jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Fabian L. M. Bernal | Jonas Sottmann | David S. Wragg | Helmer Fjellvåg | Øystein S. Fjellvåg | Christina Drathen | Wojciech A. Sławiński | Ole Martin Løvvik
Source: Physical Review Materials (article, 2020-05-15)
Field: Chemistry — Inorganic Chemistry

Structural and magnetic characterization of the elusive Jahn-Teller active <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:msub><mml:mi>NaCrF</mml:mi><mml:mn>3</mml:mn></mml:msub></mml:math> We report on the structural and magnetic properties of the elusive Jahn-Teller active compound ${\mathrm{NaCrF}}_{3}$, for first time synthesized in large quantities allowing detailed characterization. The crystal structure of ${\mathrm{NaCrF}}_{3}$ is initially described from a DFT model, which helped serve as the basis for indexing and structure determination confirmed by high-resolution synchrotron x-ray diffraction experiments. ${\mathrm{NaCrF}}_{3}$ adopts the triclinic space group $P\overline{1}$ (isostructural with ${\mathrm{NaCuF}}_{3}$). Magnetometry studies at low temperature show that ${\mathrm{NaCrF}}_{3}$ is a weak antiferromagnet, Weiss temperature $\ensuremath{\theta}=\ensuremath{-}4$ K. The N\'eel temperature is ${T}_{N}=21.3$ K and the paramagnetic moment $\ensuremath{\mu}=4.47\phantom{\rule{0.28em}{0ex}}{\ensuremath{\mu}}_{{}_{B}}$ is in accordance with the theoretical $S=2$. Field-dependent measurements between 2 and 12 K unveil the onset of metamagnetic behavior. Our experiments revealed a weakly canted $A$-type magnetic structure observed by neutron powder diffraction, with a magnetic propagation vector $(1/2,1/2,0)$ and a magnetic moment of $3.51\phantom{\rule{4pt}{0ex}}{\ensuremath{\mu}}_{B}$ at 1.5 K. Our results shed further light on the Jahn-Teller effects and strong correlations as a function of $A$-ion size in the family $A{\mathrm{CrF}}_{3}$. Magnetometer ; Paramagnetism ; Magnetic moment ; Neutron diffraction ; Characterization (materials science) ; Synchrotron ; Triclinic crystal system ; Synchrotron radiation ; Diffraction Inorganic Fluorides and Related Compounds ; Multiferroics and related materials ; Luminescence Properties of Advanced Materials
Authors: Stefano Paganelli | Alessio Angi | Nicolò Pajer | Oreste Piccolo
Source: Catalysts (article, 2020-12-25)
Field: Chemistry — Inorganic Chemistry

A Smart Heterogeneous Catalyst for Efficient, Chemo- and Stereoselective Hydrogenation of 3-Hexyn-1-ol We examine the easy preparation of mono- and bi-metallic heterogeneous catalysts with low Pd and Cu contents on alumina and provide a detailed study of many reaction parameters in the catalyzed selective semihydrogenation of 3-hexyn-1-ol to (Z)-3-hexen-1-ol, a very important fragrance with an herbaceous note. In particular, two different protocols of Pd catalyst preparation, substrate/catalyst molar ratio, the effect of time and temperature, introduction of some additives to the reaction mixture, and the nature of the solvent were investigated. These factors are not independent variables. The results show that it is possible to control the reaction outcome to obtain the target (Z)-alkenol using different experimental conditions. The best result, as an appropriate compromise between conversion and selectivity, may be obtained by working with a very high substrate/catalyst molar ratio (&gt;6000/1), with one type of Pd catalyst, in a short time (about 150 min) at 60 °C. Catalysis ; Molar ratio ; Selectivity ; Substrate (aquarium) ; Stereoselectivity ; Metal ; Solvent ; Reaction conditions ; Heterogeneous catalysis ; Chemistry ; Chemical engineering ; Materials science ; Combinatorial chemistry ; Organic chemistry Asymmetric Hydrogenation and Catalysis ; Catalysis for Biomass Conversion ; Nanomaterials for catalytic reactions
Authors: Graecia Lugito | Selvaraj Nagarajan | Eamor M. Woo
Source: Scientific Reports (article, 2020-07-02)
Field: Materials Science — Biomaterials

Explosive Fibonacci-sequence growth into unusual sector-face morphology in poly(l-lactic acid) crystallized with polymeric diluents Lamellar assembly in unusual sector-face PLLA spherulites from crystallization of poly(L-lactic acid) (PLLA) diluted with amorphous poly(methyl methacrylate) (PMMA). The growth and morphology of the crystalline structures is studied using polarized optical microscopy (POM), atomic-force and scanning electron microscopies (AFM, SEM). Crystals are also analyzed using differential scanning calorimetry (DSC) and small-angle X-ray scattering (SAXS). The two alternate sectored faces differ dramatically in their optical birefringence and top-surface and interior lamellar assembly. By originating from the nucleus center, an explosive fan-like sector of high-birefringence lamellae is packed by fractal growth from an initial single stalk into hundreds of branches upon reaching the periphery, with the number of stalks increasing roughly by the Fibonacci sequence along the radial distance. The exploded pattern resembles a cross-hatch grating structure, and displays a cauliflower-like fractal-branching of optical birefringence blue/orange stripes. This finding suggests that growth with periodic branching is one of the main mechanisms to fill the ever-expanding space in the spherulitic 3D aggregates. Materials science ; Lamellar structure ; Scanning electron microscope ; Small-angle X-ray scattering ; Birefringence ; Differential scanning calorimetry ; Crystallography ; Amorphous solid ; Morphology (biology) ; Optical microscope ; Composite material ; Scattering ; Optics ; Chemistry biodegradable polymer synthesis and properties ; Polymer crystallization and properties ; Polymer Nanocomposites and Properties
Authors: Kristel C. Tjandra | Chelsea R. Forest | Chin Ken Wong | Sheilajen Alcântara | Hannah G. Kelly | Yi Ju | Martina H. Stenzel | Joshua A. McCarroll | Maria Kavallaris | Frank Caruso | Stephen J. Kent | Pall Thordarson
Source: Advanced Healthcare Materials (article, 2020-05-19)
Field: Materials Science — Biomaterials

Modulating the Selectivity and Stealth Properties of Ellipsoidal Polymersomes through a Multivalent Peptide Ligand Display There is a need for improved nanomaterials to simultaneously target cancer cells and avoid non-specific clearance by phagocytes. An ellipsoidal polymersome system is developed with a unique tunable size and shape property. These particles are functionalized with in-house phage-display cell-targeting peptide to target a medulloblastoma cell line in vitro. Particle association with medulloblastoma cells is modulated by tuning the peptide ligand density on the particles. These polymersomes has low levels of association with primary human blood phagocytes. The stealth properties of the polymersomes are further improved by including the peptide targeting moiety, an effect that is likely driven by the peptide protecting the particles from binding blood plasma proteins. Overall, this ellipsoidal polymersome system provides a promising platform to explore tumor cell targeting in vivo. Polymersome ; Peptide ; In vivo ; In vitro ; Biophysics ; Ligand (biochemistry) ; Chemistry ; Nanotechnology ; Materials science ; Cell biology ; Receptor ; Biochemistry ; Biology Nanoparticle-Based Drug Delivery ; RNA Interference and Gene Delivery ; Dendrimers and Hyperbranched Polymers
Authors: Chongchong Qi | Qiusong Chen | Andy Fourie
Source: Minerals (article, 2020-07-26)
Field: Materials Science — Materials Chemistry

Role of Mg Impurity in the Water Adsorption over Low-Index Surfaces of Calcium Silicates: A DFT-D Study Calcium silicates are the most predominant phases in ordinary Portland cement, inside which magnesium is one of the momentous impurities. In this work, using the first-principles density functional theory (DFT), the impurity formation energy (Efor) of Mg substituting Ca was calculated. The adsorption energy (Ead) and configuration of the single water molecule over Mg-doped β-dicalcium silicate (β-C2S) and M3-tricalcium silicate (M3-C3S) surfaces were investigated. The obtained Mg-doped results were compared with the pristine results to reveal the impact of Mg doping. The results show that the Efor was positive for all but one of the calcium silicates surfaces (ranged from −0.02 eV to 1.58 eV), indicating the Mg substituting for Ca was not energetically favorable. The Ead of a water molecule on Mg-doped β-C2S surfaces ranged from –0.598 eV to −1.249 eV with the molecular adsorption being the energetically favorable form. In contrast, the Ead on M3-C3S surfaces ranged from −0.699 eV to −4.008 eV and the more energetically favorable adsorption on M3-C3S surfaces was dissociative adsorption. The influence of Mg doping was important since it affected the reactivity of surface Ca/Mg sites, the Ead of the single water adsorption, as well as the adsorption configuration compared with the water adsorption on pristine surfaces. Adsorption ; Impurity ; Density functional theory ; Doping ; Magnesium ; Molecule ; Calcium silicate ; Inorganic chemistry ; Materials science ; Silicate ; Analytical Chemistry (journal) ; Physical chemistry ; Chemistry ; Computational chemistry ; Organic chemistry ; Metallurgy Magnesium Oxide Properties and Applications ; Concrete and Cement Materials Research ; Layered Double Hydroxides Synthesis and Applications
Authors: Roman Gozdur | P. Gębara | Krzysztof Chwastek
Source: Energies (article, 2020-03-21)
Field: Materials Science — Electronic, Optical and Magnetic Materials

A Study of Temperature-Dependent Hysteresis Curves for a Magnetocaloric Composite Based on La(Fe, Mn, Si)13-H Type Alloys In the present paper, the effect of temperature on the shape of magnetic hysteresis loops for a magnetocaloric composite core was studied. The composite core, based on La(Fe, Mn, Si)13-H, was set up using three component disks with different Curie temperatures. The magnetic properties of the components and the outcome composite core were determined using a self-developed measurement setup. For the description of hysteresis loops, the phenomenological T(x) model was used. The presented methodology might be useful for the designers of magnetic active regenerators. Magnetic refrigeration ; Composite number ; Hysteresis ; Materials science ; Curie temperature ; Magnetic hysteresis ; Core (optical fiber) ; Phenomenological model ; Curie ; Condensed matter physics ; Component (thermodynamics) ; Thermodynamics ; Magnetic field ; Ferromagnetism ; Magnetization ; Composite material ; Physics Magnetic and transport properties of perovskites and related materials ; Magnetic Properties of Alloys ; Magnetic Properties and Applications
Authors: Boxue Du | Tingting Ma | Jingang Su | Meng Tian | Tao Han | Xiaoxiao Kong
Source: IEEE Access (article, 2020-01-01)
Field: Materials Science — Materials Chemistry

Effects of Temperature Gradient on Electrical Tree Growth and Partial Discharge in Silicone Rubber Under AC Voltage The Joule heat produced by the current in conductor decreases in the radial direction, producing a temperature gradient in the insulation. This existing temperature gradient can affect the electrical tree phenomenon in insulation materials because of the temperature dependence of insulation material, charge migration and tree growth. Different temperature gradients were obtained by controlling the temperature of high voltage (HV) and ground electrode (GD) sides. The electrical tree was recorded by a microscope, while the partial discharge (PD) was observed simultaneously. For better understanding the effects of the temperature gradient on electrical tree inception, the trap distribution in silicone rubber (SIR) with different temperature was analyzed by isothermal discharge current (IDC) method. With the fixed temperature in HV or GD side, it was found that the electrical tree inception voltage decreased linearly with increasing temperature in either side. The IDC results indicate that the increasing temperature contributes to the de-trapping of charges and lowers the inception voltage. Besides, the electrical tree length and accumulated damage gradually increase with the temperature gradient. The observed PD amplitude and quantity also increase with the temperature gradient, which facilitates the growth of electrical trees. These results indicate that the tree growth is promoted with an increase in the temperature gradient of the cable insulation, posing a threat to the safety of the cable system. Temperature gradient ; Materials science ; Partial discharge ; Electrical treeing ; Isothermal process ; Silicone rubber ; Composite material ; Joule heating ; Voltage ; Electrode ; Conductor ; Electrical conductor ; Electrical engineering ; Thermodynamics ; Chemistry ; Meteorology ; Physics High voltage insulation and dielectric phenomena ; Power Transformer Diagnostics and Insulation ; Electrical Fault Detection and Protection
Authors: Kho Wan You | Senthil Kumar Arumugasamy
Source: Journal of the Taiwan Institute of Chemical Engineers (article, 2020-11-01)
Field: Materials Science — Biomaterials

Deep learning techniques for polycaprolactone molecular weight prediction via enzymatic polymerization process Polycaprolactone ; Polymerization ; Process (computing) ; Chemistry ; Computer science ; Materials science ; Polymer ; Organic chemistry biodegradable polymer synthesis and properties ; 3D Printing in Biomedical Research ; Enzyme Catalysis and Immobilization
Authors: Mingjian Wen | Samuel M. Blau | Evan Walter Clark Spotte‐Smith | Shyam Dwaraknath | Kristin A. Persson
Source: ChemRxiv (preprint, 2020-09-21)
Field: Materials Science — Materials Chemistry

BonDNet: A Graph Neural Network for the Prediction of Bond Dissociation Energies for Charged Molecules A broad collection of technologies, including e.g. drug metabolism, biofuel combustion, photochemical decontamination of water, and interfacial passivation in energy production/storage systems rely on chemical processes that involve bond-breaking molecular reactions. In this context, a fundamental thermodynamic property of interest is the bond dissociation energy (BDE) which measures the strength of a chemical bond. Fast and accurate prediction of BDEs for arbitrary molecules would lay the groundwork for data-driven projections of complex reaction cascades and hence a deeper understanding of these critical chemical processes and, ultimately, how to reverse design them. In this paper, we propose a chemically inspired graph neural network machine learning model, BonDNet, for the rapid and accurate prediction of BDEs. BonDNet maps the difference between the molecular representations of the reactants and products to the reaction BDE. Because of the use of this difference representation and the introduction of global features, including molecular charge, it is the first machine learning model capable of predicting both homolytic and heterolytic BDEs for molecules of any charge. To test the model, we have constructed a dataset of both homolytic and heterolytic BDEs for neutral and charged (1 and +1) molecules. BonDNet achieves a mean absolute error (MAE) of 0.022 eV for unseen test data, significantly below chemical accuracy (0.043 eV). Besides the ability to handle complex bond dissociation reactions that no previous model could con- sider, BonDNet distinguishes itself even in only predicting homolytic BDEs for neutral molecules; it achieves an MAE of 0.020 eV on the PubChem BDE dataset, a 20% improvement over the previous best performing model. We gain additional insight into the model’s predictions by analyzing the patterns in the features representing the molecules and the bond dissociation reactions, which are qualitatively consistent with chemical rules and intuition. BonDNet is just one application of our general approach to representing and learning chemical reactivity, and it could be easily extended to the prediction of other reaction properties in the future. Heterolysis ; Homolysis ; Chemistry ; Bond-dissociation energy ; Molecule ; Dissociation (chemistry) ; Computational chemistry ; Artificial neural network ; Computer science ; Artificial intelligence ; Biological system ; Radical ; Organic chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Chemistry and Chemical Engineering
Authors: Issei Nakamura
Source: New Journal of Physics (article, 2020-01-01)
Field: Materials Science — Materials Chemistry

Phase diagrams of polymer-containing liquid mixtures with a theory-embedded neural network Abstract We develop a deep neural network (DNN) that accounts for the phase behaviors of polymer-containing liquid mixtures. The key component in the DNN consists of a theory-embedded layer that captures the characteristic features of the phase behavior via coarse-grained mean-field theory and scaling laws and substantially enhances the accuracy of the DNN. Moreover, this layer enables us to reduce the size of the DNN for the phase diagrams of the mixtures. This study also presents the predictive power of the DNN for the phase behaviors of polymer solutions and salt-free and salt-doped diblock copolymer melts. Physics ; Artificial neural network ; Phase diagram ; Phase (matter) ; Polymer ; Scaling ; Component (thermodynamics) ; Layer (electronics) ; Polymer network ; Field (mathematics) ; Copolymer ; Biological system ; Statistical physics ; Nanotechnology ; Artificial intelligence ; Thermodynamics ; Computer science ; Materials science ; Nuclear magnetic resonance ; Quantum mechanics ; Geometry Machine Learning in Materials Science ; Block Copolymer Self-Assembly ; Theoretical and Computational Physics
Authors: Ahmed Aziz Ezzat | Mostafa Bedewy
Source: The Journal of Physical Chemistry C (article, 2020-12-09)
Field: Materials Science — Materials Chemistry

Machine Learning for Revealing Spatial Dependence among Nanoparticles: Understanding Catalyst Film Dewetting via Gibbs Point Process Models We combine in situ environmental transmission electron microscopy (E-TEM) with automated image processing and statistical machine learning to uniquely formulate interpretable mathematical models and accurate simulation tools for complex nanoscale phenomena involving coupled physical and chemical processes and interactions that are otherwise hard to model. In particular, there is a need for a better understanding, characterization, and prediction of the proximity effects among dense populations of metal nanocatalysts as they form and evolve over time. Here, we leverage point process theory, a branch of statistical machine learning, to “learn” the spatial dependencies among ensembles of adjacent alumina-supported iron nanoparticles from a time sequence of E-TEM images. We construct a set of point process models to make statistical inferences about the nature of spatial dependencies that govern the rapid formation, or “popping” of nanoparticles during thin film dewetting, concomitant with metal reduction in the presence of acetylene at 750 °C. We show that nanoparticles exhibit strong dispersion behavior, i.e., new nanoparticles pop in dispersed locations at a predictable distance from their existing territorial neighbors. We also show that Gibbs point processes adequately describe the pairwise interactions underlying such time-dependent spatial variations. Further, we build on our machine-learned models to develop a computational simulation tool capable of producing accurate spatiotemporal simulations of nanoparticle formation at finer time resolutions and larger spatial domains than those of experimental observations. This is a much needed capability to overcome current limitations in computational methods supporting the design, analysis, and control of the collective behavior of nanocatalyst populations. Dewetting ; Nanoparticle ; Materials science ; Process (computing) ; Point (geometry) ; Nanotechnology ; Computer science ; Thin film ; Mathematics ; Geometry Block Copolymer Self-Assembly ; nanoparticles nucleation surface interactions ; Theoretical and Computational Physics
Authors: Nishan M. Senanayake | Jennifer Carter
Source: Integrating materials and manufacturing innovation (article, 2020-12-01)
Field: Materials Science — Metals and Alloys

Computer Vision Approaches for Segmentation of Nanoscale Precipitates in Nickel-Based Superalloy IN718 Abstract Extracting accurate volume fraction and size measurements of γ ″ and γ ′ precipitates in iron-based superalloys from micrographs is challenging and conventionally involves manual image processing due to their smaller size, and similar crystal structures and chemistries. The co-precipitation of composite particles further complicates automated segmentation. In this work, different types of traditional machine learning approaches and a convolutional neural network (CNN) were compared to a non-machine learning approach, for the segmentation of the composite particles of γ ″ and γ ′ precipitates. The objective was to optimize metrics of segmentation accuracy and the required computational resources. The data set contains 47 experimentally generated scanning electron micrographs of IN718 alloy samples, computationally increased to 188 images (900 × 900 px). All algorithms are containerized using singularity, publicly available, and can be modified without dependencies. The CNN and the random forest models achieve 95% and 94% accuracy, respectively, on the test images with better computational efficiency than the non-machine learning algorithm. The CNN tested accurately over a range of imaging conditions. Superalloy ; Convolutional neural network ; Artificial intelligence ; Segmentation ; Materials science ; Computer science ; Artificial neural network ; Pattern recognition (psychology) ; Alloy ; Metallurgy Hydrogen embrittlement and corrosion behaviors in metals ; Nuclear Physics and Applications ; Additive Manufacturing Materials and Processes
Authors: Wenhong Yang | Timothy Tizhe Fidelis | Wen‐Hua Sun
Source: Journal of Computational Chemistry (article, 2020-02-05)
Field: Materials Science — Materials Chemistry

Prediction of catalytic activities of bis(imino)pyridine metal complexes by machine learning This work demonstrates the potential of machine learning (ML) method to predict catalytic activity of transition metal complex precatalyst toward ethylene polymerization. For this purpose, 294 complexes and 15 molecular descriptors were selected to build the artificial neural network (ANN) model. The catalytic activity can be well predicted by the obtained ANN model, which was further validated by external complexes. Boruta algorithm was employed to explicitly decipher the importance of descriptors, illustrating the conjugated bond structure, and bulky substitutions are favorable for catalytic activity. The present work indicates that ML could give useful guidance for the new design of homogenous polyolefin catalyst. Pyridine ; Catalysis ; Metal ; Chemistry ; Medicinal chemistry ; Organic chemistry Machine Learning in Materials Science ; Organometallic Complex Synthesis and Catalysis ; Asymmetric Hydrogenation and Catalysis
Authors: Xinyi Yang | Ziyi Luo | Zihan Huang | Yihang Zhao | Zhichang Xue | Yingxue Wang | Wei‐Di Liu | Siliang Liu | Hong Zhang | Kui Xu | Shuming Dou | Jie Xu | Wenbin Hu | Yida Deng
Source: Frontiers in Energy Research (article, 2020-07-31)
Field: Materials Science — Materials Chemistry

Development Status and Prospects of Artificial Intelligence in the Field of Energy Conversion Materials With the characteristics of high-speed calculation and high-accuracy prediction, artificial intelligence (AI) which also known as machine intelligence, including deep learning, machine learning, etc., have shown great advantages in cross-field applications. In material science field, AI can be used to discover new materials and predict corresponding critical properties. At present, AI has been used in the exploitation of energy conversion materials and other energy-related materials. In this review, we summary the current achievements of AI applications in energy conversions, analyze the advantages and disadvantages of AI techniques in material researches and point out future development prospects. Field (mathematics) ; Artificial intelligence ; Applications of artificial intelligence ; Computer science ; Energy transformation ; Energy (signal processing) ; Machine learning ; Physics ; Mathematics Machine Learning in Materials Science