jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Tongqi Wen | Cai‐Zhuang Wang | M. J. Kramer | Yang Sun | Beilin Ye | Haidi Wang | Xue‐Yuan Liu | Chao Zhang | Feng Zhang | Kai‐Ming Ho | Nan Wang
Source: Physical review. B./Physical review. B (article, 2019-11-04)
Field: Materials Science — Materials Chemistry

Development of a deep machine learning interatomic potential for metalloid-containing Pd-Si compounds Interatomic potentials based on neural-network machine learning (ML) approach to address the long-standing challenge of accuracy versus efficiency in molecular-dynamics simulations have recently attracted a great deal of interest. Here, utilizing Pd-Si system as a prototype, we extend the development of neural-network ML potentials to compounds exhibiting various types of bonding characteristics. The ML potential is trained by fitting to the energies and forces of both liquid and crystal structures first-principles calculations based on density-functional theory (DFT). We show that the generated ML potential captures the structural features and motifs in $\mathrm{P}{\mathrm{d}}_{82}\mathrm{S}{\mathrm{i}}_{18}$ and $\mathrm{P}{\mathrm{d}}_{75}\mathrm{S}{\mathrm{i}}_{25}$ liquids more accurately than the existing interatomic potential based on embedded-atom method (EAM). The ML potential also describes the solid-liquid interface of these systems very well. Moreover, while the existing EAM potential fails to describe the relative energies of various crystalline structures and predict wrong ground-state structures at $\mathrm{P}{\mathrm{d}}_{3}\mathrm{Si}$ and $\mathrm{P}{\mathrm{d}}_{9}\mathrm{S}{\mathrm{i}}_{2}$ composition, the developed ML potential predicts correctly the ground-state structures from genetic algorithm search. The efficient ML potential with DFT accuracy from our study will provide a promising scheme for accurate atomistic simulations of structures and dynamics of complex Pd-Si system. Interatomic potential ; Atom (system on chip) ; Ground state ; Molecular dynamics ; Physics ; Artificial neural network ; Density functional theory ; Machine learning ; Crystallography ; Algorithm ; Materials science ; Atomic physics ; Computer science ; Chemistry ; Quantum mechanics Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; nanoparticles nucleation surface interactions
Authors: V. Dhayalan | Santosh C. Gadekar | Zayed Alassad | Anat Milo
Source: Nature Chemistry (article, 2019-05-13)
Field: Chemistry — Organic Chemistry

Unravelling mechanistic features of organocatalysis with in situ modifications at the secondary sphere Chemistry ; Organocatalysis ; Reactivity (psychology) ; Combinatorial chemistry ; Enantioselective synthesis ; Catalysis ; Selectivity ; Coordination sphere ; Organic chemistry ; Molecule Synthetic Organic Chemistry Methods ; Organometallic Complex Synthesis and Catalysis ; Asymmetric Hydrogenation and Catalysis
Authors: Yi Zhou | Jiang Wu | Shuguang Chen | GuanHua Chen
Source: The Journal of Physical Chemistry Letters (article, 2019-11-06)
Field: Materials Science — Materials Chemistry

Toward the Exact Exchange–Correlation Potential: A Three-Dimensional Convolutional Neural Network Construct A deep neural network is constructed to yield in principle exact exchange-correlation potential. It requires merely the electron densities of small molecules and ions and yet can determine the exchange-correlation potentials of large molecules. We train and validate the neural network based on the data for H<sub>2</sub> and HeH<sup>+</sup> and subsequently determine the ground-state electron density of stretched HeH<sup>+</sup>, linear H<sub>3</sub><sup>+</sup>, and H-He-He-H<sup>2+</sup>. Moreover, the deep neural network is proven to model the van der Waals interaction by being trained and validated on a data set containing He<sub>2</sub>. Comparisons to B3LYP are given to illustrate the accuracy and transferability of the neural network. Artificial neural network ; Convolutional neural network ; Transferability ; van der Waals force ; Set (abstract data type) ; Correlation ; Data set ; Construct (python library) ; Electron density ; Ground state ; Computer science ; Statistical physics ; Molecule ; Electron ; Artificial intelligence ; Physics ; Atomic physics ; Mathematics ; Machine learning ; Quantum mechanics Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Spectroscopy and Quantum Chemical Studies
Authors: Sajad Kiani | Saskia M. van Ruth | L.W.D. van Raamsdonk | Saeid Minaei
Source: LWT (article, 2019-01-25)
Field: Chemistry — Analytical Chemistry

Hyperspectral imaging as a novel system for the authentication of spices: A nutmeg case study Nutmeg ; Adulterant ; Hyperspectral imaging ; Authentication (law) ; Principal component analysis ; Artificial intelligence ; Myristica fragrans ; Pattern recognition (psychology) ; Partial least squares regression ; Mathematics ; Computer science ; Food science ; Chromatography ; Chemistry ; Machine learning ; Traditional medicine ; Medicine Spectroscopy and Chemometric Analyses ; Phytochemicals and Antioxidant Activities ; Essential Oils and Antimicrobial Activity
Authors: Menad Nait Amar | Mohammadhadi Shateri | Abdolhossein Hemmati‐Sarapardeh | Alireza Alamatsaz
Source: Journal of Petroleum Science and Engineering (article, 2019-08-26)
Field: Chemistry — Analytical Chemistry

Modeling oil-brine interfacial tension at high pressure and high salinity conditions Brine ; Computer science ; Crude oil ; Outlier ; Support vector machine ; Boosting (machine learning) ; Estimator ; Decision tree ; Petroleum engineering ; Machine learning ; Artificial intelligence ; Mathematics ; Chemistry ; Geology ; Statistics Petroleum Processing and Analysis ; Enhanced Oil Recovery Techniques ; Hydrocarbon exploration and reservoir analysis
Authors: Hugo Santos Silva | Ahmad Alfarra | Germain Salvato Vallverdu | Didier Bégué | Brice Bouyssière | Isabelle Baraille
Source: Petroleum Science (article, 2019-05-03)
Field: Chemistry — Analytical Chemistry

Asphaltene aggregation studied by molecular dynamics simulations: role of the molecular architecture and solvents on the supramolecular or colloidal behavior Asphaltene aggregation is a subject under vivid discussion: There are several parameters one needs to determine before its behavior can be mastered and better target solutions can be tailored. The nature of asphaltene aggregation (colloidal or supramolecular) and the role of solvents and their mixtures are among the least understood parameters in asphaltene science. This paper addresses molecular dynamic simulations to correlate the aggregation properties of asphaltenes, their molecular structure and the concentration of these solvents. We show that the formation of the nanoaggregate depends, primarily, on the size of the conjugated core and on the eventual presence of polar groups capable of forming H-bonds. Heteroatoms on the conjugated core do not change their shape or type of aggregation but may induce stronger $$\uppi - \uppi$$ interactions. The macroaggregation formation depends upon the length of the lateral chains of asphaltenes and also on the presence of polar groups at its end. Moreover, n-heptane and water may interact selectively with asphaltenes in function of their molecular architecture. Given this fact and the aggregation behavior observed, we advocate toward the assumption that a colloidal behavior of asphaltenes might be a particular case of a more general model, based on a supramolecular description. Asphaltene ; Supramolecular chemistry ; Heteroatom ; Colloid ; Chemistry ; Molecular dynamics ; Chemical physics ; Conjugated system ; Polar ; Heptane ; Molecule ; Polymer ; Organic chemistry ; Computational chemistry ; Ring (chemistry) Petroleum Processing and Analysis ; Hydrocarbon exploration and reservoir analysis ; Enhanced Oil Recovery Techniques
Authors: Gitishree Das | Jayanta Kumar Patra | Nagaraj Basavegowda | Chethala N. Vishnuprasad | Han‐Seung Shin
Source: International Journal of Nanomedicine (article, 2019-07-01)
Field: Materials Science — Materials Chemistry

&lt;p&gt;Comparative study on antidiabetic, cytotoxicity, antioxidant and antibacterial properties of biosynthesized silver nanoparticles using outer peels of two varieties of &lt;em&gt;Ipomoea batatas&lt;/em&gt; (L.) Lam&lt;/p&gt; Overall, the results concluded that the Ib2-AgNPs exhibited relatively higher functional activity than Ib1-AgNPs, which might be credited to the greater abundance of bioactive compounds existing in Ib2 extract that acted as reducing as well as capping agents in the synthesis of Ib2-AgNPs. Overall, the current study highlights a novel cost-effective and eco-friendly AgNPs synthesis using food waste peels with biocompatibility and could be potentially utilized in biomedical and pharmaceutical industries. Silver nanoparticle ; Cytotoxicity ; Chemistry ; Nuclear chemistry ; Antioxidant ; Ipomoea ; MTT assay ; Fourier transform infrared spectroscopy ; Food science ; Nanoparticle ; Biochemistry ; Nanotechnology ; Materials science ; Apoptosis ; Botany ; In vitro ; Biology ; Chemical engineering Nanoparticles: synthesis and applications ; Moringa oleifera research and applications ; Medicinal Plants and Neuroprotection
Authors: Lei Lin | Yong He | Zhitao Xiao | Ke Zhao | Tao Dong | Pengcheng Nie
Source: Applied Sciences (article, 2019-04-22)
Field: Chemistry — Analytical Chemistry

Rapid-Detection Sensor for Rice Grain Moisture Based on NIR Spectroscopy Rice grain moisture has a great impact on th production and storage storage quality of rice. The main objective of this study was to design and develop a rapid-detection sensor for rice grain moisture based on the Near-infrared spectroscopy (NIR) characteristic band, aiming to realize its accurate and on-line measurement. In this paper, the NIR spectral information of grain samples with different moisture content was obtained using a portable NIR spectrometer. Then, the partial least squares (PLS) and competitive adaptive reweighted squares (CARS) were applied to model and analyze the spectral data to find the rice grain moisture NIR spectroscopy. As a result, the 1450 nm band was sensitive to the rice grain moisture and a rapid-detection sensor was developed with a 1450 nm light emitting diode (LED) light source, InGaAs photodiode, lens and filter, whose basic principle is to establish the relationship between the rice grain moisture and the measured voltage signal. To evaluate the sensor performance, rice grain samples with 13–30% moisture content were detected, the coefficient of determination R2 was 0.936, and the sum of squares for error (SSE) was 23.44. It is concluded that this study provides a spectroscopic measuring method, as well as developing an effective and accurate sensor for the rapid determination of rice grain moisture, which is of great significance for monitoring the quality of rice grain during its production, transportation and storage process. Moisture ; Near-infrared spectroscopy ; Partial least squares regression ; Spectrometer ; Grain quality ; Materials science ; Spectroscopy ; Water content ; Remote sensing ; Environmental science ; Agronomy ; Mathematics ; Optics ; Composite material ; Engineering Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Water Quality Monitoring and Analysis
Authors: Siddharth Sundararaman | Liping Huang | Simona Ispas | Walter Kob
Source: The Journal of Chemical Physics (article, 2019-04-19)
Field: Materials Science — Ceramics and Composites

New interaction potentials for alkali and alkaline-earth aluminosilicate glasses We apply a recently developed optimization scheme to obtain effective potentials for alkali and alkaline-earth aluminosilicate glasses that contain lithium, sodium, potassium, or calcium as modifiers. As input data for the optimization, we used the radial distribution functions of the liquid at high temperature generated by means of ab initio molecular dynamics simulations and density and elastic modulus of glass at room temperature from experiments. The new interaction potentials are able to reproduce reliably the structure and various mechanical and vibrational properties over a wide range of compositions for binary silicates. We have tested these potentials for various ternary systems and find that they are transferable and can be mixed, thus allowing us to reproduce and predict the structure and properties of multicomponent glasses. Aluminosilicate ; Alkali metal ; Ternary operation ; Molecular dynamics ; Ternary numeral system ; Elastic modulus ; Ab initio ; Binary number ; Work (physics) ; Range (aeronautics) Glass properties and applications ; Material Dynamics and Properties ; High-pressure geophysics and materials
Authors: Valerio Vitale | Giovanni Pizzi | Antimo Marrazzo | Jonathan R. Yates | Nicola Marzari | Arash A. Mostofi
Source: Open MIND (article, 2019-08-30)
Field: Materials Science — Materials Chemistry

Automated high-throughput Wannierisation Abstract: Maximally-localised Wannier functions (MLWFs) are routinely used to compute from first-principles advanced materials properties that require very dense Brillouin zone integration and to build accurate tight-binding models for scale-bridging simulations. At the same time, high-throughput (HT) computational materials design is an emergent field that promises to accelerate reliable and cost-effective design and optimisation of new materials with target properties. The use of MLWFs in HT workflows has been hampered by the fact that generating MLWFs automatically and robustly without any user intervention and for arbitrary materials is, in general, very challenging. We address this problem directly by proposing a procedure for automatically generating MLWFs for HT frameworks. Our approach is based on the selected columns of the density matrix method and we present the details of its implementation in an AiiDA workflow. We apply our approach to a dataset of 200 bulk crystalline materials that span a wide structural and chemical space. We assess the quality of our MLWFs in terms of the accuracy of the band-structure interpolation that they provide as compared to the band-structure obtained via full first-principles calculations. Finally, we provide a downloadable virtual machine that can be used to reproduce the results of this paper, including all first-principles and atomistic simulations as well as the computational workflows. Workflow ; Computer science ; Bridging (networking) ; Computational science ; Interpolation (computer graphics) ; Throughput ; Computer engineering ; Algorithm ; Artificial intelligence Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides ; Catalysis and Oxidation Reactions
Authors: Katarzyna Jedynak | Beata Szczepanik | Nina Rędzia | P. M. Słomkiewicz | A. Kolbus | Paweł Rogala
Source: Water (article, 2019-05-25)
Field: Chemistry — Analytical Chemistry

Ordered Mesoporous Carbons for Adsorption of Paracetamol and Non-Steroidal Anti-Inflammatory Drugs: Ibuprofen and Naproxen from Aqueous Solutions The adsorption of paracetamol and non-steroidal anti-inflammatory drugs (ibuprofen and naproxen) on ordered mesoporous carbons (OMC) and, for comparison, on commercial activated carbon, were investigated in this work. OMC adsorbents were obtained by the soft-templating method and were characterized by low-temperature nitrogen adsorption and scanning electron microscopy (SEM). The effects of contact time and initial concentration of organic adsorbates on the adsorption were studied. The contact time to reach equilibrium for maximum adsorption was 360 min for all the studied adsorbates. The adsorption mechanism was found to fit pseudo-second-order and intra particle-diffusion models. Freundlich, Langmuir and Langmuir-Freundlich isotherm models were used to analyze equilibrium adsorption data. Based on the obtained experimental data, the adsorption isotherm in the applied concentration range for all the studied adsorbates was well represented by the Freundlich-Langmuir model. The adsorption ability of ordered mesoporous carbon materials was much higher for paracetamol and naproxen in comparison to commercial activated carbon. The removal efficiency for ibuprofen was significantly lower than for other studied pharmaceuticals and comparable for all adsorbents. Theoretical calculations made it possible to obtain optimized chemical structures of (S)-naproxen, ibuprofen, and paracetamol molecules. Knowledge of charge distributions of these adsorbate molecules can be helpful to explain why paracetamol and naproxen can react more strongly with the surface of adsorbents with a large numbers of acidic groups compared to ibuprofen facilitating more efficient adsorption of these pharmaceuticals on ordered mesoporous carbons. Adsorption ; Naproxen ; Freundlich equation ; Langmuir ; Mesoporous material ; Aqueous solution ; Ibuprofen ; Chemistry ; Langmuir adsorption model ; Activated carbon ; Chemical engineering ; Materials science ; Inorganic chemistry ; Organic chemistry ; Catalysis Analytical chemistry methods development ; Analytical Chemistry and Chromatography ; Electrochemical sensors and biosensors
Authors: Wen Zhang | Xuechuan Wang | Jianjun Wang | Lele Zhang
Source: International Journal of Biological Macromolecules (article, 2019-08-17)
Field: Materials Science — Biomaterials

Drugs adsorption and release behavior of collagen/bacterial cellulose porous microspheres Adsorption ; Biocompatibility ; Kinetics ; Porosity ; Chemical engineering ; Bacterial cellulose ; Diffusion ; Chemistry ; Membrane ; Microsphere ; Cellulose ; Drug delivery ; Porous medium ; Chromatography ; Materials science ; Organic chemistry ; Thermodynamics ; Biochemistry Advanced Cellulose Research Studies ; Electrospun Nanofibers in Biomedical Applications ; Hydrogels: synthesis, properties, applications
Authors: Mubarak B. Dambatta | Kurt Polidano | Alexander D. Northey | Jonathan M. J. Williams | Louis C. Morrill
Source: ChemSusChem (article, 2019-04-08)
Field: Chemistry — Inorganic Chemistry

Iron‐Catalyzed Borrowing Hydrogen <i>C</i>‐Alkylation of Oxindoles with Alcohols A general and efficient iron-catalyzed C-alkylation of oxindoles has been developed. This borrowing hydrogen approach employing a (cyclopentadienone)iron carbonyl complex (2 mol %) exhibited a broad reaction scope, allowing benzylic and simple primary and secondary aliphatic alcohols to be employed as alkylating agents. A variety of oxindoles underwent selective mono-C3-alkylation in good-to-excellent isolated yields (28 examples, 50-92 % yield, 79 % average yield). Alkylation ; Yield (engineering) ; Catalysis ; Chemistry ; Hydrogen ; Primary (astronomy) ; Organic chemistry ; Reaction conditions ; Combinatorial chemistry ; Materials science Asymmetric Hydrogenation and Catalysis ; Chemical Synthesis and Analysis ; Carbon dioxide utilization in catalysis
Authors: Angelina Angelova | Markus Drechsler | Vasil M. Garamus | Borislav Angelov
Source: ChemNanoMat (article, 2019-09-17)
Field: Materials Science — Biomaterials

Pep‐Lipid Cubosomes and Vesicles Compartmentalized by Micelles from Self‐Assembly of Multiple Neuroprotective Building Blocks Including a Large Peptide Hormone PACAP‐DHA Abstract Structural control over design and formation of self‐assembled nanomaterials for neuroprotection and neuroregeneration is crucial for their application in nanomedicine. Here a synthetic construct of the pituitary adenylate cyclase‐activating polypeptide (PACAP38) coupled to a docosahexaenoic acid (DHA: an ω‐3 polyunsaturated fatty acid (PUFA)) is designed towards the creation of compartmentalized liquid crystalline assemblies of neuroprotective compounds. The hormone PACAP38 is a ligand of the class B PAC1 G‐protein‐coupled receptor (GPCR), whereas DHA is a lipid trophic factor. The lipidated peptide PACAP‐DHA is co‐assembled into hierarchical nanostructures elaborated from hybrid vesicle‐micelle reservoirs as well into PEGylated cubosomes composed of multiple neuroprotective building blocks. The resulting nanostructures are determined by synchrotron small‐angle X‐ray scattering (BioSAXS) and cryogenic transmission electron microscopy (cryo‐TEM). Multicompartment topologies are obtained in a two‐fold approach: (i) intriguing compartmentalized vesicles, which embed pep‐lipid micelles forming nanopatterns, and (ii) multidomain pep‐lipid cubosomes. Both kinds of topologies are favorable for sustained‐release applications in combination therapies of neurodegeneration. The organizational complexity of the scaffolds involving the lipidated high‐molecular weight peptide hormone is beyond the one that has been reached with small lipid‐like peptide surfactants. Neuroprotection ; Vesicle ; Peptide ; Chemistry ; Micelle ; Biophysics ; Self-assembly ; Nanotechnology ; Biochemistry ; Materials science ; Biology ; Membrane ; Organic chemistry ; Neuroscience Supramolecular Self-Assembly in Materials ; Neuropeptides and Animal Physiology ; Lipid Membrane Structure and Behavior
Authors: Patrick N. Charron | Tess Braddish | Rachael A. Floreani
Source: Journal of the mechanical behavior of biomedical materials/Journal of mechanical behavior of biomedical materials (article, 2019-01-07)
Field: Materials Science — Biomaterials

PVA-gelatin hydrogels formed using combined theta-gel and cryo-gel fabrication techniques Gelatin ; Self-healing hydrogels ; Materials science ; Toughness ; Vinyl alcohol ; Polymer ; Chemical engineering ; Polyvinyl alcohol ; Composite material ; Elastic modulus ; Dynamic mechanical analysis ; PEG ratio ; Ethylene glycol ; Polymer chemistry ; Chemistry ; Organic chemistry Electrospun Nanofibers in Biomedical Applications ; Hydrogels: synthesis, properties, applications ; 3D Printing in Biomedical Research
Authors: Sungsik Lee | Avik Halder | G. Ferguson | Sönke Seifert | Randall E. Winans | Detre Teschner | Robert Schlögl | Vasiliki Papaefthimiou | Jeffrey Greeley | Larry A. Curtiss | Štefan Vajda
Source: Nature Communications (article, 2019-02-27)
Field: Materials Science — Materials Chemistry

Subnanometer cobalt oxide clusters as selective low temperature oxidative dehydrogenation catalysts The discovery of more efficient, economical, and selective catalysts for oxidative dehydrogenation is of immense economic importance. However, the temperatures required for this reaction are typically high, often exceeding 400 °C. Herein, we report the discovery of subnanometer sized cobalt oxide clusters for oxidative dehydrogenation of cyclohexane that are active at lower temperatures than reported catalysts, while they can also eliminate the combustion channel. These results found for the two cluster sizes suggest other subnanometer size (CoO)<sub>x</sub> clusters will also be active at low temperatures. The high activity of the cobalt clusters can be understood on the basis of density functional studies that reveal highly active under-coordinated cobalt atoms in the clusters and show that the oxidized nature of the clusters substantially decreases the binding energy of the cyclohexene species which desorb from the cluster at low temperature. Dehydrogenation ; Catalysis ; Cobalt ; Cobalt oxide ; Oxidative phosphorylation ; Chemistry ; Oxide ; Materials science ; Nanotechnology ; Inorganic chemistry ; Biochemistry ; Organic chemistry Catalytic Processes in Materials Science ; Catalysis and Oxidation Reactions ; Machine Learning in Materials Science