jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Farzaneh Afshariazar | Ali Morsali | Simona Sorbara | Natalia M. Padial | Esther Roldán‐Molina | J. Enrique Oltra | Valentina Colombo | Jorge A. R. Navarro
Source: Chemistry - A European Journal (article, 2021-06-11)
Field: Chemistry — Inorganic Chemistry

Impact of Pore Size and Defects on the Selective Adsorption of Acetylene in Alkyne‐Functionalized Nickel(II)‐Pyrazolate‐Based MOFs C<sub>2</sub> H<sub>2</sub> /CO<sub>2</sub> separation is a highly challenging process as a consequence of their similar physicochemical properties. In this work we have explored, by static and dynamic gas sorption techniques and computational modelling, the suitability of a series of two isoreticular robust Ni(II)pyrazolate-based MOFs, bearing alkyne moieties on the ligand backbones, for C<sub>2</sub> H<sub>2</sub> /CO<sub>2</sub> separation. The results are consistent with high adsorption capacity and selectivity of the essayed systems towards C<sub>2</sub> H<sub>2</sub> molecules. Furthermore, a post-synthetic treatment with KOH ethanolic solution gives rise to linker vacancy defects and incorporation of extraframework potassium ions. Creation of defects is responsible for increased adsorption capacity for both gases, however, strong interactions of the cluster basic sites and extraframework potassium cations with CO<sub>2</sub> molecules are responsible for a lowering of C<sub>2</sub> H<sub>2</sub> over CO<sub>2</sub> selectivity. Alkyne ; Adsorption ; Sorption ; Selectivity ; Acetylene ; Linker ; Ligand (biochemistry) ; Molecule ; Chemistry ; Nickel ; Potassium ; Combinatorial chemistry ; Chemical engineering ; Organic chemistry Metal-Organic Frameworks: Synthesis and Applications ; Covalent Organic Framework Applications ; Carbon dioxide utilization in catalysis
Authors: Peter A. Beaucage | R. B. van Dover | Francis J. DiSalvo | Sol M. Grüner | Ulrich Wiesner
Source: Advanced Materials (article, 2021-05-16)
Field: Materials Science — Materials Chemistry

Superconducting Quantum Metamaterials from Convergence of Soft and Hard Condensed Matter Science Superconducting quantum metamaterials are expected to exhibit a variety of novel properties, but have been a major challenge to prepare as a result of the lack of appropriate synthetic routes to high-quality materials. Here, the discovery of synthesis routes to block copolymer (BCP) self-assembly-directed niobium nitrides and carbonitrides is described. The resulting materials exhibit unusual structure retention even at temperatures as high as 1000 °C and resulting critical temperature, T<sub>c</sub> , values comparable to their bulk analogues. Applying the concepts of soft matter self-assembly, it is demonstrated that a series of four different BCP-directed mesostructured superconductors are accessible from a single triblock terpolymer. Resulting materials display a mesostructure-dependent T<sub>c</sub> without substantial variation of the XRD-measured lattice parameters. Finally, field-dependent magnetization measurements of a sample with double-gyroid morphology show abrupt jumps comparable in overall behavior to flux avalanches. Results suggest a fruitful convergence of soft and hard condensed matter science. Superconductivity ; Materials science ; Soft matter ; Gyroid ; Condensed matter physics ; Metamaterial ; Copolymer ; Quantum ; Lattice (music) ; Chemical physics ; Nanotechnology ; Polymer ; Colloid ; Physics ; Chemical engineering ; Optoelectronics ; Composite material ; Quantum mechanics Block Copolymer Self-Assembly ; Physics of Superconductivity and Magnetism ; Advanced Condensed Matter Physics
Authors: Raquel C. R. Gonçalves | João Pina | Susana P. G. Costa | M. Manuela M. Raposo
Source: Dyes and Pigments (article, 2021-09-03)
Field: Materials Science — Materials Chemistry

Synthesis and characterization of aryl-substituted BODIPY dyes displaying distinct solvatochromic singlet oxygen photosensitization efficiencies BODIPY ; Photochemistry ; Ultrafast laser spectroscopy ; Solvatochromism ; Singlet oxygen ; Chemistry ; Intersystem crossing ; Aryl ; Quantum yield ; Triplet state ; Toluene ; Perylene ; Singlet state ; Solvent ; Alkyl ; Oxygen ; Spectroscopy ; Fluorescence ; Excited state ; Organic chemistry ; Molecule Luminescence and Fluorescent Materials ; Nanoplatforms for cancer theranostics ; Porphyrin and Phthalocyanine Chemistry
Authors: Dnyaneshwar S. Gavhane | Atul D. Sontakke | Marijn A. van Huis
Source: Advanced Functional Materials (article, 2021-10-20)
Field: Materials Science — Materials Chemistry

Selective Vertical and Horizontal Growth of 2D WS<sub>2</sub> Revealed by In Situ Thermolysis using Transmission Electron Microscopy Abstract Direct observation of the growth dynamics of 2D transition metal dichalcogenides (TMDs) is of key importance for understanding and controlling the growth modes and for tailoring these intriguing materials to desired orientations and layer thicknesses. Here, various stages and multiple growth modes in the formation of WS 2 layers on different substrates through thermolysis of a single solid‐state (NH 4 ) 2 WS 4 precursor are revealed using in situ transmission electron microscopy. Control over vertical and horizontal growth is achieved by varying the thickness of the drop‐casted precursor from which WS 2 is grown during heating. First depositing platinum (Pt) and gold (Au) on the heating chips much enhance the growth process of WS 2 resulting in an increased length of vertical layers and in a self‐limited thickness of horizontal layers. Interference patterns are formed by the mutual rotation of two WS 2 layers by various angles on metal deposited heating chips. This shows detailed insights into the growth dynamics of 2D WS 2 as a function of temperature, thereby establishing control over orientation and size. These findings also unveil the important role of metal substrates in the evolution of WS 2 structures, offering general and effective pathways for nano‐engineering of 2D TMDs for a variety of applications. Materials science ; Transmission electron microscopy ; In situ ; Transition metal ; Metal ; Thermal decomposition ; Drop (telecommunication) ; Nanotechnology ; Chemical physics ; Crystallography ; Chemistry ; Metallurgy 2D Materials and Applications ; Graphene research and applications ; MXene and MAX Phase Materials
Authors: Tamara Fukalova Fukalova | María Dolores García Martínez | María Dolores Raigón
Source: PeerJ (article, 2021-11-23)
Field: Chemistry — Analytical Chemistry

Five undervalued edible species inherent to autumn-winter season: nutritional composition, bioactive constituents and volatiles profile Fresh edible leaves of the undervalued plants show considerable nutritional potential and a high bioactive components level, which highlight the antioxidant capacity. Leaves of <i>C. album</i> L. stand out due to their higher concentration of nutritional compounds, while <i>D. erucoides</i> L. is noted for its higher antioxidant capacity. Aromatic descriptor of pyrazines detected in the leaves of <i>D. erucoides</i> L. is associated with the slightly spicy flavors that characterize this species. Results suggest that studied species could be of great relevance in introducing these five edible herbs as a source of new grown material, postulating them as healthy food ingredients with attractive flavors for the gourmet cuisine industry. DPPH ; Composition (language) ; Botany ; Chenopodium ; Polyphenol ; Biology ; Food science ; Horticulture ; Chemistry ; Antioxidant ; Weed Heavy Metals in Plants ; Ethnobotanical and Medicinal Plants Studies ; Seed and Plant Biochemistry
Authors: Lukas Pielsticker | Rachel Nicholls | Sebastian Beeg | Caroline Hartwig | G. Klihm | Robert Schlögl | S. Tougaard | Mark Greiner
Source: Surface and Interface Analysis (article, 2021-05-07)
Field: Materials Science — Surfaces, Coatings and Films

Inelastic electron scattering by the gas phase in near ambient pressure XPS measurements X‐ray photoemission spectroscopy (XPS) measurements in near‐ambient pressure (NAP) conditions result in a signal loss of the primary spectrum as a result of inelastic scattering of photoelectrons in the gas phase. The inelastic scattering of the primary electrons gives rise to a secondary signal that can result in additional and often unwanted features in the measured spectrum. In the present work, we derive equations that can be used to model the resulting signal and provide equations that can be used to simulate or remove the inelastic scattering signal from measured spectra. We demonstrate this process for photoemission spectra of a wide range of kinetic energies, measured from Au, Ag, and Cu, in a variety of gases (N 2 , He, H 2 , and O 2 ). The work is supplemented with an open‐source software in which the algorithms described here have been implemented and can be used to remove the gas phase inelastic scattering signal. X-ray photoelectron spectroscopy ; Inelastic scattering ; Gas phase ; Electron ; Atomic physics ; Scattering ; Phase (matter) ; Ambient pressure ; Materials science ; Electron scattering ; Analytical Chemistry (journal) ; Chemistry ; Physics ; Nuclear physics ; Nuclear magnetic resonance ; Optics ; Thermodynamics ; Physical chemistry ; Environmental chemistry Electron and X-Ray Spectroscopy Techniques ; Advancements in Photolithography Techniques ; Integrated Circuits and Semiconductor Failure Analysis
Authors: Hamidreza Taghvaee | Fu Liu | Ana Díaz‐Rubio | Sergei Tretyakov
Source: Physical review. B./Physical review. B (article, 2021-12-08)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Subwavelength focusing by engineered power-flow conformal metamirrors It is highly desirable to break the diffraction limit on the resolution of optical devices and achieve subwavelength focusing. Here, the authors show that curved and inhomogeneous reflectors offer a practical method for achieving this goal. The surface profile of the focusing reflector is designed to be tangential to the desired power flow so that the metamirror can be described locally by lossless impedance boundaries and can be easily implemented using simple lossless meta-atoms. We envisage some practical applications of subwavelength focusing that become possible using this method. Conformal map ; Wavefront ; Optics ; Focus (optics) ; Reflector (photography) ; Physics ; Power (physics) ; Point source ; Point (geometry) ; Plane wave ; Computer science ; Geometry ; Mathematics Metamaterials and Metasurfaces Applications ; Advanced Antenna and Metasurface Technologies ; Antenna Design and Analysis
Authors: Tomás Andrade | Matteo Baggioli | Alexander Krikun
Source: Journal of High Energy Physics (article, 2021-03-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Phase relaxation and pattern formation in holographic gapless charge density waves A bstract We study the dynamics of spontaneous translation symmetry breaking in holographic models in presence of weak explicit sources. We show that, unlike conventional gapped quantum charge density wave systems, this dynamics is well characterized by the effective time dependent Ginzburg-Landau equation, both above and below the critical temperature, which leads to a “gapless” algebraic pattern of metal-insulator phase transition. In this framework we elucidate the nature of the damped Goldstone mode (the phason), which has earlier been identified in the effective hydrodynamic theory of pinned charge density wave and observed in holographic homogeneous lattice models. We follow the motion of the quasinormal modes across the dynamical phase transition in models with either periodic inhomogeneous or helical homogeneous spatial structures, showing that the phase relaxation rate is continuous at the critical temperature. Moreover, we find that the qualitative low-energy dynamics of the broken phase is universal, insensitive to the precise pattern of translation symmetry breaking, and therefore applies to homogeneous models as well. Phason ; Gapless playback ; Physics ; Translational symmetry ; Phase transition ; Charge density wave ; Condensed matter physics ; Critical phenomena ; Symmetry breaking ; Critical exponent ; Quantum mechanics ; Phonon ; Superconductivity Organic and Molecular Conductors Research ; Physics of Superconductivity and Magnetism ; Quantum, superfluid, helium dynamics
Authors: Swati Chaudhary | Christina Knapp | Gil Refael
Source: Physical review. B./Physical review. B (article, 2021-04-16)
Field: Materials Science — Materials Chemistry

Anomalous exciton transport in response to a uniform in-plane electric field Excitons are neutral objects that, naively, should have no response to a uniform electric field. Could the Berry curvature of the underlying electronic bands alter this conclusion? In this work, we show that Berry curvature can indeed lead to anomalous transport for excitons in two-dimensional materials subject to a uniform in-plane electric field. By considering the constituent electron and hole dynamics, we demonstrate that there exists a regime for which the corresponding anomalous velocities are in the same direction. We establish the resulting center-of-mass motion of the exciton through both a semiclassical and fully quantum mechanical analysis, and elucidate the critical role of Bloch oscillations in achieving this effect. We identify transition metal dichalcogenide heterobilayers as candidate materials to observe the effect. Electric field ; Plane (geometry) ; Condensed matter physics ; Exciton ; Field (mathematics) ; Physics ; Quantum electrodynamics ; Quantum mechanics ; Mathematics ; Geometry 2D Materials and Applications ; Perovskite Materials and Applications ; Quantum and electron transport phenomena
Authors: Marta Terrados-Cristos | Francisco Ortega Fernández | Guillermo Alonso-Iglesias | Marina Díaz-Piloñeta | Ana Fernández-Iglesias
Source: Materials (article, 2021-07-13)
Field: Materials Science — Materials Chemistry

Corrosion Prediction of Weathered Galvanised Structures Using Machine Learning Techniques Galvanised steel atmospheric corrosion is a complex multifactorial phenomenon that globally affects many structures, equipment, and sectors. Moreover, the International Organization of Standardization (ISO) standards require specific pollutant depositions values for any atmosphere classification or corrosion loss prediction result. The aim of this research is to develop predictive models to estimate corrosion loss based on easily worldwide available parameters. Experimental data from internationally validated studies were used for the data mining process, basing their characterisation on seven globally accessible qualitative and quantitative variables. Self-Organising Maps including both supervised and unsupervised layers were used to predict first-year corrosion loss, its corrosivity categories, and an uncertainty range. Additionally, a formula optimised with Newton's method has been proposed for extrapolating these results to long-term results. The predictions obtained were compared with real values using Euclidean distances to know its similarity degree, offering high prediction performance. Specifically, evaluation results showed an average saving of up to 16% in coatings using these predictions. Therefore, using the proposed models reduces the uncertainty of the final structures state by predicting their material loss, avoiding initial over-dimensioning of structures, and meeting the principles of efficiency and sustainability, thus reducing costs. Corrosion ; Galvanization ; Predictive modelling ; Dimensioning ; Standardization ; Range (aeronautics) ; Computer science ; Process (computing) ; Machine learning ; Data mining ; Environmental science ; Engineering ; Materials science ; Metallurgy ; Composite material ; Layer (electronics) Corrosion Behavior and Inhibition ; Concrete Corrosion and Durability ; Structural Integrity and Reliability Analysis
Authors: Zihui Dong | Shiladitya Paul | Karl Tassenberg | Geoff Melton | Hongbiao Dong
Source: Computers in Industry (article, 2021-03-18)
Field: Materials Science — Materials Chemistry

Transformation from human-readable documents and archives in arc welding domain to machine-interpretable data The capability of extracting useful information from documents and further transferring into knowledge is essential to advance technology innovations in industries. However, the overwhelming majority of scientific literature primarily published as unstructured human-readable formats is incompatible for machine analysis via contemporary artificial intelligence (AI) methods that effectively discovers knowledge from data. Therefore, the extraction approach transforming of unstructured data are fundamental in establishing state-of-the-art digital knowledge-based platforms. In this paper, we integrated multiple Python libraries and developed a method as a cohesive package for automated data extraction and quick processing to convert unstructured documents into machine-interpretable data. Transformed data can be further incorporated with AI analytical methods. The output files have shown excellent quality of digitalised data without major flaws in terms of context inconsistency. All scripts were written in Python with functional modules providing easy accessibility and proficiency to achieve objectives. Eventually, the finalised well-structured data can be implemented for further knowledge discovery. Python (programming language) ; Scripting language ; Computer science ; Unstructured data ; Knowledge extraction ; Information extraction ; Software engineering ; Data mining ; Artificial intelligence ; Data science ; Information retrieval ; Programming language ; Big data Machine Learning in Materials Science ; Scientific Computing and Data Management
Authors: Francis J. Alexander | James Ang | Jenna A. Bilbrey | J. Balewski | Tiernan Casey | Ryan Chard | Jong Youl Choi | Sutanay Choudhury | Bert Debusschere | Anthony M. DeGennaro | Nikoli Dryden | John Ellis | Ian Foster | Cristina García–Cardona | Sayan Ghosh | Peter Harrington | Yunzhi Huang | Shantenu Jha | Travis Johnston | Ai Kagawa | Ramakrishnan Kannan | Neeraj Kumar | Zhengchun Liu | Naoya Maruyama | Satoshi Matsuoka | Erin McCarthy | Jamaludin Mohd‐Yusof | P. Nugent | Yosuke Oyama | Thomas Proffen | David Pugmire | Sivasankaran Rajamanickam | Vinay Ramakrishniah | Malachi Schram | Sudip K. Seal | Ganesh Sivaraman | Christine Sweeney | Li Lynn Tan | Rajeev Thakur | Brian Van Essen | Logan Ward | Paul M. Welch | Michael M. Wolf | Sotiris S. Xantheas | Kevin G. Yager | Shinjae Yoo | Byung-Jun Yoon
Source: The International Journal of High Performance Computing Applications (article, 2021-09-27)
Field: Materials Science — Materials Chemistry

Co-design Center for Exascale Machine Learning Technologies (ExaLearn) Rapid growth in data, computational methods, and computing power is driving a remarkable revolution in what variously is termed machine learning (ML), statistical learning, computational learning, and artificial intelligence. In addition to highly visible successes in machine-based natural language translation, playing the game Go, and self-driving cars, these new technologies also have profound implications for computational and experimental science and engineering, as well as for the exascale computing systems that the Department of Energy (DOE) is developing to support those disciplines. Not only do these learning technologies open up exciting opportunities for scientific discovery on exascale systems, they also appear poised to have important implications for the design and use of exascale computers themselves, including high-performance computing (HPC) for ML and ML for HPC. The overarching goal of the ExaLearn co-design project is to provide exascale ML software for use by Exascale Computing Project (ECP) applications, other ECP co-design centers, and DOE experimental facilities and leadership class computing facilities. Computer science ; Exascale computing ; Supercomputer ; Artificial intelligence ; Operating system Machine Learning in Materials Science ; Reservoir Engineering and Simulation Methods ; Scientific Computing and Data Management
Authors: Chao Zhang | Yang Sun | Haidi Wang | Feng Zhang | Tongqi Wen | Kai‐Ming Ho | Cai‐Zhuang Wang
Source: The Journal of Physical Chemistry C (article, 2021-01-28)
Field: Materials Science — Materials Chemistry

Crystallization of the P<sub>3</sub>Sn<sub>4</sub> Phase upon Cooling P<sub>2</sub>Sn<sub>5</sub> Liquid by Molecular Dynamics Simulation Using a Machine Learning Interatomic Potential We performed molecular dynamics simulations to study the crystallization of the P3Sn4 phase from P2Sn5 liquid using a machine learning (ML) interatomic potential with desirable efficiency and accuracy. Our results capture the liquid properties of P2Sn5 at 1300 K, which is well above the melting temperature. The phase separation and crystallization are observed when P2Sn5 liquid is cooled down below 832 and 505 K, respectively. The simulation results are in good agreement with the experimentally observed phase transformation behaviors and provide useful insights into the complex nucleation and crystallization process at the details of atomistic scale. Our work also demonstrated that ML interatomic potentials based on neural network deep learning are robust and capable of accurately describing the energetics and kinetics of complex materials through molecular dynamics simulations. Crystallization ; Molecular dynamics ; Nucleation ; Phase (matter) ; Materials science ; Interatomic potential ; Thermodynamics ; Work (physics) ; Chemical physics ; Chemistry ; Physics ; Computational chemistry Machine Learning in Materials Science ; nanoparticles nucleation surface interactions ; Nuclear Materials and Properties
Authors: Sanket Kadulkar | Michael P. Howard | Thomas M. Truskett | Venkat Ganesan
Source: The Journal of Physical Chemistry B (article, 2021-04-29)
Field: Materials Science — Materials Chemistry

Prediction and Optimization of Ion Transport Characteristics in Nanoparticle-Based Electrolytes Using Convolutional Neural Networks We develop a convolutional neural network (CNN) model to predict the diffusivity of cations in nanoparticle-based electrolytes and use it to identify the characteristics of morphologies that exhibit optimal transport properties. The ground truth data are obtained from kinetic Monte Carlo (kMC) simulations of cation transport parametrized using a multiscale modeling strategy. We implement deep learning approaches to quantitatively link the diffusivity of cations to the spatial arrangement of the nanoparticles. We then integrate the trained CNN model with a topology optimization algorithm for accelerated discovery of nanoparticle morphologies that exhibit optimal cation diffusivities at a specified nanoparticle loading, and we investigate the ability of the CNN model to quantitatively account for the influence of interparticle spatial correlations on cation diffusivity. Finally, by using data-driven approaches, we explore how simple descriptors of nanoparticle morphology correlate with cation diffusivity, thus providing a physical rationale for the observed optimal microstructures. The results of this study highlight the capability of CNNs to serve as surrogate models for structure-property relationships in composites with monodisperse spherical particles, which can in turn be used with inverse methods to discover morphologies that produce optimal target properties. Thermal diffusivity ; Convolutional neural network ; Nanoparticle ; Multiscale modeling ; Materials science ; Electrolyte ; Kinetic Monte Carlo ; Computer science ; Biological system ; Inverse ; Artificial neural network ; Dispersity ; Monte Carlo method ; Nanotechnology ; Artificial intelligence ; Chemistry ; Physics ; Thermodynamics ; Mathematics ; Computational chemistry Machine Learning in Materials Science ; Electrostatics and Colloid Interactions ; Composite Material Mechanics