jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Ola Svahn | Erland Björklund
Source: Molecules (article, 2019-04-11)
Field: Chemistry — Analytical Chemistry

High Flow-Rate Sample Loading in Large Volume Whole Water Organic Trace Analysis Using Positive Pressure and Finely Ground Sand as a SPE-Column In-Line Filter By using an innovative, positive pressure sample loading technique in combination with an in-line filter of finely ground sand the bottleneck of solid phase extraction (SPE) can be reduced. Recently published work by us has shown the proof of concept of the technique. In this work, emphasis is put on the SPE flow rate and method validation for 26 compounds of emerging environmental concern, mainly from the 1st and 2nd EU Watch List, with various physicochemical properties. The mean absolute recoveries in % and relative standard deviations (RSD) in % for the investigated compounds from spiked pure water samples at the three investigated flow rates of 10, 20, and 40 mL/min were 63.2% (3.2%), 66.9% (3.3%), and 69.0% (4.0%), respectively. All three flow rates produced highly repeatable results, and this allowed a flow rate increase of up to 40 mL/min for a 200 mg, 6 mL, reversed phase SPE cartridge without compromising the recoveries. This figure is more than four times the maximum flow rate recommended by manufacturers. It was indicated that some compounds, especially pronounced for the investigated macrolide molecules, might suffer when long contact times with the sample glass bottle occurs. A reduced contact time somewhat decreases this complication. A very good repeatability also held true for experiments on both spiked matrix-rich pond water (high and low concentrations) and recipient waters (river and wastewater) applying 40 mL/min. This work has shown that, for a large number of compounds of widely differing physicochemical properties, there is a generous flow rate window from 10 to 40 mL/min where sample loading can be conducted. A sample volume of 0.5 L, which at the recommended maximum flow rate speed of 10 mL/min, would previously take 50 min, can now be processed in 12 min using a flow rate of 40 mL/min. This saves 38 min per processed sample. This low-cost technology allows the sample to be transferred to the SPE-column, closer to the sample location and by the person taking the sample. This further means that only the sample cartridge would need to be sent to the laboratory, instead of the whole water sample, like today's procedure. Volumetric flow rate ; Repeatability ; Chromatography ; Solid phase extraction ; Chemistry ; Volume (thermodynamics) ; Analytical Chemistry (journal) ; Extraction (chemistry) ; Wastewater ; Cartridge ; Matrix (chemical analysis) ; Filter (signal processing) ; Materials science ; Environmental science ; Environmental engineering ; Thermodynamics Analytical chemistry methods development ; Analytical Chemistry and Chromatography ; Mass Spectrometry Techniques and Applications
Authors: Ankur Chaurasia | Shahriar Sajjadi
Source: Lab on a Chip (article, 2019-01-01)
Field: Materials Science — Biomaterials

Transformable bubble-filled alginate microfibers<i>via</i>vertical microfluidics A novel buoyancy-assisted vertical microfluidic setup has been developed to fabricate a new class of transformable bubble-filled hydrogel microfibers. A co-axial flow of an aqueous sodium-alginate solution enveloping an air phase was injected into a quiescent aqueous CaCl2 solution, through a vertically-oriented co-axial glass-capillary setup. This induced instantaneous gelation and produced bubble-filled calcium-alginate fibers. The surface-morphology of the resulting fibers was controlled from smooth to wavy by slowing down the gelation kinetics. The advantage of the buoyancy force acting on the fibers by the trapped air bubbles was taken not only to shape the fibers, but to transform them into several other novel hydrogel structures, such as water-filled segmented fibers, beaded microfibers, and threaded capsules. The ultimate transformability was demonstrated by the fibers being allowed to elongate and then undergo controlled destruction to produce uniform anisotropic micro-particles with a wide range of sizes and shapes from frustums to barrel and cylindrical types. Microfiber ; Bubble ; Microfluidics ; Nanotechnology ; Engineering ; Materials science ; Physics ; Mechanics ; Composite material Electrospun Nanofibers in Biomedical Applications ; Surface Modification and Superhydrophobicity ; Advanced Sensor and Energy Harvesting Materials
Authors: Michela F. Picardi | Anatoly V. Zayats | Francisco J. Rodríguez‐Fortuño
Source: Laser & Photonics Review (article, 2019-11-13)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Amplitude and Phase Control of Guided Modes Excitation from a Single Dipole Source: Engineering Far‐ and Near‐Field Directionality Abstract The design of far‐field radiation diagrams from combined electric and magnetic dipolar sources has recently found applications in nanophotonic metasurfaces that realize tailored reflection and refraction. Such dipolar sources also exhibit important near‐field evanescent coupling properties with applications in polarimetry and quantum optics. Here, a rigorous theoretical framework is introduced for engineering the angular spectra encompassing both far‐ and near‐fields of electric and magnetic sources and a unified description of both free space and guided mode directional radiation is developed. The approach uses the full parametric space of six complex‐valued components of magnetic and electric dipoles in order to engineer constructive or destructive near‐field interference. Such dipolar sources can be realized with dielectric or plasmonic nanoparticles. It is shown how a single dipolar source can be designed to achieve the selective coupling to multiple waveguide modes and far‐field simultaneously with a desired amplitude, phase, and direction. Physics ; Dipole ; Near and far field ; Optics ; Metamaterial ; Magnetic dipole ; Nanophotonics ; Excitation ; Amplitude ; Angular spectrum method ; Dipole antenna ; Computational physics ; Diffraction ; Antenna (radio) ; Computer science ; Telecommunications ; Quantum mechanics Metamaterials and Metasurfaces Applications ; Plasmonic and Surface Plasmon Research ; Orbital Angular Momentum in Optics
Authors: Youyuan Wang | Yudong Li | Zhanxi Zhang | Haisen Zhao | Yanfang Zhang
Source: Applied Sciences (article, 2019-10-01)
Field: Materials Science — Polymers and Plastics

Repair Performance of Self-Healing Microcapsule/Epoxy Resin Insulating Composite to Physical Damage Minor physical damage can reduce the insulation performance of epoxy resin, which seriously threatens the reliability of electrical equipment. In this paper, the epoxy resin insulating composite was prepared by a microcapsule system to achieve its self-healing goal. The repair performance to physical damage was analyzed by the tests of scratch, cross-section damage, electric tree, and breakdown strength. The results show that compared with pure epoxy resin, the composite has the obvious self-healing performance. For mechanical damage, the maximum repair rate of physical structure is 100%, and the breakdown strength can be restored to 83% of the original state. For electrical damage, microcapsule can not only attract the electrical tree and inhibit its propagation process, but also repair the tubules of electrical tree effectively. Moreover, the repair rate is fast, which meets the application requirements of epoxy resin insulating material. In addition, the repair behavior is dominated by capillarity and molecular diffusion on the defect surface. Furthermore, the electrical properties of repaired part are greatly affected by the characteristics of damage interface and repair product. In a word, the composite shows better repair performance to physical damage, which is conducive to improving the reliability of electrical insulating materials. Epoxy ; Materials science ; Composite material ; Composite number Polymer composites and self-healing ; High voltage insulation and dielectric phenomena ; Corrosion Behavior and Inhibition
Authors: Mohamed Ghouse Shaik | K. Vijayarekha
Source: Energies (article, 2019-10-03)
Field: Materials Science — Materials Chemistry

Investigation of Surface Degradation of Aged High Temperature Vulcanized (HTV) Silicone Rubber Insulators Polymeric composite insulators are subjected to varying work conditions like rain and heat, which create an impact on degradation during their long service period. Electrical tracking under the Alternating Current (AC) field plays a predominant role in surface degradation, which can be different for fresh and aged insulations. The tracking studies on the fresh and aged polymeric insulation therefore become significant. Motivated by this, an indigenous low-cost electrical tracking setup was developed, and the tracking studies were carried out as per International Electro technical Commission standard (IEC) 60587 on fresh, thermal-aged and water-aged silicone rubber samples. Contact angles of samples were measured to analyse the effect of ageing on hydrophobicity. Further, to analyse the influence of ageing on insulation integrity, tracking tests were conducted and parameters like leakage current pattern and magnitudes, tracking length and loss of weight in the material due to tracking were examined. The physicochemical impacts of ageing on the surface degradation of the samples were also analysed using X-ray diffraction analysis and Fourier Transform Infrared Spectroscopy analysis. The investigations added insight into the degradation mechanism of polymeric insulators in terms of their electrical performance and physicochemical changes in the material. Comparison of these changes showed that ageing could influence surface degradation of samples. Silicone rubber ; Materials science ; Composite material ; Vulcanization ; Degradation (telecommunications) ; Accelerated aging ; Natural rubber ; Forensic engineering ; Electrical engineering High voltage insulation and dielectric phenomena ; Electrical Fault Detection and Protection ; Power Transformer Diagnostics and Insulation
Authors: Maximilian Amsler | Logan Ward | Vinay I. Hegde | Maarten G. Goesten | Xia Yi | Chris Wolverton
Source: Physical Review Materials (article, 2019-03-26)
Field: Materials Science — Materials Chemistry

Ternary mixed-anion semiconductors with tunable band gaps from machine-learning and crystal structure prediction We report the computational investigation of a series of ternary ${X}_{4}{Y}_{2}Z$ and ${X}_{5}{Y}_{2}{Z}_{2}$ compounds with $X$=Mg, Ca, Sr, Ba; $Y$=P, As, Sb, Bi; and $Z$=S, Se, Te. The compositions for these materials were predicted through a search guided by machine learning, while the structures were resolved using the minima hopping crystal structure prediction method. Based on ab initio calculations, we predict that many of these phases are thermodynamically stable. In particular, 21 of the ${X}_{4}{Y}_{2}Z$ compounds crystallize in a tetragonal structure with $I\overline{4}2d$ symmetry, and exhibit band gaps in the range of 0.8 and 1.8 eV, well suited for various energy applications. We show that several candidates (in particular ${X}_{4}{Y}_{2}\mathrm{Te}$ and ${X}_{4}{\mathrm{Sb}}_{2}\mathrm{Se}$) exhibit good photo absorption in the visible range, while others (e.g., ${\mathrm{Ba}}_{4}{\mathrm{Sb}}_{2}\mathrm{Se}$) show excellent thermoelectric performance due to high power factors and extremely low lattice thermal conductivities. Ternary operation ; Crystal structure prediction ; Tetragonal crystal system ; Band gap ; Crystal structure ; Electronic band structure ; Ab initio ; Lattice (music) ; Semiconductor Advanced Thermoelectric Materials and Devices ; Machine Learning in Materials Science ; Heusler alloys: electronic and magnetic properties
Authors: Daniel C. Elton | Zois Boukouvalas | Mark Fuge | Peter W. Chung
Source: arXiv (Cornell University) (review, 2019-03-11)
Field: Materials Science — Materials Chemistry

Deep learning for molecular generation and optimization - a review of the state of the art In the space of only a few years, deep generative modeling has revolutionized how we think of artificial creativity, yielding autonomous systems which produce original images, music, and text. Inspired by these successes, researchers are now applying deep generative modeling techniques to the generation and optimization of molecules - in our review we found 45 papers on the subject published in the past two years. These works point to a future where such systems will be used to generate lead molecules, greatly reducing resources spent downstream synthesizing and characterizing bad leads in the lab. In this review we survey the increasingly complex landscape of models and representation schemes that have been proposed. The four classes of techniques we describe are recursive neural networks, autoencoders, generative adversarial networks, and reinforcement learning. After first discussing some of the mathematical fundamentals of each technique, we draw high level connections and comparisons with other techniques and expose the pros and cons of each. Several important high level themes emerge as a result of this work, including the shift away from the SMILES string representation of molecules towards more sophisticated representations such as graph grammars and 3D representations, the importance of reward function design, the need for better standards for benchmarking and testing, and the benefits of adversarial training and reinforcement learning over maximum likelihood based training. Generative grammar ; Artificial intelligence ; Reinforcement learning ; Computer science ; Adversarial system ; Deep learning ; Representation (politics) ; Benchmarking ; Creativity ; Cognitive science ; Function (biology) ; Machine learning ; Data science Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Linlin Zhong
Source: Journal of Applied Physics (article, 2019-05-08)
Field: Chemistry — Spectroscopy

Fast prediction of electron-impact ionization cross sections of large molecules via machine learning The theoretical determination of electron-impact ionization cross section (Qion) for a molecule requires ab initio computation, which is time-consuming for large molecules. We propose a machine learning based method to construct a model for predicting Qion of large molecules without the high-cost ab initio calculation. The model is learned from the data composed of the calculated Qion of the small molecules with fewer constituent atoms and the electron numbers of the corresponding molecules in a train set by a support vector machine. The radial basis function is set as a kernel function to map data to a higher dimensional space. The grid search with 5-fold cross-validation is performed to find optimal hyperparameters in the learning model. The prediction on the test sets composed of CF4, C3F8, SF6, C6, C6F12, and C6F12O shows that this data-driven model can generate well-agreed Qion and has good generalization performance. Hyperparameter optimization ; Ab initio ; Test set ; Ionization ; Generalization ; Hyperparameter ; Support vector machine ; Molecule ; Computation ; Computer science ; Set (abstract data type) ; Function (biology) ; Kernel (algebra) ; Cross-validation ; Chemistry ; Artificial intelligence ; Algorithm ; Mathematics Mass Spectrometry Techniques and Applications ; Analytical Chemistry and Chromatography ; Analytical chemistry methods development
Authors: Aditya Nandy | Jiazhou Zhu | Jon Paul Janet | Chenru Duan | Rachel B. Getman | Heather J. Kulik
Source: ChemRxiv (preprint, 2019-05-28)
Field: Materials Science — Materials Chemistry

Machine Learning Accelerates the Discovery of Design Rules and Exceptions in Stable Metal-Oxo Intermediate Formation Metal-oxo moieties are important catalytic intermediates in the selective partial oxidation of hydrocarbons and in water splitting. Stable metal-oxo species have reactive properties that vary depending on the spin state of the metal, complicating the development of structure-property relationships. To overcome these challenges, we train the first machine learning (ML) models capable of predicting metal-oxo formation energies across a range of first-row metals, oxidation states, and spin states. Using connectivity-only features tailored for inorganic chemistry as inputs to kernel ridge regression or artificial neural network ML models, we achieve good mean absolute errors (4-5 kcal/mol) on set-aside test data across a range of ligand orientations. Analysis of feature importance for oxo formation energy prediction reveals the dominance of non-local, electronic ligand properties in contrast to other transition metal complex properties (e.g., spin-state or ionization potential). We enumerate the theoretical catalyst space with an ANN, revealing both expected trends in oxo formation energetics, such as destabilization of the metal-oxo species with increasing d-filling, as well as exceptions, such as weak correlations with indicators of oxidative stability of the metal in the resting state or unexpected spin-state dependence in reactivity. We carry out uncertainty aware evolutionary optimization using the ANN to explore a > 37,000 candidate catalyst space. New metal and oxidation state combinations are uncovered and validated with density functional theory (DFT), including counter-intuitive oxo-formation energies for oxidatively stable complexes. This approach doubles the density of confirmed DFT leads in originally sparsely populated regions of property space, highlighting the potential of ML-model-driven discovery to uncover catalyst design rules and exceptions. Computer science ; Artificial intelligence ; Chemistry Machine Learning in Materials Science
Authors: Prabhas Hundi | Rouzbeh Shahsavari
Source: Small (article, 2019-04-10)
Field: Materials Science — Materials Chemistry

Deep Learning to Speed up the Development of Structure–Property Relations For Hexagonal Boron Nitride and Graphene Structure-property maps play a key role in accelerated materials discovery. The current norm for developing these maps includes computationally expensive physics-based simulations. Here, the capabilities of deep learning agents are explored such as convolutional neural networks (CNNs) and multilayer perceptrons (MLPs) to predict structure-property relations and reduce dependence on simulations. This study contains simulated hexagonal boron nitride (h-BN) microstructures damaged by various levels of radiation and temperature, with the objective to predict their residual strengths from the final atomic positions. By developing low dimensional physical descriptors to statistically describe the defects, these results show that purpose-specific microstructure representation can help in achieving a good prediction accuracy at low computational cost. Furthermore, the adaptability of the trained deep learning agents is explored to predict structure-property maps of other 2D materials using transfer learning. It is shown that in order to achieve good predictions accuracy (≈95% R<sup>2</sup> ), an agent that is training for the first time ("learning from scratch") requires 23-45% of simulated data, whereas an agent adapting to a different material ("transfer learning") requires only about 10% or less. This suggests that transfer learning is a potential game changer in material discovery and characterization approaches. Transfer of learning ; Convolutional neural network ; Materials science ; Graphene ; Artificial intelligence ; Boron nitride ; Deep learning ; Computer science ; Hexagonal boron nitride ; Property (philosophy) ; Adaptability ; Machine learning ; Nanotechnology Machine Learning in Materials Science ; Graphene research and applications ; Electronic and Structural Properties of Oxides
Authors: Alireza Rahnama | Seetharaman Sridhar
Source: Materialia (article, 2019-06-06)
Field: Materials Science — Materials Chemistry

Application of data science tools to determine feature correlation and cluster metal hydrides for hydrogen storage Hydrogen storage ; Cluster analysis ; Outlier ; Materials science ; Hydride ; Hydrogen ; Cluster (spacecraft) ; Range (aeronautics) ; Kernel (algebra) ; Data mining ; Metal ; Computer science ; Mathematics ; Artificial intelligence ; Chemistry ; Metallurgy ; Alloy Hydrogen Storage and Materials ; Hybrid Renewable Energy Systems ; Ammonia Synthesis and Nitrogen Reduction
Authors: Zhilei Wang | Toshio Ogawa | Yoshitaka Adachi
Source: ISIJ International (article, 2019-07-12)
Field: Materials Science — Materials Chemistry

Properties-to-microstructure-to-processing Inverse Analysis for Steels via Machine Learning As traditional experimental science appears to be inefficient for designing novel materials with desired properties because of the complex combination of processing conditions and chemical compositions, data-driven materials science is becoming increasingly important for materials design. A properties-to-microstructure-to-processing inverse analysis for steels is attempted via a machine learning approach in this work, where a potential best balanced property of tensile strength (TS) and total elongation (tEL) TS × tEL and its corresponding microstructure and processing conditions are explored using a genetic algorithm, which is implemented by an independently developed machine learning tool called the Materials Genome Integration System Phase and Property Analysis (MIPHA). The results demonstrate that a property-to-microstructure/processing method is sufficient to identify a best model performance, potential TS × tEL, and a reasonable relationship description among the processing, microstructure and property. A microstructure with Widmanstatten ferrite, banite, and martensite is found to be beneficial to a good balanced property. Microstructure ; Martensite ; Property (philosophy) ; Ferrite (magnet) ; Materials science ; Ultimate tensile strength ; Inverse ; Mechanical engineering ; Computer science ; Metallurgy ; Composite material ; Mathematics ; Engineering ; Geometry Machine Learning in Materials Science ; Microstructure and Mechanical Properties of Steels ; Hydrogen embrittlement and corrosion behaviors in metals
Authors: Robert M. Elder | Alessio Zaccone | Timothy W. Sirk
Source: ACS Macro Letters (article, 2019-08-22)
Field: Materials Science — Materials Chemistry

Identifying Nonaffine Softening Modes in Glassy Polymer Networks: A Pathway to Chemical Design Using molecular simulations and theory, we develop an explicit mapping of the contribution of molecular relaxation modes in glassy thermosets to the shear modulus, where the relaxations were tuned by altering the polarity of side groups. Specifically, motions at the domain, segmental, monomer, and atomic levels are taken from molecular dynamics snapshots and directly linked with the viscoelasticity through a framework based in the lattice dynamics of amorphous solids. This unique approach provides direct insight into the roles of chemical groups in the stress response, including the time scale and spatial extent of relaxations during mechanics. Two thermoset networks with differing concentrations of polar side groups were examined, dicyclopentadiene (DCPD) and 5-norbornene-2-methanol (NBOH). A machine learning method is found to be effective for quantifying large-scale correlated motions, while more local chemical relaxations are readily identified by direct inspection. The approach is broadly applicable and enables rapid predictions of the frequency-dependent modulus for any glass. Viscoelasticity ; Thermosetting polymer ; Molecular dynamics ; Amorphous solid ; Polymer ; Softening ; Stress relaxation ; Relaxation (psychology) ; Shear modulus Material Dynamics and Properties ; Polymer composites and self-healing ; Polymer crystallization and properties