jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Mahdi Bodaghi | A.R. Damanpack | Wei‐Hsin Liao
Source: Smart Materials and Structures (article, 2018-04-06)
Field: Materials Science — Polymers and Plastics

Triple shape memory polymers by 4D printing This article aims at introducing triple shape memory polymers (SMPs) by four-dimensional (4D) printing technology and shaping adaptive structures for mechanical/bio-medical devices. The main approach is based on arranging hot-cold programming of SMPs with fused decomposition modeling technology to engineer adaptive structures with triple shape memory effect (SME). Experiments are conducted to characterize elasto-plastic and hyper-elastic thermo-mechanical material properties of SMPs in low and high temperatures at large deformation regime. The feasibility of the dual and triple SMPs with self-bending features is demonstrated experimentally. It is advantageous in situations either where it is desired to perform mechanical manipulations on the 4D printed objects for specific purposes or when they experience cold programming inevitably before activation. A phenomenological 3D constitutive model is developed for quantitative understanding of dual/triple SME of SMPs fabricated by 4D printing in the large deformation range. Governing equations of equilibrium are established for adaptive structures on the basis of the nonlinear Green-Lagrange strains. They are then solved by developing a finite element approach along with an elastic-predictor plastic-corrector return map procedure accomplished by the Newton-Raphson method. The computational tool is applied to simulate dual/triple SMP structures enabled by 4D printing and explore hot-cold programming mechanisms behind material tailoring. It is shown that the 4D printed dual/triple SMPs have great potential in mechanical/bio-medical applications such as self-bending gripers/stents and self-shrinking/tightening staples. Shape-memory polymer ; Bending ; 3D printing ; Materials science ; Dual (grammatical number) ; Nonlinear system ; Computer science ; Deformation (meteorology) ; Mechanical engineering ; Finite element method ; Structural engineering ; Shape-memory alloy ; Composite material ; Engineering ; Physics Polymer composites and self-healing ; Advanced Materials and Mechanics ; Innovations in Concrete and Construction Materials
Authors: Geng Sun | Philippe Sautet
Source: Journal of the American Chemical Society (article, 2018-02-09)
Field: Materials Science — Materials Chemistry

Metastable Structures in Cluster Catalysis from First-Principles: Structural Ensemble in Reaction Conditions and Metastability Triggered Reactivity Reactivity studies on catalytic transition metal clusters are usually performed on a single global minimum structure. With the example of a Pt<sub>13</sub> cluster under a pressure of hydrogen, we show from first-principle calculations that low energy metastable structures of the cluster can play a major role for catalytic reactivity and that hence consideration of the global minimum structure alone can severely underestimate the activity. The catalyst is fluxional with an ensemble of metastable structures energetically accessible at reaction conditions. A modified genetic algorithm is proposed to comprehensively search for the low energy metastable ensemble (LEME) structures instead of merely the global minimum structure. In order to reduce the computational cost of density functional calculations, a high dimensional neural network potential is employed to accelerate the exploration. The presence and influence of LEME structures during catalysis is discussed by the example of H covered Pt<sub>13</sub> clusters for two reactions of major importance: hydrogen evolution reaction and methane activation. The results demonstrate that although the number of accessible metastable structures is reduced under reaction condition for Pt<sub>13</sub> clusters, these metastable structures can exhibit high activity and dominate the observed activity due to their unique electronic or structural properties. This underlines the necessity of thoroughly exploring the LEME structures in catalysis simulations. The approach enables one to systematically address the impact of isomers in catalysis studies, taking into account the high adsorbate coverage induced by reaction conditions. Metastability ; Chemistry ; Reactivity (psychology) ; Catalysis ; Cluster (spacecraft) ; Chemical physics ; Density functional theory ; Molecular dynamics ; Computational chemistry ; Organic chemistry Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Catalysis and Oxidation Reactions
Authors: Prasanna V. Balachandran | Antoine Emery | J. E. Gubernatis | Turab Lookman | Chris Wolverton | Alex Zunger
Source: Physical Review Materials (article, 2018-04-11)
Field: Materials Science — Materials Chemistry

Predictions of new <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:mi mathvariant="italic">AB</mml:mi><mml:msub><mml:mi mathvariant="normal">O</mml:mi><mml:mn>3</mml:mn></mml:msub></mml:mrow></mml:math> perovskite compounds by combining machine learning and density functional theory We apply machine learning (ML) methods to a database of 390 experimentally reported $AB{\mathrm{O}}_{3}$ compounds to construct two statistical models that predict possible new perovskite materials and possible new cubic perovskites. The first ML model classified the 390 compounds into 254 perovskites and 136 that are not perovskites with a 90% average cross-validation (CV) accuracy; the second ML model further classified the perovskites into 22 known cubic perovskites and 232 known noncubic perovskites with a 94% average CV accuracy. We find that the most effective chemical descriptors affecting our classification include largely geometric constructs such as the $A$ and $B$ Shannon ionic radii, the tolerance and octahedral factors, the $A$-O and $B$-O bond length, and the $A$ and $B$ Villars' Mendeleev numbers. We then construct an additional list of $625AB{\mathrm{O}}_{3}$ compounds assembled from charge conserving combinations of $A$ and $B$ atoms absent from our list of known compounds. Then, using the two ML models constructed on the known compounds, we predict that 235 of the 625 exist in a perovskite structure with a confidence greater than 50% and among them that 20 exist in the cubic structure (albeit, the latter with only $\ensuremath{\sim}50%$ confidence). We find that the new perovskites are most likely to occur when the $A$ and $B$ atoms are a lanthanide or actinide, when the $A$ atom is an alkali, alkali earth, or late transition metal atom, or when the $B$ atom is a $p$-block atom. We also compare the ML findings with the density functional theory calculations and convex hull analyses in the Open Quantum Materials Database (OQMD), which predicts the $T=0$ K ground-state stability of all the $AB{\mathrm{O}}_{3}$ compounds. We find that OQMD predicts 186 of 254 of the perovskites in the experimental database to be thermodynamically stable within 100 meV/atom of the convex hull and predicts 87 of the 235 ML-predicted perovskite compounds to be thermodynamically stable within 100 meV/atom of the convex hull, including 6 of these to be in cubic structures. We suggest these 87 as the most promising candidates for future experimental synthesis of novel perovskites. Atom (system on chip) ; Ionic radius ; Perovskite (structure) ; Alkali metal ; Octahedron ; Lanthanide ; Materials science ; Crystallography ; Crystal structure ; Physics ; Chemistry ; Ion ; Computer science ; Quantum mechanics Machine Learning in Materials Science ; Advanced Condensed Matter Physics ; X-ray Diffraction in Crystallography
Authors: Nasreen Khan | Blair Brettmann
Source: Polymers (review, 2018-12-31)
Field: Chemistry — Organic Chemistry

Intermolecular Interactions in Polyelectrolyte and Surfactant Complexes in Solution Polyelectrolytes are an important class of polymeric materials and are increasingly used in complex industrial formulations. A core use of these materials is in mixtures with surfactants, where a combination of hydrophobic and electrostatic interactions drives unique solution behavior and structure formation. In this review, we apply a molecular level perspective to the broad literature on polyelectrolyte-surfactant complexes, discussing explicitly the hydrophobic and electrostatic interaction contributions to polyelectrolyte surfactant complexes (PESCs), as well as the interplay between the two molecular interaction types. These interactions are sensitive to a variety of solution conditions, such as pH, ionic strength, mixing procedure, charge density, etc. and these parameters can readily be used to control the concentration at which structures form as well as the type of structure in the bulk solution. Polyelectrolyte ; Pulmonary surfactant ; Intermolecular force ; Ionic strength ; Chemical physics ; Electrostatics ; Ionic bonding ; Hydrophobic effect ; Chemistry ; Electrostatic interaction ; Materials science ; Molecule ; Chemical engineering ; Computational chemistry ; Aqueous solution ; Polymer ; Organic chemistry ; Physical chemistry ; Ion Surfactants and Colloidal Systems ; Electrostatics and Colloid Interactions ; Environmental Chemistry and Analysis
Authors: Candie Xie | Jingyong Liu | Xiaochun Zhang | Wuming Xie | Jian Sun | Ken‐Lin Chang | Jia‐Hong Kuo | Wenhao Xie | Chao Liu | Shuiyu Sun | Musa Büyükada | Fatih Evrendilek
Source: Applied Energy (article, 2018-01-04)
Field: Materials Science — Materials Chemistry

Co-combustion thermal conversion characteristics of textile dyeing sludge and pomelo peel using TGA and artificial neural networks Thermogravimetric analysis ; Combustion ; Dyeing ; Materials science ; Heat of combustion ; Textile ; Chemical engineering ; Waste management ; Chemistry ; Composite material ; Engineering ; Organic chemistry Thermal and Kinetic Analysis ; Thermochemical Biomass Conversion Processes
Authors: Guadalupe G. Flores‐Rojas | Felipe López‐Saucedo | Emilio Bucio
Source: Radiation Physics and Chemistry (review, 2018-08-16)
Field: Materials Science — Materials Chemistry

Gamma-irradiation applied in the synthesis of metallic and organic nanoparticles: A short review Nanoparticle ; Radical ; Polymer ; Organic synthesis ; Polymerization ; Monomer ; Metal ; Materials science ; Nanotechnology ; Absorption (acoustics) ; Metal ions in aqueous solution ; Chemistry ; Aqueous solution ; Combinatorial chemistry ; Chemical engineering ; Organic chemistry ; Catalysis Nanoparticles: synthesis and applications ; Laser-Ablation Synthesis of Nanoparticles ; TiO2 Photocatalysis and Solar Cells
Authors: Daniel R. Cassar | André C. P. L. F. de Carvalho | Edgar Dutra Zanotto
Source: Acta Materialia (article, 2018-08-17)
Field: Materials Science — Ceramics and Composites

Predicting glass transition temperatures using neural networks Glass transition ; Artificial neural network ; Materials science ; Hyperparameter ; Generalization ; Thermodynamics ; Biological system ; Machine learning ; Computer science ; Mathematics ; Composite material ; Polymer ; Physics Glass properties and applications ; Phase-change materials and chalcogenides ; Material Dynamics and Properties
Authors: A. Miguel | Volker L. Deringer | Jari Koskinen | Tomi Laurila | Gábor Cśanyi
Source: Physical Review Letters (article, 2018-04-18)
Field: Materials Science — Materials Chemistry

Growth Mechanism and Origin of High <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"><mml:mi>s</mml:mi><mml:msup><mml:mi>p</mml:mi><mml:mn>3</mml:mn></mml:msup></mml:math> Content in Tetrahedral Amorphous Carbon We study the deposition of tetrahedral amorphous carbon (ta-C) films from molecular dynamics simulations based on a machine-learned interatomic potential trained from density-functional theory data. For the first time, the high sp^{3} fractions in excess of 85% observed experimentally are reproduced by means of computational simulation, and the deposition energy dependence of the film's characteristics is also accurately described. High confidence in the potential and direct access to the atomic interactions allow us to infer the microscopic growth mechanism in this material. While the widespread view is that ta-C grows by "subplantation," we show that the so-called "peening" model is actually the dominant mechanism responsible for the high sp^{3} content. We show that pressure waves lead to bond rearrangement away from the impact site of the incident ion, and high sp^{3} fractions arise from a delicate balance of transitions between three- and fourfold coordinated carbon atoms. These results open the door for a microscopic understanding of carbon nanostructure formation with an unprecedented level of predictive power. Physics ; Algorithm ; Computer science Diamond and Carbon-based Materials Research ; Graphene research and applications ; Boron and Carbon Nanomaterials Research
Authors: Jin Woo Choi | Won‐Jin Moon
Source: Korean Journal of Radiology (review, 2018-12-31)
Field: Materials Science — Materials Chemistry

Gadolinium Deposition in the Brain: Current Updates Gadolinium-based contrast agents (GBCAs) are commonly used for enhancement in MR imaging and have long been considered safe when administered at recommended doses. However, since the report that nephrogenic systemic fibrosis is linked to the use of GBCAs in subjects with severe renal diseases, accumulating evidence has suggested that GBCAs are not cleared entirely from our bodies; some GBCAs are deposited in our tissues, including the brain. GBCA deposition in the brain is mostly linked to the specific chelate structure of the GBCA: linear GBCAs were responsible for brain deposition in almost all reported studies. This review aimed to summarize the current knowledge about GBCA brain deposition and discuss its clinical implications. Medicine ; Gadolinium ; Current (fluid) ; Deposition (geology) ; Radiology ; Nuclear medicine ; Oceanography ; Paleontology Lanthanide and Transition Metal Complexes ; Advanced MRI Techniques and Applications ; Medical Imaging Techniques and Applications
Authors: Paul Z. Hanakata | Ekin D. Cubuk | David Campbell | Harold S. Park
Source: Physical Review Letters (article, 2018-12-20)
Field: Materials Science — Materials Chemistry

Accelerated Search and Design of Stretchable Graphene Kirigami Using Machine Learning Making kirigami-inspired cuts into a sheet has been shown to be an effective way of designing stretchable materials with metamorphic properties where the 2D shape can transform into complex 3D shapes. However, finding the optimal solutions is not straightforward as the number of possible cutting patterns grows exponentially with system size. Here, we report on how machine learning (ML) can be used to approximate the target properties, such as yield stress and yield strain, as a function of cutting pattern. Our approach enables the rapid discovery of kirigami designs that yield extreme stretchability as verified by molecular dynamics (MD) simulations. We find that convolutional neural networks, commonly used for classification in vision tasks, can be applied for regression to achieve an accuracy close to the precision of the MD simulations. This approach can then be used to search for optimal designs that maximize elastic stretchability with only 1000 training samples in a large design space of ∼4×10^{6} candidate designs. This example demonstrates the power and potential of ML in finding optimal kirigami designs at a fraction of iterations that would be required of a purely MD or experiment-based approach, where no prior knowledge of the governing physics is known or available. Graphene ; Materials science ; Computer science ; Nanotechnology Graphene research and applications ; Advanced Memory and Neural Computing ; Advanced Sensor and Energy Harvesting Materials
Authors: Ziletti, A. | Kumar, D. | Scheffler, M. | Ghiringhelli, L.
Source: MPG.PuRe (Max Planck Society) (article, 2018-01-01)
Field: Materials Science — Materials Chemistry

Insightful classification of crystal structures using deep learning Computational methods that automatically extract knowledge from data are critical for enabling data-driven materials science. A reliable identification of lattice symmetry is a crucial first step for materials characterization and analytics. Current methods require a user-specified threshold, and are unable to detect "average symmetries" for defective structures. Here, we propose a new machine-learning-based approach to automatically classify structures by crystal symmetry. First, we represent crystals by a diffraction image, and then construct a deep-learning neural-network model for classification. Our approach is able to correctly classify a dataset comprising more than 80,000 structures, including heavily defective ones. The internal operations of the neural network are unraveled through attentive response maps, demonstrating that it uses the same landmarks a materials scientist would use, although never explicitly instructed to do so. Our study paves the way for crystal-structure recognition in computational and experimental big-data materials science. Computer science ; Artificial intelligence ; Artificial neural network ; Deep learning ; Construct (python library) ; Big data ; Identification (biology) ; Machine learning ; Lattice (music) ; Pattern recognition (psychology) ; Data mining ; Physics Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Advanced X-ray and CT Imaging
Authors: Andrea Rizzi | Steven Murkli | J. Nolan McNeill | Wei Yao | Matthew R. Sullivan | Michael K. Gilson | Michael Chiu | Lyle Isaacs | Bruce C. Gibb | David L. Mobley | John D. Chodera
Source: Journal of Computer-Aided Molecular Design (article, 2018-10-01)
Field: Chemistry — Physical and Theoretical Chemistry

Overview of the SAMPL6 host–guest binding affinity prediction challenge Binding affinities ; Supramolecular chemistry ; Macromolecule ; Computer science ; Variety (cybernetics) ; Chemistry ; Host (biology) ; Solvent models ; Affinities ; Biochemical engineering ; Computational chemistry ; Molecule ; Machine learning ; Artificial intelligence ; Stereochemistry ; Biology ; Organic chemistry ; Engineering Crystallography and molecular interactions ; Protein Structure and Dynamics ; Machine Learning in Materials Science
Authors: Tiffany Porta Siegel | Grégory Hamm | Josephine Bunch | Jo Cappell | John S. Fletcher | Kristina Schwamborn
Source: Molecular Imaging and Biology (review, 2018-08-30)
Field: Chemistry — Spectroscopy

Mass Spectrometry Imaging and Integration with Other Imaging Modalities for Greater Molecular Understanding of Biological Tissues Over the last two decades, mass spectrometry imaging (MSI) has been increasingly employed to investigate the spatial distribution of a wide variety of molecules in complex biological samples. MSI has demonstrated its potential in numerous applications from drug discovery, disease state evaluation through proteomic and/or metabolomic studies. Significant technological and methodological advancements have addressed natural limitations of the techniques, i.e., increased spatial resolution, increased detection sensitivity especially for large molecules, higher throughput analysis and data management. One of the next major evolutions of MSI is linked to the introduction of imaging mass cytometry (IMC). IMC is a multiplexed method for tissue phenotyping, imaging signalling pathway or cell marker assessment, at sub-cellular resolution (1 μm). It uses MSI to simultaneously detect and quantify up to 30 different antibodies within a tissue section. The combination of MSI with other molecular imaging techniques can also provide highly relevant complementary information to explore new scientific fields. Traditionally, classical histology (especially haematoxylin and eosin-stained sections) is overlaid with molecular profiles obtained by MSI. Thus, MSI-based molecular histology provides a snapshot of a tissue microenvironment and enables the correlation of drugs, metabolites, lipids, peptides or proteins with histological/pathological features or tissue substructures. Recently, many examples combining MSI with other imaging modalities such as fluorescence, confocal Raman spectroscopy and MRI have emerged. For instance, brain pathophysiology has been studied using both MRI and MSI, establishing correlations between in and ex vivo molecular imaging techniques. Endogenous metabolite and small peptide modulation were evaluated depending on disease state. Here, we review advanced 'hot topics' in MSI development and explore the combination of MSI with established molecular imaging techniques to improve our understanding of biological and pathophysiological processes. Mass spectrometry imaging ; Molecular imaging ; Modalities ; Mass spectrometry ; Medical physics ; Computer science ; Medicine ; Chemistry ; Biology ; Chromatography Mass Spectrometry Techniques and Applications ; Ion-surface interactions and analysis ; Nuclear Physics and Applications
Authors: Anna M. J. Coenen | Katrien V. Bernaerts | Jules Harings | Stefan Jockenhoevel | Samaneh Ghazanfari
Source: Acta Biomaterialia (review, 2018-08-28)
Field: Materials Science — Biomaterials

Elastic materials for tissue engineering applications: Natural, synthetic, and hybrid polymers Materials science ; Polymer ; Tissue engineering ; Natural polymers ; Composite material ; Polymer science ; Nanotechnology ; Biomedical engineering ; Engineering biodegradable polymer synthesis and properties ; Electrospun Nanofibers in Biomedical Applications ; Tissue Engineering and Regenerative Medicine
Authors: Magdalena Ola Cichocka | Jonas Ångström | Bin Wang | Xiaodong Zou | Stef Smeets
Source: Journal of Applied Crystallography (article, 2018-11-22)
Field: Materials Science — Materials Chemistry

High-throughput continuous rotation electron diffraction data acquisition<i>via</i>software automation Single-crystal electron diffraction (SCED) is emerging as an effective technique to determine and refine the structures of unknown nano-sized crystals. In this work, the implementation of the continuous rotation electron diffraction (cRED) method for high-throughput data collection is described. This is achieved through dedicated software that controls the transmission electron microscope and the camera. Crystal tracking can be performed by defocusing every <i>n</i>th diffraction pattern while the crystal rotates, which addresses the problem of the crystal moving out of view of the selected area aperture during rotation. This has greatly increased the number of successful experiments with larger rotation ranges and turned cRED data collection into a high-throughput method. The experimental parameters are logged, and input files for data processing software are written automatically. This reduces the risk of human error, and makes data collection more reproducible and accessible for novice and irregular users. In addition, it is demonstrated how data from the recently developed serial electron diffraction technique can be used to supplement the cRED data collection by automatic screening for suitable crystals using a deep convolutional neural network that can identify promising crystals through the corresponding diffraction data. The screening routine and cRED data collection are demonstrated using a sample of the zeolite mordenite, and the quality of the cRED data is assessed on the basis of the refined crystal structure. Software ; Computer science ; Diffraction ; Data collection ; Automation ; Rotation (mathematics) ; Throughput ; Electron diffraction ; Crystal (programming language) ; Optics ; Materials science ; Artificial intelligence ; Physics ; Statistics ; Engineering ; Mathematics ; Telecommunications X-ray Diffraction in Crystallography ; Enzyme Structure and Function ; Hydrocarbon exploration and reservoir analysis