jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Maliheh Hajiramezanali | Fatemeh Atyabi | Mona Mosayebnia | Mehdi Akhlaghi | Parham Geramifar | Amir Reza Jalilian | Seyed Mohammad Mazidi | Hassan Yousefnia | Soraya Shahhosseini | Davood Beiki
Source: International Journal of Nanomedicine (article, 2019-04-01)
Field: Materials Science — Biomaterials

&lt;p&gt;&lt;sup&gt;68&lt;/sup&gt;Ga-radiolabeled bombesin-conjugated to trimethyl chitosan-coated superparamagnetic nanoparticles for molecular imaging: preparation, characterization and biological evaluation&lt;/p&gt; <sup>68</sup>Ga-DOTA-BN-TMC-MNPs could be a potential diagnostic probe to detect BC using PET/MRI technique. Nuclear chemistry ; DOTA ; Biodistribution ; Materials science ; Bombesin ; Superparamagnetism ; Nanoparticle ; Chitosan ; MRI contrast agent ; Magnetic nanoparticles ; Chemistry ; Radiochemistry ; Chelation ; Nanotechnology ; In vitro ; Biochemistry ; Magnetization Nanoparticle-Based Drug Delivery ; Radiopharmaceutical Chemistry and Applications ; Lanthanide and Transition Metal Complexes
Authors: Andressa Trentin | Andressa de L. Gasparini | Flávio A. Faria | Samarah Vargas Harb | Fábio C. dos Santos | Sandra H. Pulcinelli | Celso V. Santilli | Peter Hammer
Source: Progress in Organic Coatings (article, 2019-10-30)
Field: Materials Science — Polymers and Plastics

Barrier properties of high performance PMMA-silica anticorrosion coatings Materials science ; Dielectric spectroscopy ; Polymerization ; Chemical engineering ; Thermal stability ; Composite material ; Methacrylate ; Nanocomposite ; Methyl methacrylate ; Benzoyl peroxide ; Electrolyte ; Electrochemistry ; Polymer ; Electrode Conducting polymers and applications ; Corrosion Behavior and Inhibition ; Analytical Chemistry and Sensors
Authors: S. Rajasekaran | Hossein Bakhshi Khaniki
Source: Applied Mathematical Modelling (article, 2019-03-15)
Field: Materials Science — Materials Chemistry

Size-dependent forced vibration of non-uniform bi-directional functionally graded beams embedded in variable elastic environment carrying a moving harmonic mass Vibration ; Parametric statistics ; Beam (structure) ; Harmonic ; Mathematical analysis ; Material properties ; Moving load ; Mechanics ; Nonlinear system ; Mathematics ; Timoshenko beam theory ; Finite element method ; Structural engineering ; Materials science ; Physics ; Engineering ; Acoustics Nonlocal and gradient elasticity in micro/nano structures ; Composite Structure Analysis and Optimization ; Numerical methods in engineering
Authors: Carmine Autieri | Paolo Barone | Jagoda Sławińska | Silvia Picozzi
Source: Physical Review Materials (article, 2019-08-19)
Field: Materials Science — Materials Chemistry

Persistent spin helix in Rashba-Dresselhaus ferroelectric <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:msub><mml:mrow><mml:mi>CsBiNb</mml:mi></mml:mrow><mml:mn>2</mml:mn></mml:msub><mml:msub><mml:mrow><mml:mi mathvariant="normal">O</mml:mi></mml:mrow><mml:mn>7</mml:mn></mml:msub></mml:math> Ferroelectric Rashba semiconductors (FERSC) are a novel class of multifunctional materials showing a giant Rashba spin splitting which can be reversed by switching the electric polarization. Although they are excellent candidates as channels in spin field effect transistors, the experimental research has been limited so far to semiconducting GeTe, in which ferroelectric switching is often prevented by heavy doping and/or large leakage currents. Here, we report that ${\mathrm{CsBiNb}}_{2}{\mathrm{O}}_{7}$, a layered perovskite of Dion-Jacobson type, is a robust ferroelectric with sufficiently strong spin-orbit coupling and spin texture reversible by electric field. Moreover, we reveal that its topmost valence band's spin texture is quasi-independent from the momentum, as a result of the low symmetry of its ferroelectric phase. The peculiar spin-polarization pattern in the momentum space may yield the so-called ``persistent spin helix,'' a specific spin-wave mode which protects the spin from decoherence in the diffusive transport regime, potentially ensuring a very long spin lifetime in this material. Ferroelectricity ; Condensed matter physics ; Materials science ; Electric field ; Spin polarization ; Spin (aerodynamics) ; Physics ; Optoelectronics ; Quantum mechanics ; Dielectric ; Electron Electronic and Structural Properties of Oxides ; Ferroelectric and Piezoelectric Materials ; Multiferroics and related materials
Authors: Andrea D. Merg | Gavin Touponse | Eric van Genderen | Xiaobing Zuo | Alisina Bazrafshan | Thorsten B. Blum | Spencer Hughes | Khalid Salaita | Jan Pieter Abrahams | Vincent P. Conticello
Source: Angewandte Chemie International Edition (review, 2019-07-10)
Field: Materials Science — Biomaterials

2D Crystal Engineering of Nanosheets Assembled from Helical Peptide Building Blocks The successful integration of 2D nanomaterials into functional devices hinges on developing fabrication methods that afford hierarchical control across length scales of the entire assembly. We demonstrate structural control over a class of crystalline 2D nanosheets assembled from collagen triple helices. By lengthening the triple helix unit through sequential additions of Pro-Hyp-Gly triads, we achieved sub-angstrom tuning over the 2D lattice. These subtle changes influence the overall nanosheet size, which can be adjusted across the mesoscale size regime. The internal structure was observed by cryo-TEM with direct electron detection, which provides real-space high-resolution images, in which individual triple helices comprising the lattice can be clearly discerned. These results establish a general strategy for tuning the structural hierarchy of 2D nanomaterials that employ rigid, cylindrical structural units. Nanosheet ; Nanomaterials ; Hinge ; Materials science ; Nanotechnology ; Fabrication ; Crystallography ; Crystal structure ; Triple helix ; Angstrom ; Lattice (music) ; Hierarchy ; Self-assembly ; Chemistry ; Physics ; Stereochemistry ; Engineering ; Mechanical engineering Collagen: Extraction and Characterization ; Bone Tissue Engineering Materials ; Bone and Dental Protein Studies
Authors: Fábio Machado Cavalcanti | Martín Schmal | Reinaldo Giudici | Rita M.B. Alves
Source: Journal of Environmental Management (article, 2019-03-01)
Field: Materials Science — Materials Chemistry

A catalyst selection method for hydrogen production through Water-Gas Shift Reaction using artificial neural networks Water-gas shift reaction ; Catalysis ; Artificial neural network ; Hydrogen production ; Selection (genetic algorithm) ; Environmental science ; Production (economics) ; Hydrogen ; Process engineering ; Chemistry ; Biochemical engineering ; Waste management ; Computer science ; Engineering ; Artificial intelligence ; Organic chemistry Machine Learning in Materials Science ; Catalysis and Hydrodesulfurization Studies ; Catalysis and Oxidation Reactions
Authors: Şule Atahan-Evrenk | F. Betul Atalay
Source: The Journal of Physical Chemistry A (article, 2019-06-03)
Field: Materials Science — Materials Chemistry

Prediction of Intramolecular Reorganization Energy Using Machine Learning Facile charge transport is desired for many applications of organic semiconductors (OSCs). To take advantage of high-throughput screening methodologies for the discovery of novel OSCs, parameters relevant to charge transport are of high interest. The intramolecular reorganization energy (RE) is one of the important charge transport parameters suitable for molecular-level screening. Because the calculation of the RE with quantum-chemical methods is expensive for large-scale screening, we investigated the possibility of predicting the RE from the molecular structure by means of machine learning methods. We combinatorially generated a molecular library of 5631 molecules with extended conjugated backbones using benzene, thiophene, furan, pyrrole, pyridine, pyridazine, and cyclopentadiene as building blocks and obtained the target electronic data at the B3LYP level of theory with the 6-31G* basis set. We compared ridge, kernel ridge, and deep neural net (DNN) regression models based on graph- and geometry-based descriptors. We found that DNNs outperform the other methods and can predict the RE with a coefficient of determination of 0.92 and root-mean-square error of ∼12 meV. This study shows that the REs of organic semiconductor molecules can be predicted from the molecular structures with high accuracy. Intramolecular force ; Computer science ; Quantum chemistry ; Organic semiconductor ; Molecule ; Computational chemistry ; Artificial intelligence ; Materials science ; Chemistry ; Supramolecular chemistry ; Organic chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Organic Electronics and Photovoltaics
Authors: Jin Hyun Chang | David Kleiven | Marko Melander | Jaakko Akola | Juan Maria Garcia-Lastra | Tejs Vegge
Source: Journal of Physics Condensed Matter (article, 2019-04-23)
Field: Materials Science — Materials Chemistry

CLEASE: a versatile and user-friendly implementation of cluster expansion method Materials exhibiting a substitutional disorder such as multicomponent alloys and mixed metal oxides/oxyfluorides are of great importance in many scientific and technological sectors. Disordered materials constitute an overwhelmingly large configurational space, which makes it practically impossible to be explored manually using first-principles calculations such as density functional theory due to the high computational costs. Consequently, the use of methods such as cluster expansion (CE) is vital in enhancing our understanding of the disordered materials. CE dramatically reduces the computational cost by mapping the first-principles calculation results on to a Hamiltonian which is much faster to evaluate. In this work, we present our implementation of the CE method, which is integrated as a part of the atomic simulation environment (ASE) open-source package. The versatile and user-friendly code automates the complex set up and construction procedure of CE while giving the users the flexibility to tweak the settings and to import their own structures and previous calculation results. Recent advancements such as regularization techniques from machine learning are implemented in the developed code. The code allows the users to construct CE on any bulk lattice structure, which makes it useful for a wide range of applications involving complex materials. We demonstrate the capabilities of our implementation by analyzing the two example materials with varying complexities: a binary metal alloy and a disordered lithium chromium oxyfluoride. Cluster expansion ; Binary number ; Hamiltonian (control theory) ; Cluster (spacecraft) ; Density functional theory ; Lattice (music) ; Flexibility (engineering) ; Code (set theory) Machine Learning in Materials Science ; Inorganic Chemistry and Materials ; X-ray Diffraction in Crystallography
Authors: Fanchen Kong | Yefei Li | Cheng Shang | Zhi‐Pan Liu
Source: The Journal of Physical Chemistry C (article, 2019-06-26)
Field: Materials Science — Materials Chemistry

Stability and Phase Transition of Cobalt Oxide Phases by Machine Learning Global Potential Energy Surface Cobalt oxides (CoOx), despite its wide applications, are notoriously complex on structure, in particular under experimental reduction/oxidation conditions. Here, using the newly developed machine learning method, stochastic surface walking global optimization in combination with global neural network potential, we are able to, for the first time, explore the global potential energy surface (PES) of CoOx at different Co/O ratios. Rich information on the thermodynamics and kinetics of CoOx is thus gleaned from more than 107 PES data, which helps to resolve the long-standing puzzles on CoOx chemistry. We show that (i) only CoO and Co3O4 are thermodynamically stable compositions in CoOx, whereas Co3O4 is the most stable phase. The trivalent Co2O3, although having a well-defined global minimum, tends to decompose to Co3O4 and O2 at finite temperatures. (ii) The solid phase transition between wurtzite CoO (h-CoO) and rock salt CoO (c-CoO) follows the reconstructive phase transition mechanism with a high barrier. Because a high temperature is required for transition, the strong preference of structural defects inside c-CoO instead of h-CoO contributes to the one-way solid-phase transition from h-CoO to c-CoO. (iii) It is c-CoO that can achieve a coherent interface with Co3O4 in forming a biphasic junction, which implies the reversibility of Co3O4 and c-CoO transition under reduction/oxidation conditions. Our results demonstrate the power of global neural network potential in material discovery for fast exploration of polymorphism and transition kinetics and lay the structural foundation for understanding CoOx applications. Cobalt oxide ; Phase transition ; Materials science ; Oxide ; Cobalt ; Wurtzite crystal structure ; Artificial neural network ; Transition metal ; Kinetics ; Chemical physics ; Thermodynamics ; Computer science ; Chemistry ; Catalysis ; Physics ; Artificial intelligence ; Metallurgy Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Copper-based nanomaterials and applications
Authors: Kyle Mills | Kevin Ryczko | Iryna Luchak | Adam Domurad | Chris Beeler | Isaac Tamblyn
Source: Chemical Science (article, 2019-01-01)
Field: Materials Science — Materials Chemistry

Extensive deep neural networks for transferring small scale learning to large scale systems We present a physically-motivated topology of a deep neural network that can efficiently infer extensive parameters (such as energy, entropy, or number of particles) of arbitrarily large systems, doing so with scaling. We use a form of domain decomposition for training and inference, where each sub-domain (tile) is comprised of a non-overlapping focus region surrounded by an overlapping context region. The size of these regions is motivated by the physical interaction length scales of the problem. We demonstrate the application of EDNNs to three physical systems: the Ising model and two hexagonal/graphene-like datasets. In the latter, an EDNN was able to make total energy predictions of a 60 atoms system, with comparable accuracy to density functional theory (DFT), in 57 milliseconds. Additionally EDNNs are well suited for massively parallel evaluation, as no communication is necessary during neural network evaluation. We demonstrate that EDNNs can be used to make an energy prediction of a two-dimensional 35.2 million atom system, over 1.0 μm<sup>2</sup> of material, at an accuracy comparable to DFT, in under 25 minutes. Such a system exists on a length scale visible with optical microscopy and larger than some living organisms. Scale (ratio) ; Deep learning ; Deep neural networks ; Artificial neural network ; Artificial intelligence ; Computer science ; Machine learning ; Geography ; Cartography Machine Learning in Materials Science ; Neural Networks and Applications ; Machine Learning and ELM
Authors: Xinyu Wang | Dan Han | Yang Hong | Haiyi Sun | Jingzhi Zhang | Jingchao Zhang
Source: ACS Omega (article, 2019-06-11)
Field: Materials Science — Materials Chemistry

Machine Learning Enabled Prediction of Mechanical Properties of Tungsten Disulfide Monolayer One of two-dimensional transition metal dichalcogenide materials, tungsten disulfide (WS<sub>2</sub>), has aroused much research interest, and its mechanical properties play an important role in a practical application. Here the mechanical properties of h-WS<sub>2</sub> and t-WS<sub>2</sub> monolayers in the armchair and zigzag directions are evaluated by utilizing the molecular dynamics (MD) simulations and machine learning (ML) technique. We mainly focus on the effects of chirality, system size, temperature, strain rate, and random vacancy defect on mechanical properties, including fracture strain, fracture strength, and Young's modulus. We find that the mechanical properties of h-WS<sub>2</sub> surpass those of t-WS<sub>2</sub> due to the different coordination spheres of the transition metal atoms. It can also be observed that the fracture strain, fracture strength, and Young's modulus decrease when temperature and vacancy defect ratio are enhanced. The random forest (RF) supervised ML algorithm is employed to model the correlations between different impact factors and target outputs. A total number of 3600 MD simulations are performed to generate the training and testing dataset for the ML model. The mechanical properties of WS<sub>2</sub> (i.e., target outputs) can be predicted using the trained model with the knowledge of different input features, such as WS<sub>2</sub> type, chirality, temperature, strain rate, and defect ratio. The mean square errors of ML predictions for the mechanical properties are orders of magnitude smaller than the actual values of each property, indicating good training results of the RF model. Tungsten disulfide ; Zigzag ; Materials science ; Vacancy defect ; Monolayer ; Modulus ; Tungsten ; Fracture (geology) ; Chirality (physics) ; Composite material ; Condensed matter physics ; Nanotechnology ; Mathematics ; Metallurgy ; Physics ; Geometry 2D Materials and Applications ; MXene and MAX Phase Materials ; Graphene research and applications
Authors: Dorián László Galata | Attila Farkas | Zsófia Könyves | Lilla Alexandra Mészáros | Edina Szabó | István Csontos | Andrea Pálos | György Marosi | Zsombor Kristóf Nagy | Brigitta Nagy
Source: Pharmaceutics (article, 2019-08-09)
Field: Chemistry — Analytical Chemistry

Fast, Spectroscopy-Based Prediction of In Vitro Dissolution Profile of Extended Release Tablets Using Artificial Neural Networks The pharmaceutical industry has never seen such a vast development in process analytical methods as in the last decade. The application of near-infrared (NIR) and Raman spectroscopy in monitoring production lines has also become widespread. This work aims to utilize the large amount of information collected by these methods by building an artificial neural network (ANN) model that can predict the dissolution profile of the scanned tablets. An extended release formulation containing drotaverine (DR) as a model drug was developed and tablets were produced with 37 different settings, with the variables being the DR content, the hydroxypropyl methylcellulose (HPMC) content and compression force. NIR and Raman spectra of the tablets were recorded in both the transmission and reflection method. The spectra were used to build a partial least squares prediction model for the DR and HPMC content. The ANN model used these predicted values, along with the measured compression force, as input data. It was found that models based on both NIR and Raman spectra were capable of predicting the dissolution profile of the test tablets within the acceptance limit of the f<sub>2</sub> difference factor. The performance of these ANN models was compared to PLS models using the same data as input, and the prediction of the ANN models was found to be more accurate. The proposed method accomplishes the prediction of the dissolution profile of extended release tablets using either NIR or Raman spectra. Raman spectroscopy ; Partial least squares regression ; Dissolution ; Artificial neural network ; Near-infrared spectroscopy ; Biological system ; Dissolution testing ; Chemometrics ; Materials science ; Computer science ; Artificial intelligence ; Chemistry ; Machine learning ; Optics ; Physics Spectroscopy and Chemometric Analyses ; Drug Solubulity and Delivery Systems ; Analytical Methods in Pharmaceuticals
Authors: Jan‐Joris Devogelaer | Sander J. T. Brugman | Hugo Meekes | Paul Tinnemans | Elias Vlieg | R. De Gelder
Source: CrystEngComm (article, 2019-01-01)
Field: Materials Science — Materials Chemistry

Cocrystal design by network-based link prediction Cocrystals are predicted using a network of coformers extracted from the CSD. Cocrystal ; Link (geometry) ; Computer science ; Chemistry ; Combinatorial chemistry ; Computer network ; Molecule ; Organic chemistry Machine Learning in Materials Science ; Crystallography and molecular interactions ; Protein Structure and Dynamics
Authors: І. V. Konovalenko | Pavlo Maruschak | Janette Brezinová | Jozef Brezina
Source: Materials (article, 2019-06-26)
Field: Materials Science — Materials Chemistry

Morphological Characteristics of Dimples of Ductile Fracture of VT23M Titanium Alloy and Identification of Dimples on Fractograms of Different Scale The authors developed a method for the automated detection and calculation of quantitative parameters of dimples of ductile fracture on the digital images of fracture surfaces obtained at different scales. The processing algorithm of fractographic images was proposed, which allowed high quality recognition of the shape and size of dimples to be achieved, taking into account the morphological features of their digital images. The developed method for identifying dimples of various physical and morphological characteristics was tested on the VT23M alloy. The test results showed that the method meets the quality requirements for the automated diagnostics of fracture mechanisms of titanium alloys. Dimple ; Fracture (geology) ; Materials science ; Alloy ; Titanium alloy ; Scale (ratio) ; Titanium ; Metallurgy ; Composite material Material Properties and Failure Mechanisms ; Engineering Diagnostics and Reliability ; Geotechnical and Geomechanical Engineering
Authors: Владимир П. Баклаушев | В. Г. Богуш | Vladimir A. Kalsin | N. N. Sovetnikov | Ekaterina M. Samoilova | V. A. Revkova | К. В. Сидорук | Mikhail Konoplyannikov | Peter Timashev | Svetlana Kotova | Konstantin B. Yushkov | Alexander Averyanov | A. V. Troitskiy | J.-E. Ahlfors
Source: Scientific Reports (article, 2019-02-28)
Field: Materials Science — Biomaterials

Tissue Engineered Neural Constructs Composed of Neural Precursor Cells, Recombinant Spidroin and PRP for Neural Tissue Regeneration We have designed a novel two-component matrix (SPRPix) for the encapsulation of directly reprogrammed human neural precursor cells (drNPC). The matrix is comprised of 1) a solid anisotropic complex scaffold prepared by electrospinning a mixture of recombinant analogues of the spider dragline silk proteins - spidroin 1 (rS1/9) and spidroin 2 (rS2/12) - and polycaprolactone (PCL) (rSS-PCL), and 2) a "liquid matrix" based on platelet-rich plasma (PRP). The combination of PRP and spidroin promoted drNPC proliferation with the formation of neural tissue organoids and dramatically activated neurogenesis. Differentiation of drNPCs generated large numbers of βIII-tubulin and MAP2 positive neurons as well as some GFAP-positive astrocytes, which likely had a neuronal supporting function. Interestingly the SPRPix microfibrils appeared to provide strong guidance cues as the differentiating neurons oriented their processes parallel to them. Implantation of the SPRPix matrix containing human drNPC into the brain and spinal cord of two healthy Rhesus macaque monkeys showed good biocompatibility: no astroglial and microglial reaction was present around the implanted construct. Importantly, the human drNPCs survived for the 3 month study period and differentiated into MAP2 positive neurons. Tissue engineered constructs based on SPRPix exhibits important attributes that warrant further examination in spinal cord injury treatment. Tissue engineering ; Cell biology ; Neurogenesis ; Matrix (chemical analysis) ; Chemistry ; Scaffold ; Organoid ; Biocompatibility ; Neural stem cell ; Spinal cord ; Anatomy ; Biology ; Biomedical engineering ; Neuroscience ; Medicine ; Stem cell Silk-based biomaterials and applications ; Nerve injury and regeneration ; Electrospun Nanofibers in Biomedical Applications