jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Y. Eren Suyolcu | G. Christiani | Peter A. van Aken | Г. Логвенов
Source: Journal of Superconductivity and Novel Magnetism (article, 2019-10-21)
Field: Materials Science — Materials Chemistry

Design of Complex Oxide Interfaces by Oxide Molecular Beam Epitaxy Abstract Complex oxides provide a versatile playground for many phenomena and possible applications, for instance, high-temperature superconductivity, magnetism, ferroelectricity, metal-to-insulator transition, colossal magnetoresistance, and piezoelectricity. The origin of these phenomena is the competition between different degrees of freedom such as charge, orbital, and spin, which are interrelated with the crystal structure, the oxygen stoichiometry, and the doping dependence. Recent developments not only in the epitaxial growth technologies, such as reactive molecular beam epitaxy, but also in the characterization techniques, as aberration-corrected scanning transmission electron microscopy with spectroscopic tools, allow synthesizing and identifying epitaxial systems at the atomic scale. Combination of different oxide layers opens access to interface physics and leads to engineering interface properties, where the degrees of freedom can be artificially modified. In this review, we present different homo- and hetero-epitaxial interfaces with extraordinary structural quality and different functionalities, including high-temperature superconductivity, thermoelectricity, and magnetism. Molecular beam epitaxy ; Materials science ; Magnetism ; Epitaxy ; Superconductivity ; Condensed matter physics ; Scanning transmission electron microscopy ; Ferroelectricity ; Oxide ; Nanotechnology ; Doping ; Characterization (materials science) ; Optoelectronics ; Transmission electron microscopy ; Physics Electronic and Structural Properties of Oxides ; Magnetic and transport properties of perovskites and related materials ; Advanced Condensed Matter Physics
Authors: Wouter J. Geelhoed | Koen E.A. van der Bogt | T.C. Rothuizen | Febriyani Damanik | Jaap F. Hamming | Carlos Mota | M.S. van Agen | Hetty C. de Boer | Mauricio Tobón Restrepo | Boris Hinz | Ankur Kislaya | Christian Poelma | Anton Jan van Zonneveld | Ton J. Rabelink | Lorenzo Moroni | Joris I. Rotmans
Source: Biomaterials (article, 2019-10-29)
Field: Materials Science — Biomaterials

A novel method for engineering autologous non-thrombogenic in situ tissue-engineered blood vessels for arteriovenous grafting Ex vivo ; Tissue engineering ; Elastin ; Jugular vein ; Biomedical engineering ; Decellularization ; In vivo ; Transplantation ; Blood vessel ; Lumen (anatomy) ; Medicine ; Thrombogenicity ; Artery ; Materials science ; Surgery ; Thrombosis ; Pathology ; Biology ; Internal medicine Electrospun Nanofibers in Biomedical Applications ; Tissue Engineering and Regenerative Medicine ; Infectious Aortic and Vascular Conditions
Authors: N. Marcano | P. A. Algarabel | L. Fernández Barquı́n | João P. Araújo | André M. Pereira | J.H. Belo | César Magén | L. Morellón | M. R. Ibarra
Source: Physical review. B./Physical review. B (article, 2019-02-19)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Cluster-glass dynamics of the Griffiths phase in <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:msub><mml:mi>Tb</mml:mi><mml:mrow><mml:mn>5</mml:mn><mml:mo>−</mml:mo><mml:mi>x</mml:mi></mml:mrow></mml:msub><mml:msub><mml:mi>La</mml:mi><mml:mi>x</mml:mi></mml:msub><mml:msub><mml:mi>Si</mml:mi><mml:mn>2</mml:mn></mml:msub><mml:msub><mml:mi>Ge</mml:mi><mml:mn>2</mml:mn></mml:msub></mml:mrow></mml:math> The static magnetization and dynamic susceptibility responses of the cluster system within a Griffiths phase of the magnetocaloric compound ${\mathrm{Tb}}_{5\ensuremath{-}x}{\mathrm{La}}_{x}{\mathrm{Si}}_{2}{\mathrm{Ge}}_{2}$ ($x=0.075$) have been investigated. A novel cluster-glass state within the Griffiths phase is formed at a characteristic freezing temperature where short-range ferromagnetic correlations set in the paramagnetic regime. Ferromagneticlike correlations are built up at around 155 K, which suddenly become frozen at a lower temperature $\ensuremath{\sim}140\phantom{\rule{0.16em}{0ex}}\mathrm{K}$, thus in analogy with a reentrant spin glass behavior. The ac susceptibility near the freezing temperature follows a critical slowing down process characterized by ${\ensuremath{\tau}}_{0}={10}^{\ensuremath{-}13}s$ and dynamic exponents $\mathrm{z}\ensuremath{\nu}\ensuremath{\sim}6$ and $\ensuremath{\beta}\ensuremath{\sim}0.4$, similar to well-known spin glass systems. The nonlinear ac susceptibility analysis shows clearly the existence of a transition associated to the reentrant behavior. The origin of the intermediate cluster-glass phase inside the Griffiths phase is proposed to be the result of a combination of short-ranged RKKY intralayer positive exchange interactions between rare-earth ${\mathrm{Tb}}^{3+}$ ions and antiferromagnetic exchange between adjacent interlayers involving Si and Ge atoms in connection to the ${\mathrm{Tb}}^{3+}$ atoms. Spin glass ; Antiferromagnetism ; Ferromagnetism ; Cluster (spacecraft) ; Paramagnetism ; Physics ; Condensed matter physics ; Magnetization ; Phase (matter) ; Magnetic susceptibility ; Crystallography ; Magnetic refrigeration ; Phase transition ; Materials science ; Chemistry ; Quantum mechanics ; Magnetic field Magnetic and transport properties of perovskites and related materials ; Advanced Condensed Matter Physics ; Rare-earth and actinide compounds
Authors: Yang Mo | Lijun Yang | Tiantian Zou | Wei Hou | Ruijin Liao
Source: IEEE Access (article, 2019-01-01)
Field: Materials Science — Biomaterials

Preparation of Composite Insulating Paper With Decreased Permittivity, Good Mechanical and Thermal Properties by Kevlar/Nano Cellulose Fibrils/Softwood Pulp Hybrid Kevlar pulp treated with low-temperature plasma and nano cellulose fibrils (NCFs) have been introduced as reinforcements in the preparation for composite insulating paper with low relative permittivity, dielectric loss, good mechanical properties, and thermal stability. More polar groups of Kevlar fibers could be exposed after plasma treatment. Results showed that the relative permittivity of the composite paper containing 20% Kevlar pulp, 10% NCF, and 70% softwood pulp is decreased by 29.6% and the dielectric loss is decreased by 43.2% at 50 Hz, 25°, compared with the conventional paper formed by pure softwood pulp. The change was due to the introduction of Kevlar pulp with intrinsic low dielectric constant and loss, which decreases the whole polarizability of the paper. The mechanical properties of the composite paper became worse after introducing 20% Kevlar pulp. About 10% NCF can work as a mechanical reinforcement to compensate for the tensile and Yang's modulus loss. Scanning electron microscopy results showed that NCF could fill the void defects and improve the interface bonding of the fibers to enhance the paper mechanical properties. The composite paper also had better insulating and thermal properties than the conventional paper. It has been concluded that the Kevlar pulp/NCF/softwood pulp composite paper has the potential to replace the pure softwood paper for the application of high-voltage ac transformer insulation. Composite material ; Materials science ; Kevlar ; Pulp (tooth) ; Softwood ; Dielectric ; Composite number ; Permittivity ; Dielectric loss ; Relative permittivity ; Electrical insulation paper ; Cellulose ; Chemical engineering ; Voltage ; Transformer Advanced Cellulose Research Studies ; High voltage insulation and dielectric phenomena ; Lignin and Wood Chemistry
Authors: Onur Çaylak | Anil Yaman | Björn Baumeier
Source: Journal of Chemical Theory and Computation (article, 2019-02-12)
Field: Materials Science — Materials Chemistry

Evolutionary Approach to Constructing a Deep Feedforward Neural Network for Prediction of Electronic Coupling Elements in Molecular Materials We present a general framework for the construction of a deep feedforward neural network (FFNN) to predict distance and orientation dependent electronic coupling elements in disordered molecular materials. An evolutionary algorithm automatizes the selection of an optimal architecture of the artificial neural network within a predefined search space. Systematic guidance, beyond minimizing the model error with stochastic gradient descent based backpropagation, is provided by simultaneous maximization of a model fitness that takes into account additional physical properties, such as the field-dependent carrier mobility. As a prototypical system, we consider hole transport in amorphous tris(8-hydroxyquinolinato)aluminum. Reference data for training and validation is obtained from multiscale ab initio simulations, in which coupling elements are evaluated using density-functional theory, for a system containing 4096 molecules. The Coulomb matrix representation is chosen to encode the explicit molecular pair coordinates into a rotation and translation invariant feature set for the FFNN. The final optimized deep feedforward neural network is tested for transport models without and with energetic disorder. It predicts electronic coupling elements and mobilities in excellent agreement with the reference data. Such a FFNN is readily applicable to much larger systems at negligible computational cost, providing a powerful surrogate model to overcome the size limitations of the ab initio approach. Computer science ; Artificial neural network ; Feed forward ; Backpropagation ; Feedforward neural network ; Artificial intelligence ; Ab initio ; Physics Machine Learning in Materials Science ; Thermal properties of materials ; Organic Electronics and Photovoltaics
Authors: Jay Whitacre | Jared Mitchell | Adarsh Dave | Wei Wu | Sven Burke | Venkatasubramanian Viswanathan
Source: Journal of The Electrochemical Society (article, 2019-01-01)
Field: Materials Science — Materials Chemistry

An Autonomous Electrochemical Test Stand for Machine Learning Informed Electrolyte Optimization A fully automated, computer-controlled test stand capable of rapidly creating and electrochemically characterizing any arbitrary liquid electrolyte solution is described. Hundreds of different electrolytes were studied, and the results were used to verify the precision and accuracy of the system. To test the functionality of the approach, several 2-dimensional co-solvated electrolyte solutions containing blends of aqueous sulfates and nitrates were rapidly created and examined automatically. The test stand took less than a day to conduct these searches, while conventional manual methods would have taken much longer. The demonstrated standard error of the test-stand was 0.5 mS/cm on conductivity and 0.02 V for voltage stability window measurements, and several of the combinations studied revealing surprisingly high voltage stability and conductivity values. The demonstrated success of the test-stand in a 2-dimensional search spaces shows the promise of conducting high speed co-optimization studies of liquid electrolytes in particular when used in concert with a machine learning-based real time/in-loop data assessment computational package. Electrolyte ; Conductivity ; Electrochemical window ; Electrochemistry ; Voltage ; Computer science ; Stability (learning theory) ; Materials science ; Analytical Chemistry (journal) ; Chemistry ; Electrical engineering ; Ionic conductivity ; Engineering ; Chromatography ; Machine learning ; Electrode Machine Learning in Materials Science ; Fuel Cells and Related Materials ; Electrocatalysts for Energy Conversion
Authors: Jianshu Jie | Zongxiang Hu | Guoyu Qian | Mouyi Weng | Shunning Li | Shucheng Li | Mingyu Hu | Chen Dong | Weiji Xiao | Jiaxin Zheng | Lin‐Wang Wang | Feng Pan
Source: Science Bulletin (article, 2019-04-05)
Field: Materials Science — Materials Chemistry

Discovering unusual structures from exception using big data and machine learning techniques Big data ; Computer science ; Artificial intelligence ; Data science ; Data mining Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Uxía Rivero | Oliver T. Unke | Markus Meuwly | Stefan Willitsch
Source: The Journal of Chemical Physics (article, 2019-09-09)
Field: Chemistry — Spectroscopy

Reactive atomistic simulations of Diels-Alder reactions: The importance of molecular rotations The Diels-Alder reaction between 2,3-dibromo-1,3-butadiene and maleic anhydride has been studied by means of multisurface adiabatic reactive molecular dynamics and the PhysNet neural network architecture. This system is used as a prototype to explore the concertedness, synchronicity, and possible ways of promotion of Diels-Alder reactions. Analysis of the minimum dynamic path indicates that rotational energy is crucial (∼65%) to drive the system toward the transition state in addition to collision energy (∼20%). Comparison with the reaction of butadiene and maleic anhydride shows that the presence of bromine substituents in the diene accentuates the importance of rotational excitation to promote the reaction. At the high total energies at which reactive events are recorded, the reaction is found to be direct and mostly synchronous. Maleic anhydride ; Diels–Alder reaction ; Chemistry ; Diene ; Computational chemistry ; Photochemistry ; Organic chemistry ; Natural rubber ; Polymer Molecular spectroscopy and chirality ; Advanced Chemical Physics Studies ; Catalysis and Oxidation Reactions
Authors: Guang Zeng | Can Hu | Simin Zou | Li Zhang | Gang Sun
Source: Polymer Composites (article, 2019-02-28)
Field: Materials Science — Polymers and Plastics

BP neural network model for predicting the mechanical performance of a foamed wood‐fiber reinforced thermoplastic starch composite A foamed starch‐based biomass composite was prepared by the extrusion foaming method with starch, wood fiber and polyvinyl alcohol (PVA) as raw materials. The effects of three additives (plasticizer, cross‐linking agent, and blowing agent) on the mechanical performance of the foamed starch‐based biomass composite were studied, and a three‐layer back‐propagation (BP) artificial neural network model was established for mechanical property prediction. The orthogonal experiment results were used as samples to train the network and predict the mechanical performance of foamed composite. The results show that the proposed BP network model is effective in forecasting the mechanical performance of foamed composite. The appropriate amount of composite plasticizer (glycerin/NaOH) can reduce the crystallinity of the starch; increase the compatibility of the starch，wood fiber and PVA, and increase the tensile strength of the composite to 5.91 MPa. Crosslink reaction occurs between PVA molecular under the reaction of cross‐linking agent borax. The tensile strength of the composite increases up to 6.12 MPa. The tensile strength lowers down as a result of the decrease of the contact area caused emerging bubbles with the help of the blowing agent NaHCO 3 . POLYM. COMPOS., 40:3923–3928, 2019. © 2019 Society of Plastics Engineers Materials science ; Composite number ; Composite material ; Ultimate tensile strength ; Starch ; Plasticizer ; Polyvinyl alcohol ; Extrusion ; Thermoplastic ; Blowing agent ; Fiber ; Polyurethane Polymer Foaming and Composites ; Natural Fiber Reinforced Composites ; Lignin and Wood Chemistry
Authors: Alexander A. Guda | С. А. Гуда | Andrea Martini | Aram L. Bugaev | Mikhail A. Soldatov | А. В. Солдатов | Carlo Lamberti
Source: Radiation Physics and Chemistry (article, 2019-07-26)
Field: Chemistry — Inorganic Chemistry

Machine learning approaches to XANES spectra for quantitative 3D structural determination: The case of CO2 adsorption on CPO-27-Ni MOF XANES ; Overfitting ; Adsorption ; Artificial neural network ; Spectral line ; Biological system ; Chemistry ; Computer science ; Materials science ; Artificial intelligence ; Physics ; Physical chemistry Metal-Organic Frameworks: Synthesis and Applications ; Machine Learning in Materials Science ; Phase Equilibria and Thermodynamics
Authors: Zhilei Wang | Toshio Ogawa | Yoshitaka Adachi
Source: Advanced Theory and Simulations (article, 2019-07-17)
Field: Materials Science — Materials Chemistry

Influence of Algorithm Parameters of Bayesian Optimization, Genetic Algorithm, and Particle Swarm Optimization on Their Optimization Performance Abstract In response to modern materials research, a data‐driven properties‐to‐microstructure‐to‐processing inverse analysis is proposed for use in material design. In the present work, machine learning optimization algorithms of Bayesian optimization, genetic algorithm, and particle swarm optimization are used to perform inverse analysis with a maximum property search. The use of machine learning algorithms readily involves careful tuning of learning parameters, which is often carried out by a trial‐and‐error method requiring expert experience or general guidelines, and the choices of such parameters can play a critical role in attaining good optimization performance. Thus, the influence of various parameters on the optimization performance of the aforementioned algorithms are systematically investigated to provide a protocol for selecting adequate algorithm parameters for a given optimization problem in data‐driven material design. Multi-swarm optimization ; Meta-optimization ; Particle swarm optimization ; Bayesian optimization ; Computer science ; Metaheuristic ; Algorithm ; Inverse ; Mathematical optimization ; Genetic algorithm ; Derivative-free optimization ; Optimization algorithm ; Machine learning ; Mathematics Machine Learning in Materials Science ; Advanced Memory and Neural Computing ; Stochastic Gradient Optimization Techniques
Authors: Mikhail Askerka | Ziliang Li | Mathieu Lempen | Yanan Liu | Andrew Johnston | Makhsud I. Saidaminov | Zoltán Zajacz | Edward H. Sargent
Source: Journal of the American Chemical Society (article, 2019-01-31)
Field: Materials Science — Materials Chemistry

Learning-in-Templates Enables Accelerated Discovery and Synthesis of New Stable Double Perovskites In the past three years, machine learning (ML) in combination with density functional theory (DFT) has enabled computational screening of compounds with the goal of accelerated materials discovery. Unfortunately, DFT+ML has, until now, either relied on knowledge of the atomic positions at DFT energy minima, which are a priori unknown, or been limited to chemical spaces of modest size. Here we report a strategy that we term learning-in-templates (LiT), wherein we first define a series of space group and stoichiometry templates corresponding to hypothesized compounds and, orthogonally, we allow any list of atoms to take on any template. The LiT approach is deployed in combination with previously established position-dependent representations and performs best with the representations that rely least on the atomic positions. Since the positions of the atoms in templates are known and do not change, LiT enables us to infer the properties of interest directly; additionally, LiT allows working with increased chemical spaces, since the same elements can take on a large number of templates. Only by using LiT were we able to span 5 × 10<sup>6</sup> double-perovskite compounds and achieve an acceleration factor of 700 compared to brute-force DFT, allowing us to predict never-before-screened compounds. Our findings motivated us to synthesize a new BaCu <sub>y</sub>Ta<sub>(1- y)</sub>S<sub>3</sub> perovskite, which we show using an electron probe microanalyzer has a 5:3 molar ratio of Cu to Ta and, using powder X-ray diffraction (XRD) analysis combined with a DFT-based XRD simulation and fitting, indicate a new phase having an I4/ m space group. Template ; Chemistry ; Chemical space ; Stoichiometry ; Density functional theory ; Space (punctuation) ; Perovskite (structure) ; Nanotechnology ; Crystallography ; Computational chemistry ; Computer science ; Drug discovery ; Physical chemistry ; Materials science Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides ; Quantum Dots Synthesis And Properties
Authors: Fernando Álvarez | Yosadara Ruiz‐Morales
Source: Journal of Chemical Information and Modeling (article, 2019-11-12)
Field: Chemistry — Organic Chemistry

Database of Nuclear Independent Chemical Shifts (NICS) versus NICS<sub>ZZ</sub> of Polycyclic Aromatic Hydrocarbons (PAHs) In the present contribution, we have developed a database, called the FAR-database, where the acronym FAR stands for Fused Aromatic Rings, which presents the results of nuclear independent chemical shifts calculations, NICS(0), NICS(1), NICS(0)<sub>ZZ</sub>, and NICS(1)<sub>ZZ</sub>, of 660 neutral benzenoid-PAHs and cyclopenta-fused PAHs. The FAR-database provides NICS data of aromaticity of PAHs that could be used in data science and machine learning. To the best of our knowledge, no such database is available in the literature. The importance of this database lies in its potential to transform data into insight and knowledge. Additionally, a new visual representation of the NICS aromaticity pattern, based on the magnitude of the NICS value, is presented. Nowadays calculations of NICS(0)<sub>ZZ</sub> or NICS(1)<sub>ZZ</sub> have become popular methods to evaluate aromaticity of systems. By looking at all the 660 systems in the FAR-database, it becomes evident that NICS(0), NICS(1), and NICS(1)<sub>ZZ</sub> present similar NICS aromaticity patterns for most of the systems. But the NICS aromaticity patterns found with NICS(0)<sub>ZZ</sub> in many cases do not agree with the NICS aromaticity patterns found with NICS(0), NICS(1), and NICS(1)<sub>ZZ</sub>. There are cases where the NICS(0)<sub>ZZ</sub> aromaticity pattern does not show an aromatic character at all. From XY NICS scan at planes from Z = 0 to Z = 1, it is found that as the Z-height is increased, the π-electron ring current effects are dominant, and the σ-bonding contributions are diminished. Therefore, it is recommended here to compute NICS(1)<sub>ZZ</sub> instead of NICS(0)<sub>ZZ</sub> when analyzing NICS of PAHs. Aromaticity ; Ring (chemistry) ; Chemistry ; Chemical shift ; Database ; Computer science ; Physical chemistry ; Molecule ; Organic chemistry Synthesis and Properties of Aromatic Compounds ; Graph theory and applications ; Axial and Atropisomeric Chirality Synthesis
Authors: Laura Pirro | Pedro S.F. Mendes | Stijn Paret | Bart D. Vandegehuchte | Guy Marin | Joris Thybaut
Source: Catalysis Science & Technology (article, 2019-01-01)
Field: Materials Science — Materials Chemistry

Descriptor–property relationships in heterogeneous catalysis: exploiting synergies between statistics and fundamental kinetic modelling Combined kinetic and statistical approach to shed light on the link between kinetically-relevant descriptors and easily tuneable catalyst properties. Kinetic energy ; Catalysis ; Property (philosophy) ; Biological system ; Statistical physics ; Computer science ; Biochemical engineering ; Chemistry ; Physics ; Engineering ; Biology ; Quantum mechanics ; Organic chemistry Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Catalytic Processes in Materials Science
Authors: Peter Visser | Kristof Marcoen | Gustavo F. Trindade | M‐L. Abel | John F. Watts | Tom Hauffman | J.M.C. Mol | Herman Terryn
Source: Corrosion Science (article, 2019-02-11)
Field: Materials Science — Materials Chemistry

The chemical throwing power of lithium-based inhibitors from organic coatings on AA2024-T3 Corrosion ; Leaching (pedology) ; Materials science ; Throwing ; Lithium (medication) ; Corrosion inhibitor ; Aluminium ; Kinetics ; Chemical engineering ; Metallurgy ; Inorganic chemistry ; Chemistry ; Environmental science Corrosion Behavior and Inhibition ; Non-Destructive Testing Techniques ; Concrete Corrosion and Durability