jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: M. Gayatri Vineela | Abhishek Dave | Phaneendra Kiran Chaganti
Source: Materials Today Proceedings (article, 2018-01-01)
Field: Materials Science — Polymers and Plastics

Artificial Neural Network based Prediction of Tensile Strength of Hybrid Composites Ultimate tensile strength ; Epoxy ; Materials science ; Composite material ; Artificial neural network ; Fiber ; Composite number ; Characterization (materials science) ; Volume fraction ; Glass fiber ; Tensile testing ; Test data ; Computer science ; Machine learning Natural Fiber Reinforced Composites ; Mechanical Behavior of Composites ; Textile materials and evaluations
Authors: Daniel W. Davies | Keith T. Butler | Olexandr Isayev | Aron Walsh
Source: Faraday Discussions (article, 2018-01-01)
Field: Materials Science — Materials Chemistry

Materials discovery by chemical analogy: role of oxidation states in structure prediction The likelihood of an element to adopt a specific oxidation state in a solid, given a certain set of neighbours, might often be obvious to a trained chemist. However, encoding this information for use in high-throughput searches presents a significant challenge. We carry out a statistical analysis of the occurrence of oxidation states in 16 735 ordered, inorganic compounds and show that a large number of cations are only likely to exhibit certain oxidation states in combination with particular anions. We use this data to build a model that ascribes probabilities to the formation of hypothetical compounds, given the proposed oxidation states of their constituent species. The model is then used as part of a high-throughput materials design process, which significantly narrows down the vast compositional search space for new ternary metal halide compounds. Finally, we employ a machine learning analysis of existing compounds to suggest likely structures for a small subset of the candidate compositions. We predict two new compounds, MnZnBr4 and YSnF7, that are thermodynamically stable according to density functional theory, as well as four compounds, MnCdBr4, MnRu2Br8, ScZnF5 and ZnCoBr4, which lie within the window of metastability. Analogy ; Statistical physics ; Chemistry ; Physics ; Epistemology ; Philosophy Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Computational Drug Discovery Methods
Authors: Ole Schütt | Joost VandeVondele
Source: Journal of Chemical Theory and Computation (article, 2018-06-29)
Field: Materials Science — Materials Chemistry

Machine Learning Adaptive Basis Sets for Efficient Large Scale Density Functional Theory Simulation It is chemically intuitive that an optimal atom centered basis set must adapt to its atomic environment, for example by polarizing toward nearby atoms. Adaptive basis sets of small size can be significantly more accurate than traditional atom centered basis sets of the same size. The small size and well conditioned nature of these basis sets leads to large saving in computational cost, in particular in a linear scaling framework. Here, it is shown that machine learning can be used to predict such adaptive basis sets using local geometrical information only. As a result, various properties of standard DFT calculations can be easily obtained at much lower costs, including nuclear gradients. In our approach, a rotationally invariant parametrization of the basis is obtained by employing a potential anchored on neighboring atoms to ultimately construct a rotation matrix that turns a traditional atom centered basis set into a suitable adaptive basis set. The method is demonstrated using MD simulations of liquid water, where it is shown that minimal basis sets yield structural properties in fair agreement with basis set converged results, while reducing the computational cost in the best case by a factor of 200 and the required flops by 4 orders of magnitude. Already a very small training set yields satisfactory results as the variational nature of the method provides robustness. Basis (linear algebra) ; Basis set ; Basis function ; Computer science ; STO-nG basis sets ; Scaling ; Robustness (evolution) ; Parametrization (atmospheric modeling) ; Algorithm ; Invariant (physics) ; Statistical physics ; Mathematics ; Density functional theory ; Quantum mechanics ; Physics ; Geometry ; Chemistry Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Computational Drug Discovery Methods
Authors: Haitian Zhang | Zhen Zhang | Hua Zhou | Hidekazu Tanaka | Dillon D. Fong | Shriram Ramanathan
Source: Advances in Physics X (article, 2018-10-23)
Field: Materials Science — Materials Chemistry

Beyond electrostatic modification: design and discovery of functional oxide phases via ionic-electronic doping A new research field of functional materials and device physics is rising that combines ionic transport with charge carrier modulation to realize emergent physical properties and discovery of metastable phases. The paradigm for enabling function extends far beyond carrier accumulation or depletion in band semiconductors or simply moving ions through an insulating electrolyte. Rather, by carefully selecting electronically or structurally fragile materials, one can collapse or open band gaps via extreme ionic dopant concentration, or reconfigure their entire crystal structure to create new phases. Electron–electron and electron–lattice interactions can be coupled or controlled independently in such systems via electric fields without thermal constraints by use of ionic dopants. The unifying theme across these studies is to introduce ions and electrons via electric fields through interfaces, with electrochemistry playing a dominant role. In this review, we briefly summarize this nascent field of iontronics and discuss principal results to date with examples from binary and complex oxides as well as selected 2D materials systems. We conclude the review by highlighting gaps in fundamental scientific understanding and prospects for the use of such novel devices in future electronic, photonic and energy technologies. Dopant ; Ionic bonding ; Chemical physics ; Materials science ; Electron ; Doping ; Semiconductor ; Ion ; Electric field ; Nanotechnology ; Band gap ; Charge carrier ; Ambipolar diffusion ; Engineering physics ; Physics ; Optoelectronics Electronic and Structural Properties of Oxides ; Ferroelectric and Piezoelectric Materials ; Gas Sensing Nanomaterials and Sensors
Authors: Pieter Plehiers | Guy Marin | Christian V. Stevens | Kevin M. Van Geem
Source: Journal of Cheminformatics (article, 2018-03-09)
Field: Materials Science — Materials Chemistry

Automated reaction database and reaction network analysis: extraction of reaction templates using cheminformatics Both the automated generation of reaction networks and the automated prediction of synthetic trees require, in one way or another, the definition of possible transformations a molecule can undergo. One way of doing this is by using reaction templates. In view of the expanding amount of known reactions, it has become more and more difficult to envision all possible transformations that could occur in a studied system. Nonetheless, most reaction network generation tools rely on user-defined reaction templates. Not only does this limit the amount of chemistry that can be accounted for in the reaction networks, it also confines the wide-spread use of the tools by a broad public. In retrosynthetic analysis, the quality of the analysis depends on what percentage of the known chemistry is accounted for. Using databases to identify templates is therefore crucial in this respect. For this purpose, an algorithm has been developed to extract reaction templates from various types of chemical databases. Some databases such as the Kyoto Encyclopedia for Genes and Genomes and RMG do not report an atom-atom mapping (AAM) for the reactions. This makes the extraction of a template non-straightforward. If no mapping is available, it is calculated by the Reaction Decoder Tool (RDT). With a correct AAM-either calculated by RDT or specified-the algorithm consistently extracts a correct template for a wide variety of reactions, both elementary and non-elementary. The developed algorithm is a first step towards data-driven generation of synthetic trees or reaction networks, and a greater accessibility for non-expert users. Cheminformatics ; Template ; Computer science ; Retrosynthetic analysis ; Database ; Data mining ; Chemistry ; Computational chemistry ; Total synthesis ; Programming language Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Bioinformatics and Genomic Networks
Authors: Emily Grisanti | Maria Totska | Stefan Huber | Christina Krick Calderon | Monika Hohmann | Dominic Lingenfelser | Matthias Otto
Source: Journal of Spectroscopy (article, 2018-10-28)
Field: Chemistry — Analytical Chemistry

Dynamic Localized SNV, Peak SNV, and Partial Peak SNV: Novel Standardization Methods for Preprocessing of Spectroscopic Data Used in Predictive Modeling An essential part of multivariate analysis in spectroscopic context is preprocessing. The aim of preprocessing is to remove scattering phenomena or disturbances in the spectra due to measurement geometry in order to improve subsequent predictive models. Especially in vibrational spectroscopy, the Standard Normal Variate (SNV) transformation has become very popular and is widely used in many practical applications, but standardization is not always ideal when performed across the full spectrum. Herein, three different new standardization techniques are presented that apply SNV to defined regions rather than to the full spectrum: Dynamic Localized SNV (DLSNV), Peak SNV (PSNV) and Partial Peak SNV (PPSNV). DLSNV is an extension of the Localized SNV (LSNV), which allows a dynamic starting point of the localized windows on which the SNV is executed individually. Peak and Partial Peak SNV are based on picking regions from the spectra with a high correlation to the target value and perform SNV on these essential regions to ensure optimal scatter correction. All proposed methods are able to significantly improve the model performance in cross validation and robustness tests compared to SNV. The prediction errors could be reduced by up to 16% and 29% compared with LSNV for two regression models. Preprocessor ; Standardization ; Univariate ; Robustness (evolution) ; Computer science ; Algorithm ; Artificial intelligence ; Context (archaeology) ; Pattern recognition (psychology) ; Biology ; Multivariate statistics ; Machine learning ; Genetics Spectroscopy and Chemometric Analyses ; Water Quality Monitoring and Analysis ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: Philipp del Hougne | Geoffroy Lerosey
Source: Physical Review X (article, 2018-11-30)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Leveraging Chaos for Wave-Based Analog Computation: Demonstration with Indoor Wireless Communication Signals In sight of fundamental thermal limits on further substantial performance improvements of modern digital computational processing units, wave-based analog computation is becoming an enticing alternative. A wave, as it propagates through a carefully tailored medium, performs the desired computational operation. Yet, the necessary designs are so intricate that experimental demonstrations will necessitate further technological advances. Here, we show that, counterintuitively, the carefully tailored medium can be replaced with a random medium, subject to an appropriate shaping of the incident wave front. Using tunable metasurface reflect-arrays, we demonstrate our concept experimentally in a chaotic microwave cavity. We conclude that off-the-shelf wireless communication infrastructure in combination with a simple reflect-array suffices to perform analog computation with Wi-Fi waves reverberating in a room. Chaotic ; Wireless ; Computation ; CHAOS (operating system) ; Simple (philosophy) ; Signal processing ; Microwave Metamaterials and Metasurfaces Applications ; Advanced Antenna and Metasurface Technologies ; Advanced Wireless Communication Technologies
Authors: En Yang | Shirong Ge | Shibo Wang
Source: Journal of Spectroscopy (article, 2018-11-06)
Field: Chemistry — Analytical Chemistry

Characterization and Identification of Coal and Carbonaceous Shale Using Visible and Near-Infrared Reflectance Spectroscopy Because of the high organic carbon concentration in carbonaceous shale, a large proportion of carbonaceous shales are often misclassified into coals using visible and near-infrared (VIS-NIR) reflectance spectroscopy in the field of coal-gangue identification of hyperspectral remote sensing of coal mine. In order to study spectral characterization of coal and carbonaceous shale, three bituminite samples and three carbonaceous shales were collected from a coal mine of China, and their spectral reflectance curves were obtained by a field spectrometer in the wavelength range of 350–2500 nm. Only one carbonaceous shale could be easily identified from the three bituminite samples according to obvious absorption valleys near 1400 nm, 1900 nm, and 2200 nm of its reflectance curve while the other two carbonaceous shales have similar reflectance curves to the three bituminite samples. The effect of carbon concentration on reflectance curve was simulated by the mixed powder of ultralow ash bituminite and clay in 0.5 mm grain size under various mixing ratios. It was found that absorption valleys near 1400 nm, 1900 nm, and 2200 nm of the mixed powder become not obvious when the bituminite content is more than 30%. In order to establish an effective identification method of coal and carbonaceous shale, 250 other samples collected from the same coal mine were divided into 150 training samples and 100 prediction samples. Principal component analysis (PCA) and Gauss radial basis kernel principal component analysis (GRB-KPCA) were employed to extract principal components (PCs) of continuum removed (CR) spectra of the training samples in eight selected wavelength regions which are related to the main mineral and organic compositions. Two support vector machine- (SVM-) based models PCA-SVM and GRB-KPCA-SVM were established. The results showed that the GRB-KPCA-SVM model had better identification accuracies of 94% and 92% for powder and nature block prediction samples, respectively. Oil shale ; Coal ; Mineralogy ; Carbon fibers ; Principal component analysis ; Diffuse reflectance infrared fourier transform ; Near-infrared spectroscopy ; Maceral ; Analytical Chemistry (journal) ; Geology ; Chemistry ; Materials science ; Environmental chemistry ; Petrography ; Optics ; Composite number Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Geochemistry and Geologic Mapping
Authors: Tyler Rhodes | S. Smolentsev | Mohamed Abdou
Source: Physics of Fluids (article, 2018-05-01)
Field: Materials Science — Materials Chemistry

Magnetohydrodynamic pressure drop and flow balancing of liquid metal flow in a prototypic fusion blanket manifold Understanding magnetohydrodynamic (MHD) phenomena associated with the flow of electrically conducting fluids in complex geometry ducts subject to a strong magnetic field is required to effectively design liquid metal (LM) blankets for fusion reactors. Particularly, accurately predicting the 3D MHD pressure drop and flow distribution is important. To investigate these topics, we simulate a LM MHD flow through an electrically non-conducting prototypic manifold for a wide range of flow and geometry parameters using a 3D MHD solver, HyPerComp incompressible MHD solver for arbitrary geometry. The reference manifold geometry consists of a rectangular feeding duct which suddenly expands such that the duct thickness in the magnetic field direction abruptly increases by a factor rexp. Downstream of the sudden expansion, the LM is distributed into several parallel channels. As a first step in qualifying the flow, a magnitude of the curl of the induced Lorentz force was used to distinguish between inviscid, irrotational core flows and boundary and internal shear layers where inertia and/or viscous forces are important. Scaling laws have been obtained which characterize the 3D MHD pressure drop and flow balancing as a function of the flow parameters and the manifold geometry. Associated Hartmann and Reynolds numbers in the computations were ∼103 and ∼101-103, respectively, while rexp was varied from 4 to 12. An accurate model for the pressure drop was developed for the first time for inertial-electromagnetic and viscous-electromagnetic regimes based on 96 computed cases. Analysis shows that flow balance can be improved by lengthening the distance between the manifold inlet and the entrances of the parallel channels by utilizing the effect of flow transitioning to a quasi-two-dimensional state in the expansion region of the manifold. Physics ; Magnetohydrodynamics ; Mechanics ; Inviscid flow ; Reynolds number ; Lorentz force ; Magnetohydrodynamic drive ; Hartmann number ; Pressure drop ; Classical mechanics ; Pressure gradient ; Magnetic field ; Geometry ; Turbulence ; Mathematics Fusion materials and technologies ; Magnetic confinement fusion research ; Nuclear Engineering Thermal-Hydraulics
Authors: Stuart T. Johnston | Matthew Faria | Edmund J. Crampin
Source: Journal of The Royal Society Interface (article, 2018-07-01)
Field: Materials Science — Biomaterials

An analytical approach for quantifying the influence of nanoparticle polydispersity on cellular delivered dose Nanoparticles provide a promising approach for the targeted delivery of therapeutic, diagnostic and imaging agents in the body. However, it is not yet fully understood how the physico-chemical properties of the nanoparticles influence cellular association and uptake. Cellular association experiments are routinely performed in an effort to determine how nanoparticle properties impact the rate of nanoparticle-cell association. To compare experiments in a meaningful manner, the association data must be normalized by the amount of nanoparticles that arrive at the cells, a measure referred to as the delivered dose. The delivered dose is calculated from a model of nanoparticle transport through fluid. A standard assumption is that all nanoparticles within the population are monodisperse, namely the nanoparticles have the same physico-chemical properties. We present a semi-analytic solution to a modified model of nanoparticle transport that allows for the nanoparticle population to be polydisperse. This solution allows us to efficiently analyse the influence of polydispersity on the delivered dose. Combining characterization data obtained from a range of commonly used nanoparticles and our model, we find that the delivered dose changes by more than a factor of 2 if realistic amounts of polydispersity are considered. Dispersity ; Nanoparticle ; Population ; Nanotechnology ; Materials science ; Biological system ; Chemistry ; Medicine Nanoparticle-Based Drug Delivery ; Coagulation and Flocculation Studies ; Nanoparticles: synthesis and applications
Authors: Jiarui He | Fuliang Sun | Fuhao Han | Junjie Gu | Minrui Ou | Wenkai Xu | Xiaoping Xu
Source: RSC Advances (article, 2018-01-01)
Field: Chemistry — Inorganic Chemistry

Preparation of a novel polyacrylic acid and chitosan interpenetrating network hydrogel for removal of U(<scp>vi</scp>) from aqueous solutions A clean and simple method has been developed for preparation of interpenetrating polymer networks using polyacrylic acid (PAA) and chitosan (CS) for extraction of uranium from polluted water. The peak of Fourier transform infrared spectroscopy (FTIR) occurred at 928 cm<sup>-1</sup> indicating combination of uranium and PAA/CS. The energy dispersive X-ray (EDX) and the scanning electron microscope (SEM) studies illustrated the formation of a crosslinking structure and excellent binding ability of uranium on PAA/CS. The maximum adsorption capacity was 289.6 mg g<sup>-1</sup> calculated using the equation of the Langmuir model. The adsorption capacity reached a plateau at pH 4 and the sorption process fits the pseudo-second-order model well. The PAA/CS composite has stability of reuse, with the adsorbent capacity decreasing slowly with increasing usage frequency. The experimental results confirm that the PAA/CS hydrogel could be a novel alternative for highly efficient removal of uranium from wastewater. Polyacrylic acid ; Chitosan ; Aqueous solution ; Chemistry ; Chemical engineering ; Nuclear chemistry ; Interpenetrating polymer network ; Self-healing hydrogels ; Polymer chemistry ; Materials science ; Polymer ; Organic chemistry ; Engineering Radioactive element chemistry and processing ; Covalent Organic Framework Applications ; Chemical Synthesis and Characterization
Authors: Jia Jin | Quan Wang
Source: IEEE Transactions on Geoscience and Remote Sensing (article, 2018-12-04)
Field: Chemistry — Analytical Chemistry

Selection of Informative Spectral Bands for PLS Models to Estimate Foliar Chlorophyll Content Using Hyperspectral Reflectance Partial least-squares (PLS) regression is a popular method for modeling chemical constituents from spectroscopic data and has been widely applied to retrieve leaf chemical components via hyperspectral remote sensing. However, one persistent challenge for applying the PLS regression is the selection of informative spectral bands among the vast array of acquired spectra. No consensus has been reached yet on how to select informative bands regardless of many techniques being proposed. In this paper, we have composited four individual data sets containing a total of 598 leaf samples from various species to evaluate four different band elimination/selection methods. Results revealed that the stepwise-PLS approach was optimal to estimate leaf chlorophyll content even under different spectral resolutions, from which informative bands were identified. Informative bands, in general, include bands inside the near-infrared (NIR), and in addition, one within the blue range and one within the red range. With such combinations, the PLS regression models meet the requirement for accurate leaf chlorophyll estimation. For most PLS regression models, their accuracies decreased with the reduction of spectral resolution, but the stepwise-PLS approach could consistently estimate the chlorophyll content at different spectral resolutions (with R2 ≥ 0.77 for resolutions <; 20 nm). The findings, hence, provide valuable insights for selecting informative spectral bands for PLS analysis and lay a strong foundation for retrieving foliar biochemical content using hyperspectral remote sensing data. Hyperspectral imaging ; Remote sensing ; Partial least squares regression ; Spectral bands ; Selection (genetic algorithm) ; Range (aeronautics) ; Mean squared error ; Regression ; Near-infrared spectroscopy ; Content (measure theory) ; Reflectivity ; Spectral resolution ; Regression analysis ; Stepwise regression ; Chlorophyll ; Computer science ; Mathematics ; Environmental science ; Artificial intelligence ; Spectral line ; Statistics ; Botany ; Materials science ; Biology ; Optics ; Geography ; Physics Spectroscopy and Chemometric Analyses ; Remote Sensing in Agriculture ; Leaf Properties and Growth Measurement