jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Tsung-Wei Ke | Aaron S. Brewster | Stella X. Yu | Daniela Ushizima | Chao Yang | Nicholas K. Sauter
Source: Journal of Synchrotron Radiation (article, 2018-04-24)
Field: Materials Science — Materials Chemistry

A convolutional neural network-based screening tool for X-ray serial crystallography A new tool is introduced for screening macromolecular X-ray crystallography diffraction images produced at an X-ray free-electron laser light source. Based on a data-driven deep learning approach, the proposed tool executes a convolutional neural network to detect Bragg spots. Automatic image processing algorithms described can enable the classification of large data sets, acquired under realistic conditions consisting of noisy data with experimental artifacts. Outcomes are compared for different data regimes, including samples from multiple instruments and differing amounts of training data for neural network optimization. Convolutional neural network ; Computer science ; Artificial neural network ; Artificial intelligence ; Diffraction ; Deep learning ; Pattern recognition (psychology) ; Algorithm ; Optics ; Physics Enzyme Structure and Function ; Advanced X-ray Imaging Techniques ; Advanced Electron Microscopy Techniques and Applications
Authors: Michael M. Porter | Pooya Niksiar
Source: PLoS ONE (article, 2018-09-28)
Field: Materials Science — Biomaterials

Multidimensional mechanics: Performance mapping of natural biological systems using permutated radar charts Comparing the functional performance of biological systems often requires comparing multiple mechanical properties. Such analyses, however, are commonly presented using orthogonal plots that compare N ≤ 3 properties. Here, we develop a multidimensional visualization strategy using permutated radar charts (radial, multi-axis plots) to compare the relative performance distributions of mechanical systems on a single graphic across N ≥ 3 properties. Leveraging the fact that radar charts plot data in the form of closed polygonal profiles, we use shape descriptors for quantitative comparisons. We identify mechanical property-function correlations distinctive to rigid, flexible, and damage-tolerant biological materials in the form of structural ties, beams, shells, and foams. We also show that the microstructures of dentin, bone, tendon, skin, and cartilage dictate their tensile performance, exhibiting a trade-off between stiffness and extensibility. Lastly, we compare the feeding versus singing performance of Darwin's finches to demonstrate the potential of radar charts for multidimensional comparisons beyond mechanics of materials. Radar ; Computer science ; Biological system ; Radar chart ; Plot (graphics) ; Visualization ; Artificial intelligence ; Mathematics ; Biology ; Statistics Silk-based biomaterials and applications ; Cephalopods and Marine Biology ; Paleontology and Evolutionary Biology
Authors: Jiwei Cui | Mattias Björnmalm | Yi Ju | Frank Caruso
Source: Langmuir (article, 2018-08-22)
Field: Materials Science — Biomaterials

Nanoengineering of Poly(ethylene glycol) Particles for Stealth and Targeting The assembly of particles composed solely or mainly of poly(ethylene glycol) (PEG) is an emerging area that is gaining increasing interest within bio-nano science. PEG, widely considered to be the "gold standard" among polymers for drug delivery, is providing a platform for exploring fundamental questions and phenomena at the interface between particle engineering and biomedicine. These include the targeting and stealth behaviors of synthetic nanomaterials in biological environments. In this feature article, we discuss recent work in the nanoengineering of PEG particles and explore how they are enabling improved targeting and stealth performance. Specific examples include PEG particles prepared through surface-initiated polymerization, mesoporous silica replication via postinfiltration, and particle assembly through metal-phenolic coordination. This particle class exhibits unique in vivo behavior (e.g., biodistribution and immune cell interactions) and has recently been explored for drug delivery applications. Nanoengineering ; Ethylene glycol ; Nanotechnology ; Chemical engineering ; Materials science ; Chemistry ; Polymer science ; Organic chemistry ; Engineering Nanoparticle-Based Drug Delivery ; Graphene and Nanomaterials Applications ; Electrospun Nanofibers in Biomedical Applications
Authors: Elena Uteva | R. Graham | R. Wilkinson | Richard J. Wheatley
Source: The Journal of Chemical Physics (article, 2018-11-07)
Field: Materials Science — Materials Chemistry

Active learning in Gaussian process interpolation of potential energy surfaces Three active learning schemes are used to generate training data for Gaussian process interpolation of intermolecular potential energy surfaces. These schemes aim to achieve the lowest predictive error using the fewest points and therefore act as an alternative to the status quo methods involving grid-based sampling or space-filling designs like Latin hypercubes (LHC). Results are presented for three molecular systems: CO<sub>2</sub>-Ne, CO<sub>2</sub>-H<sub>2</sub>, and Ar<sub>3</sub>. For each system, two of the active learning schemes proposed notably outperform LHC designs of comparable size, and in two of the systems, produce an error value an order of magnitude lower than the one produced by the LHC method. The procedures can be used to select a subset of points from a large pre-existing data set, to select points to generate data <i>de novo</i>, or to supplement an existing data set to improve accuracy. Interpolation (computer graphics) ; Gaussian process ; Kriging ; Process (computing) ; Energy (signal processing) ; Gaussian ; Computer science ; Artificial intelligence ; Applied mathematics ; Mathematical optimization ; Mathematics ; Physics ; Machine learning ; Statistics ; Quantum mechanics ; Programming language Machine Learning in Materials Science ; Process Optimization and Integration ; Reservoir Engineering and Simulation Methods
Authors: Rouba Ghobeira | Mahtab Asadian | Chris Vercruysse | Heidi Declercq | Nathalie De Geyter | Rino Morent
Source: Polymer (article, 2018-10-11)
Field: Materials Science — Biomaterials

Wide-ranging diameter scale of random and highly aligned PCL fibers electrospun using controlled working parameters Electrospinning ; Materials science ; Fiber ; Polycaprolactone ; Composite material ; Ultimate tensile strength ; Polymer ; Contact angle ; Polyester ; Solvent ; Chemical engineering ; Chemistry ; Organic chemistry Electrospun Nanofibers in Biomedical Applications ; Advanced Sensor and Energy Harvesting Materials ; Silk-based biomaterials and applications
Authors: Matthew Blain‐Hartung | Nathan C. Rockwell | Marcus V. Moreno | Shelley S. Martin | Fei Gan | Donald A. Bryant | J. Clark Lagarias
Source: Journal of Biological Chemistry (article, 2018-04-09)
Field: Materials Science — Materials Chemistry

Cyanobacteriochrome-based photoswitchable adenylyl cyclases (cPACs) for broad spectrum light regulation of cAMP levels in cells Class III adenylyl cyclases generate the ubiquitous second messenger cAMP from ATP often in response to environmental or cellular cues. During evolution, soluble adenylyl cyclase catalytic domains have been repeatedly juxtaposed with signal-input domains to place cAMP synthesis under the control of a wide variety of these environmental and endogenous signals. Adenylyl cyclases with light-sensing domains have proliferated in photosynthetic species depending on light as an energy source, yet are also widespread in nonphotosynthetic species. Among such naturally occurring light sensors, several flavin-based photoactivated adenylyl cyclases (PACs) have been adopted as optogenetic tools to manipulate cellular processes with blue light. In this report, we report the discovery of a cyanobacteriochrome-based photoswitchable adenylyl cyclase (cPAC) from the cyanobacterium <i>Microcoleus</i> sp. <i>PCC 7113.</i> Unlike flavin-dependent PACs, which must thermally decay to be deactivated, cPAC exhibits a bistable photocycle whose adenylyl cyclase could be reversibly activated and inactivated by blue and green light, respectively. Through domain exchange experiments, we also document the ability to extend the wavelength-sensing specificity of cPAC into the near IR. In summary, our work has uncovered a cyanobacteriochrome-based adenylyl cyclase that holds great potential for the design of bistable photoswitchable adenylyl cyclases to fine-tune cAMP-regulated processes in cells, tissues, and whole organisms with light across the visible spectrum and into the near IR. Chemistry ; Adenylyl cyclase ; Cell biology ; Broad spectrum ; Biology ; Signal transduction ; Combinatorial chemistry Photochromic and Fluorescence Chemistry ; Light effects on plants ; Photoreceptor and optogenetics research
Authors: Angela M. Wagner | David S. Spencer | Nicholas A. Peppas
Source: Journal of Applied Polymer Science (article, 2018-01-31)
Field: Materials Science — Biomaterials

Advanced architectures in the design of responsive polymers for cancer nanomedicine In recent decades, nanoparticles have shown significant promise as an oncology treatment modality. Responsive polymers represent a promising class of nanoparticles that can trigger delivery through the exploitation of a specific stimuli. Response to a stimulus is one of the most basic processes found in living systems. As such, the desire to engineer dynamic and functional materials is becoming more prevalent in an effort to achieve precise control over our environment. The combination of controlled radical polymerization and high yielding chemistry strategies provide an excellent basis for the development of the next generation of drug delivery systems. The versatility of polymer chemistries available enables the synthesis of increasingly complex architectures with enhanced delivery specificity and control over the desired properties to interface with biological systems. This tutorial review highlights recent developments in polymer-based approaches to internally responsive nanoparticles for oncology. Presented are concise overviews of the current challenges and opportunities in cancer nanomedicine, common polymer-based architectures, and the basis for internally triggered stimuli-response relationships commonly employed in oncology applications. Examples of the chemistry used in the design of environmentally labile nanomaterials are discussed, and we outline recent advances in creating advanced bioresponsive drug delivery architectures. Nanomedicine ; Nanotechnology ; Drug delivery ; Cancer therapy ; Computer science ; Nanoparticle ; Materials science ; Cancer ; Medicine Nanoparticle-Based Drug Delivery ; Nanoplatforms for cancer theranostics ; Graphene and Nanomaterials Applications
Authors: Daniel M. Balazs | Maria Antonietta Loi
Source: Advanced Materials (article, 2018-06-10)
Field: Materials Science — Materials Chemistry

Lead‐Chalcogenide Colloidal‐Quantum‐Dot Solids: Novel Assembly Methods, Electronic Structure Control, and Application Prospects Abstract The quest for novel semiconductors with easy, cheap fabrication and tailorable properties has led to the development of several classes of materials, such as semiconducting polymers, carbon nanotubes, hybrid perovskites, and colloidal quantum dots. All these candidates can be processed from the liquid phase, enabling easy fabrication, and are suitable for different electronic and optoelectronic applications. Here, recent developments in the field of colloidal‐quantum‐dot solids are discussed, with a focus on lead‐chalcogenide systems. These include novel deposition methods; the recent growing understanding of their fundamental properties, driven by major successes in the control of the nanostructured assembly and surface chemistry; and selected reports on lab‐scale devices showing the technological prospects of these fascinating class of materials. Nanotechnology ; Chalcogenide ; Materials science ; Quantum dot ; Fabrication ; Semiconductor ; Electronics ; Carbon nanotube ; Optoelectronics ; Chemistry Quantum Dots Synthesis And Properties ; Chalcogenide Semiconductor Thin Films ; Perovskite Materials and Applications
Authors: Fabian Arnold | Raluca-Maria Stan | Sanjoy Kr Mahatha | Henriette E. Lund | Davide Curcio | Maciej Dendzik | Harsh Bana | Elisabetta Travaglia | Luca Bignardi | Paolo Lacovig | Daniel Lizzit | Zheshen Li | Marco Bianchi | Jill A. Miwa | Martin Bremholm | Silvano Lizzit | Philip Hofmann | Charlotte E. Sanders
Source: 2D Materials (article, 2018-07-25)
Field: Materials Science — Materials Chemistry

Novel single-layer vanadium sulphide phases VS2 is a challenging material to prepare stoichiometrically in the bulk, and the single layer has not been successfully isolated before now. Here we report the first realization of single-layer VS2, which we have prepared epitaxially with high quality on Au(1 1 1) in the octahedral (1T) structure. We find that we can deplete the VS2 lattice of S by annealing in vacuum so as to create an entirely new 2D compound that has no bulk analogue. The transition is reversible upon annealing in an H2S gas atmosphere. We report the structural properties of both the stoichiometric and S-depleted compounds on the basis of low-energy electron diffraction, x-ray photoelectron spectroscopy and diffraction, and scanning tunneling microscopy experiments. X-ray photoelectron spectroscopy ; Low-energy electron diffraction ; Stoichiometry ; Annealing (glass) ; Vanadium ; Diffraction ; Electron diffraction ; Crystallography ; Scanning tunneling microscope ; Materials science ; Transition metal ; Epitaxy ; Octahedron ; Lattice (music) ; Layer (electronics) ; Chemistry ; Nanotechnology ; Chemical engineering ; Crystal structure ; Metallurgy ; Physical chemistry ; Optics ; Physics ; Catalysis 2D Materials and Applications ; MXene and MAX Phase Materials ; Advanced Thermoelectric Materials and Devices
Authors: Dipendra Jha | Saransh Singh | Reda Al-Bahrani | Wei‐keng Liao | Alok Choudhary | Marc De Graef | Ankit Agrawal
Source: Microscopy and Microanalysis (article, 2018-10-01)
Field: Materials Science — Materials Chemistry

Extracting Grain Orientations from EBSD Patterns of Polycrystalline Materials Using Convolutional Neural Networks We present a deep learning approach to the indexing of electron backscatter diffraction (EBSD) patterns. We design and implement a deep convolutional neural network architecture to predict crystal orientation from the EBSD patterns. We design a differentiable approximation to the disorientation function between the predicted crystal orientation and the ground truth; the deep learning model optimizes for the mean disorientation error between the predicted crystal orientation and the ground truth using stochastic gradient descent. The deep learning model is trained using 374,852 EBSD patterns of polycrystalline nickel from simulation and evaluated using 1,000 experimental EBSD patterns of polycrystalline nickel. The deep learning model results in a mean disorientation error of 0.548° compared to 0.652° using dictionary based indexing. Electron backscatter diffraction ; Crystallite ; Artificial intelligence ; Convolutional neural network ; Ground truth ; Materials science ; Orientation (vector space) ; Deep learning ; Computer science ; Pattern recognition (psychology) ; Geometry ; Composite material ; Mathematics ; Microstructure ; Metallurgy Machine Learning in Materials Science ; Non-Destructive Testing Techniques ; Hydrogen embrittlement and corrosion behaviors in metals
Authors: Pavlo Golub | Sergei Manzhos
Source: Physical Chemistry Chemical Physics (article, 2018-11-29)
Field: Materials Science — Materials Chemistry

Kinetic energy densities based on the fourth order gradient expansion: performance in different classes of materials and improvement<i>via</i>machine learning We study the performance of fourth-order gradient expansions of the kinetic energy density (KED) in semi-local kinetic energy functionals depending on the density-dependent variables. The formal fourth-order expansion is convergent for periodic systems and small molecules but does not improve over the second-order expansion (the Thomas-Fermi term plus one-ninth of the von Weizsäcker term). Linear fitting of the expansion coefficients somewhat improves on the formal expansion. The tuning of the fourth order expansion coefficients allows for better reproducibility of the Kohn-Sham kinetic energy density than the tuning of the second-order expansion coefficients alone. The possibility of a much more accurate match with the Kohn-Sham kinetic energy density by using neural networks (NN) trained using the terms of the 4th order expansion as density-dependent variables is demonstrated. We obtain ultra-low fitting errors without overfitting of NN parameters. Small single hidden layer neural networks can provide good accuracy in separate KED fits of each compound, while for joint fitting of KEDs of multiple compounds multiple hidden layers were required to achieve good fit quality. The critical issue of data distribution is highlighted. We also show the critical role of pseudopotentials in the performance of the expansion, where in the case of a too rapid decay of the valence density at the nucleus with some pseudopotentials, numeric instabilities can arise. Kinetic energy ; Energy (signal processing) ; Order (exchange) ; Energy density ; Statistical physics ; Physics ; Classical mechanics ; Theoretical physics ; Quantum mechanics Machine Learning in Materials Science ; Thermal properties of materials ; Advanced Chemical Physics Studies
Authors: Matti Hellström | Vanessa Quaranta | Jörg Behler
Source: Chemical Science (article, 2018-11-05)
Field: Materials Science — Materials Chemistry

One-dimensional <i>vs.</i> two-dimensional proton transport processes at solid–liquid zinc-oxide–water interfaces Long-range charge transport is important for many applications like batteries, fuel cells, sensors, and catalysis. Obtaining microscopic insights into the atomistic mechanism is challenging, in particular if the underlying processes involve protons as the charge carriers. Here, large-scale reactive molecular dynamics simulations employing an efficient density-functional-theory-based neural network potential are used to unravel long-range proton transport mechanisms at solid-liquid interfaces, using the zinc oxide-water interface as a prototypical case. We find that the two most frequently occurring ZnO surface facets, (101[combining macron]0) and (112[combining macron]0), that typically dominate the morphologies of zinc oxide nanowires and nanoparticles, show markedly different proton conduction behaviors along the surface with respect to the number of possible proton transfer mechanisms, the role of the solvent for long-range proton migration, as well as the proton transport dimensionality. Understanding such surface-facet-specific mechanisms is crucial for an informed bottom-up approach for the functionalization and application of advanced oxide materials. Proton ; Zinc ; Molecular dynamics ; Oxide ; Liquid water ; Range (aeronautics) ; Materials science ; Chemical physics ; Proton transport ; Chemical engineering ; Chemistry ; Nanotechnology ; Computational chemistry ; Physics ; Thermodynamics ; Metallurgy ; Composite material ; Engineering ; Nuclear physics Electronic and Structural Properties of Oxides ; Electrochemical Analysis and Applications ; Machine Learning in Materials Science
Authors: Diego López Barreiro | Jingjie Yeo | Anna Tarakanova | Francisco J. Martín‐Martínez | Markus J. Buehler
Source: Macromolecular Bioscience (review, 2018-10-30)
Field: Materials Science — Biomaterials

Multiscale Modeling of Silk and Silk‐Based Biomaterials—A Review Silk embodies outstanding material properties and biologically relevant functions achieved through a delicate hierarchical structure. It can be used to create high-performance, multifunctional, and biocompatible materials through mild processes and careful rational material designs. To achieve this goal, computational modeling has proven to be a powerful platform to unravel the causes of the excellent mechanical properties of silk, to predict the properties of the biomaterials derived thereof, and to assist in devising new manufacturing strategies. Fine-scale modeling has been done mainly through all-atom and coarse-grained molecular dynamics simulations, which offer a bottom-up description of silk. In this work, a selection of relevant contributions of computational modeling is reviewed to understand the properties of natural silk, and to the design of silk-based materials, especially combined with experimental methods. Future research directions are also pointed out, including approaches such as 3D printing and machine learning, that may enable a high throughput design and manufacturing of silk-based biomaterials. SILK ; Polymer science ; Fibroin ; Nanotechnology ; Materials science ; Composite material Silk-based biomaterials and applications ; Collagen: Extraction and Characterization ; Metal Forming Simulation Techniques
Authors: Remco Hartkamp | Anne-Laure Biance | Li Fu | Jean-François Dufrêche | Oriane Bonhomme | Laurent Joly
Source: Current Opinion in Colloid & Interface Science (article, 2018-08-23)
Field: Chemistry — Physical and Theoretical Chemistry

Measuring surface charge: Why experimental characterization and molecular modeling should be coupled Characterization (materials science) ; Reliability (semiconductor) ; Coupling (piping) ; Molecular dynamics ; Surface (topology) ; Experimental data ; Charge (physics) ; Scale (ratio) Electrostatics and Colloid Interactions ; Nanopore and Nanochannel Transport Studies ; Electrowetting and Microfluidic Technologies
Authors: Rebecca L. Whetton | Toby Waine | Abdul Mounem Mouazen
Source: Biosystems Engineering (article, 2018-02-07)
Field: Chemistry — Analytical Chemistry

Hyperspectral measurements of yellow rust and fusarium head blight in cereal crops: Part 2: On-line field measurement Rust (programming language) ; Fusarium ; Hyperspectral imaging ; Fungicide ; Gibberella zeae ; Partial least squares regression ; Agronomy ; Horticulture ; Mathematics ; Biology ; Statistics ; Remote sensing ; Geography ; Computer science Spectroscopy and Chemometric Analyses ; Mycotoxins in Agriculture and Food ; Plant Pathogens and Fungal Diseases