</sub>) and maximum energy product ((BH)<sub>max</sub>) was found. Materials science ; Neodymium magnet ; Remanence ; Coercivity ; Magnet ; Microstructure ; Composite material ; Demagnetizing field ; Porosity ; Layer (electronics) ; Magnetization ; Magnetic field ; Condensed matter physics ; Mechanical engineering Magnetic Properties of Alloys ; Additive Manufacturing Materials and Processes ; Metallic Glasses and Amorphous Alloys
Authors: Shuqing Cui | Afsaneh Marandi | Gaëlle Lebourleux | Mireille Thimon | Maxime Bourdon | Chaoben Chen | Maria Inês Severino | Victoria Steggles | Farid Nouar | Christian Serre
Source: Applied Thermal Engineering (article, 2019-07-25)
Field: Chemistry — Inorganic Chemistry

Heat properties of a hydrophilic carboxylate-based MOF for water adsorption applications Materials science ; Adsorption ; Thermal conductivity ; Porous medium ; Differential scanning calorimetry ; Thermodynamics ; Volumetric heat capacity ; Heat transfer ; Chemical engineering ; Thermal energy storage ; Heat capacity ; Heat pump ; Desorption ; Porosity ; Thermal energy ; Calorimeter (particle physics) ; Work (physics) ; Heat transfer coefficient ; Composite material ; Organic chemistry ; Heat exchanger ; Chemistry Metal-Organic Frameworks: Synthesis and Applications ; Adsorption and Cooling Systems ; Phase Change Materials Research
Authors: Emir Kocer | Jeremy K. Mason | Hakan Ertürk
Source: The Journal of Chemical Physics (article, 2019-04-15)
Field: Materials Science — Materials Chemistry

A novel approach to describe chemical environments in high-dimensional neural network potentials A central concern of molecular dynamics simulations is the potential energy surfaces that govern atomic interactions. These hypersurfaces define the potential energy of the system and have generally been calculated using either predefined analytical formulas (classical) or quantum mechanical simulations (ab initio). The former can accurately reproduce only a selection of material properties, whereas the latter is restricted to short simulation times and small systems. Machine learning potentials have recently emerged as a third approach to model atomic interactions, and are purported to offer the accuracy of ab initio simulations with the speed of classical potentials. However, the performance of machine learning potentials depends crucially on the description of a local atomic environment. A set of invariant, orthogonal, and differentiable descriptors for an atomic environment is proposed, implemented in a neural network potential for solid-state silicon, and tested in molecular dynamics simulations. Neural networks using the proposed descriptors are found to outperform ones using the Behler-Parinello and smooth overlap of atomic position descriptors in the literature. Artificial neural network ; Ab initio ; Invariant (physics) ; Computer science ; Statistical physics ; Differentiable function ; Molecular dynamics ; Potential energy ; Position (finance) ; Quantum ; Artificial intelligence ; Set (abstract data type) ; Energy (signal processing) ; Biological system ; Physics ; Quantum mechanics ; Mathematics Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Surface and Thin Film Phenomena
Authors: Safwan Altarazi | Rula M. Allaf | Firas Al-Hindawi
Source: Materials (article, 2019-05-07)
Field: Materials Science — Materials Chemistry

Machine Learning Models for Predicting and Classifying the Tensile Strength of Polymeric Films Fabricated via Different Production Processes In this study, machine learning algorithms (MLA) were employed to predict and classify the tensile strength of polymeric films of different compositions as a function of processing conditions. Two film production techniques were investigated, namely compression molding and extrusion-blow molding. Multi-factor experiments were designed with corresponding parameters. A tensile test was conducted on samples and the tensile strength was recorded. Predictive and classification models from nine MLA were developed. Performance analysis demonstrated the superior predictive ability of the support vector machine (SVM) algorithm, in which a coefficient of determination and mean absolute percentage error of 96% and 4%, respectively were obtained for the extrusion-blow molded films. The classification performance of the MLA was also evaluated, with several algorithms exhibiting excellent performance. Ultimate tensile strength ; Extrusion ; Materials science ; Molding (decorative) ; Support vector machine ; Composite material ; Compression molding ; Tensile testing ; Universal testing machine ; Machine learning ; Algorithm ; Artificial intelligence ; Computer science Machine Learning in Materials Science ; Injection Molding Process and Properties ; Advanced machining processes and optimization