jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Larysa Yuzevych | Ruslan Skrynkovskyy | Volodymyr Yuzevych | Vitalii Lozovan | Grzegorz Pawlowski | Mykhailo Yasinskyi | Ihor Ogirko
Source: Eastern-European Journal of Enterprise Technologies (article, 2019-07-30)
Field: Materials Science — Materials Chemistry

Improving the diagnostics of underground pipelines at oilandgas enterprises based on determining hydrogen exponent (PH) of the soil media applying neural networks A set of key parameters and information flows has been formed to simulate stages of probing the outside surface of underground metal pipelines (UMP) taking into account pH of the soil contacting with the pipe metal.Specimens of 17G1S steel placed in acid, alkaline and neutral media were examined using a polarization potential meter in a complex with a contactless current meter. Principles of application of neural networks (NN) in processing experimental results were formulated. A database has been developed. It meets the initial conditions for controlling the soil pH at the boundary with the metal under real conditions.Elements of the optimization approach for assessing pH of a coated UMP in the soil medium were proposed. The approach is based on the multiplicative qualimetric criterion of quality for the UMP section taking into account two groups of coefficients. The first group of coefficients refers to the internal coefficients and characterizes the metal pipeline and the second group refers to the external medium (i.e., soil electrolyte). Elements of the optimization approach for assessing pH of the coated pipeline in the soil medium were proposed.An NN was presented for the "pipeline-coating" system, which:1) is capable of solving the problem of cluster analysis and image classification;2) makes it possible to process data without their prior spectral transformation operating with discrete counts of information signals.The proposed NN type allows it to dynamically expand its own knowledge base of possible types of defects in controlled objects (pipelines) in the process of operation. With the help of the NN, soil pH was assessed for an UMP of 17G1S steel for three situations.The above information is important for improving the methods for controlling oil-and-gas enterprise UMPs, in particular, the methods for a correct assessment of anode current density in metal defects taking into account nonlinear character of informative parameters. Pipeline transport ; Artificial neural network ; Coating ; Polarization (electrochemistry) ; Computer science ; Process engineering ; Engineering ; Artificial intelligence ; Chemistry ; Mechanical engineering ; Materials science ; Composite material Material Properties and Failure Mechanisms ; Structural Integrity and Reliability Analysis ; Engineering Diagnostics and Reliability
Authors: Chenyi Li | Hichem Dammak | Guilhem Dezanneau
Source: Physical Chemistry Chemical Physics (article, 2019-01-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Identification of oxygen diffusion mechanisms in Nd<sub>1−x</sub>AE<sub>x</sub>BaInO<sub>4−x/2</sub> (AE = Ca, Sr, Ba) compounds through molecular dynamics Molecular dynamics simulations have been widely adopted to study oxygen ion diffusion mechanisms in materials for application in solid oxide fuel cells. Indeed, understanding the fundamental aspects of oxygen diffusion is important to develop new materials for this application. In this work, Nd1-xAExBaInO4-x/2 (AE = Ca, Sr, Ba) compounds have been studied by MD simulations focusing on oxygen diffusion mechanisms. Two general clustering methods were used, namely a convex hull classification method and a DBSCAN machine learning algorithm, to identify oxygen ion diffusion pathways. Here, relevant details are provided for an efficient use of these two approaches during MD analysis of ion conductors. The calculations show that Ca is the most favorable dopant for substituting Nd in NdBaInO4, while Ba is the least desired. Indeed, the substitution of Nd by Ca hardly changes the pristine lattice parameters of NdBaInO4 and leads to the highest oxygen diffusion coefficient compared to other dopants. The oxygen vacancies induced by doping mainly locate on two specific oxygen sites over four oxygen sites available. Concerning the diffusion process, jumps involving these two sites play the main role and are associated with smaller migration enthalpies. For the main diffusion path, ions migrate along the b (2 routes) and c (4 routes) directions. Some other oxygen sites can be considered as barriers for the diffusion process inducing a strong anisotropy in the diffusion process. Additionally, the residence time analysis of oxygen ions confirms that ions at different sites have different motion abilities. As a whole, the approach presented here can be extrapolated to other ion conductors for gaining detailed information about the diffusion process. Diffusion ; Oxygen ; Molecular dynamics ; Chemical physics ; Identification (biology) ; Chemistry ; Materials science ; Analytical Chemistry (journal) ; Crystallography ; Thermodynamics ; Physics ; Computational chemistry ; Environmental chemistry Magnetic and transport properties of perovskites and related materials ; Advancements in Solid Oxide Fuel Cells ; Electronic and Structural Properties of Oxides
Authors: Roger D. Amos | Rika Kobayashi
Source: Journal of Chemical Information and Modeling (article, 2019-02-07)
Field: Materials Science — Materials Chemistry

Feature Engineering for Materials Chemistry—Does Size Matter? The effects of structural featurizers in the prediction of band gaps have been investigated through machine learning by application to a silver nanoparticle data set and 2254 potential light-harvesting materials with known band gaps. Elemental properties were extended with structural features via Voronoi polyhedra, allowing for neighbor effects and thus presumably giving a better representation of the extended system. However, we did not find any noticeably significant difference in the predictive performance of our model. The biggest improvement in our model was due to inclusion of band gaps calculated using density functional theory. This resulted in a model that could predict the band gaps of the 2254 light-harvesting materials in the data set with an accuracy reflected in a root-mean-square error of 0.232 eV and mean absolute error of 0.142 eV. Furthermore, the good performance of our model was transferable to the prediction of a set of 72 experimental band gaps that were independent of the training set, giving a root-mean-square error of 0.91 eV and mean absolute error of 0.76 eV. Mean squared error ; Band gap ; Root mean square ; Representation (politics) ; Feature (linguistics) ; Set (abstract data type) ; Data set ; Approximation error ; Computer science ; Materials science ; Biological system ; Computational physics ; Mathematics ; Algorithm ; Artificial intelligence ; Statistics ; Physics ; Optoelectronics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; X-ray Diffraction in Crystallography
Authors: Mohamed Nadir Boucherit | Sid Ahmed Amzert | Fahd Arbaoui | Yakoub Boukhari | Abdelkrim Brahimi | Aziz Younsi
Source: Anti-Corrosion Methods and Materials (article, 2019-03-22)
Field: Materials Science — Metals and Alloys

Modelling input data interactions for the optimization of artificial neural networks used in the prediction of pitting corrosion Purpose This paper aims to predict the localized corrosion resistance by the application of artificial neural networks. It emphasizes the importance to take into account the relationships between the physical parameters before presenting them to the network. Design/methodology/approach The work was conducted in two phases. At the beginning, the authors executed an experimental program to measure pitting corrosion resistance of carbon steel in an aqueous environment. More than 900 electrochemical experiments were conducted in chemical solutions containing different concentrations of pitting agents, corrosion inhibitors and oxidant reagents. The obtained results were collected in a table where for a combination of the experimental parameters corresponds a pitting potential Epit obtained from the corresponding electrochemical experiment. In the second step, the authors used the experimental data to train different artificial neuron networks for predicting pitting potentials. Findings In this step, the authors considered the relationships that the chemical parameters are likely to have between them. Two types of relationships were taken into account: chemical equilibria which are controlled by the pH and the synergistic relationships that some corrosion inhibitors may have when they are in the presence of a chemical oxidant. Originality/value This comparative study shows that adjusting the input data by considering the physical relationships between them allows a better prediction of the pitting potential. The quality of the prediction, quantified by a regression factor, is qualitatively confirmed by a statistical distribution of the gap between experimental and calculated pitting potentials. Artificial neural network ; Pitting corrosion ; Corrosion ; Biological system ; Experimental data ; Electrochemistry ; Computer science ; Materials science ; Artificial intelligence ; Chemistry ; Metallurgy ; Mathematics ; Statistics ; Electrode Hydrogen embrittlement and corrosion behaviors in metals ; Corrosion Behavior and Inhibition ; Concrete Corrosion and Durability
Authors: Mojtaba Mansourpoor | Reza Azin | Shahriar Osfouri | Amir Abbas Izadpanah
Source: Journal of Dispersion Science and Technology (article, 2019-02-01)
Field: Chemistry — Analytical Chemistry

Experimental measurement and modeling study for estimation of wax disappearance temperature Wax deposition is a frequent problem in oil pipelines and down-stream industries. Correct prediction of wax formation conditions is required to prevent this phenomenon. In this study, wax appearance temperature (WAT) of 12 Iranian oil and condensate samples were measured using viscometry data and differential scanning Calorimetry (DSC) analysis. Also, a new empirical correlation and intelligent artificial neural network (ANN) model were developed to estimate wax disappearance temperature (WDT) of crude oils. Specific gravity, pressure, and molecular weight of oil sample were used as input variables for these models. The ANN model was trained using different hidden neurons and training algorithms. Experimental measurements studies were used for validation of the new correlation. Comparing the results indicated that the ANN model has 0.27% error while most thermodynamic models have an average error of 0.35% to 2.19%. Also, the proposed correlation can predict WDT with good accuracy and minimum input data. Results show that this correlation has a maximum error of 1.16% for 310 published experimental data and 1.19% for 9 Iranian samples. Wax ; Artificial neural network ; Differential scanning calorimetry ; Biological system ; Viscometer ; Statistics ; Mathematics ; Materials science ; Thermodynamics ; Petroleum engineering ; Computer science ; Viscosity ; Artificial intelligence ; Engineering ; Physics ; Composite material Petroleum Processing and Analysis ; Spectroscopy and Chemometric Analyses ; Phase Equilibria and Thermodynamics
Authors: Chiara Panosetti | Artur Engelmann | Lydia Nemec | Karsten Reuter | Johannes T. Margraf
Source: ChemRxiv (preprint, 2019-10-02)
Field: Materials Science — Materials Chemistry

Learning to Use the Force: Fitting Repulsive Potentials in Density-Functional Tight-Binding with Gaussian Process Regression The Density-Functional Tight Binding (DFTB) method is a popular semiempirical approximation to Density Functional Theory (DFT). In many cases, DFTB can provide comparable accuracy to DFT at a fraction of the cost, enabling simulations on length- and time-scales that are unfeasible with first principles DFT. At the same time (and in contrast to empirical interatomic potentials and force-fields), DFTB still offers direct access to electronic properties such as the band-structure. These advantages come at the cost of introducing empirical parameters to the method, leading to a reduced transferability compared to true first-principle approaches. Consequently, it would be very useful if the parameter-sets could be routinely adjusted for a given project. While fairly robust and transferable parameterization workflows exist for the electronic structure part of DFTB, the so-called repulsive potential Vrep poses a major challenge. In this paper we propose a machine-learning (ML) approach to fitting Vrep, using Gaussian Process Regression (GPR). The use of GPR circumvents the need for non-linear or global parameter optimization, while at the same time offering arbitrary flexibility in terms of the functional form. We also show that the proposed method can be applied to multiple elements at once, by fitting repulsive potentials for organic molecules containing carbon, hydrogen and oxygen. Overall, the new approach removes focus from the choice of functional form and parameterization procedure, in favour of a data-driven philosophy. Density functional theory ; Tight binding ; Gaussian ; Flexibility (engineering) ; Computer science ; Gaussian process ; Statistical physics ; Kriging ; Workflow ; Electronic structure ; Mathematical optimization ; Computational chemistry ; Chemistry ; Mathematics ; Machine learning ; Physics ; Statistics Machine Learning in Materials Science ; Metabolomics and Mass Spectrometry Studies ; Mass Spectrometry Techniques and Applications
Authors: Sheshera Mysore | Zach Jensen | Edward Kim | Kevin Huang | Haw-Shiuan Chang | Emma Strubell | Jeffrey Flanigan | Andrew McCallum | Elsa Olivetti
Source:  (preprint, 2019-01-01)
Field: Materials Science — Materials Chemistry

The Materials Science Procedural Text Corpus: Annotating Materials Synthesis Procedures with Shallow Semantic Structures Sheshera Mysore, Zachary Jensen, Edward Kim, Kevin Huang, Haw-Shiuan Chang, Emma Strubell, Jeffrey Flanigan, Andrew McCallum, Elsa Olivetti. Proceedings of the 13th Linguistic Annotation Workshop. 2019. Computer science ; Semantics (computer science) ; Knowledge graph ; Graph ; Information extraction ; Resource (disambiguation) ; Natural language processing ; Information retrieval ; Data science ; Artificial intelligence ; Programming language ; Theoretical computer science Machine Learning in Materials Science ; Topic Modeling ; Software Engineering Research
Authors: Al Juboury Abdul-Hameed
Source: IRAQI JOURNAL OF AGRICULTURAL SCIENCES (article, 2019-10-23)
Field: Materials Science — Materials Chemistry

USING OF ACTIVATED CARBON DERIVED FROM AGRICULTURE WASTE COATING BY LAYERED DOUBLE HYDROXIDE FOR COPPER ADSORPTION Mg/Fe layered double hydroxides (LDH) as highly effective clay adsorbents coating activated carbon derived from agricultural waste (AW) were prepared by activation of date palm leaf base (LB) synthesized on Mg/Fe layered double hydroxides. FTIR, SEM and EDS were used to characterize the obtained adsorbent for the copper removal from aqueous solutions. The evaluation of adsorption performances was carried out at various metal concentrations, adsorbent dosage, contact time, and pH. The pseudo-second-order kinetic and the Langmuir isotherm model showed a good compliance with the experimental data, the result demonstrated that the improving adsorption capacity and fast rating adsorption for Cu2+ resulted from successful intercalation of date palm petiole activated carbon into LDH. Adsorption ; Layered double hydroxides ; Copper ; Activated carbon ; Langmuir adsorption model ; Fourier transform infrared spectroscopy ; Hydroxide ; Hydrotalcite ; Intercalation (chemistry) ; Nuclear chemistry ; Aqueous solution ; Inorganic chemistry ; Materials science ; Chemistry ; Chemical engineering ; Organic chemistry ; Catalysis Layered Double Hydroxides Synthesis and Applications
Authors: Srikanth B. Yoginath | Maksudul Alam | Arvind Ramanathan | Debsindhu Bhowmik | Nouamane Laanait | Kalyan S. Perumalla
Source:  (article, 2019-05-01)
Field: Materials Science — Materials Chemistry

Towards Native Execution of Deep Learning on a Leadership-Class HPC System Large parallel machines generally offer the best parallel performance with "native execution" that is achieved using codes developed with the optimized compilers, communication libraries, and runtimes offered on the machines. In this paper, we report and analyze performance results from native execution of deep learning on a leadership-class high-performance computing (HPC) system. Using our new code called DeepEx, we present a study of the parallel speed up and convergence rates of learning achieved with native parallel execution. In the trade-off between computational parallelism and synchronized convergence, we first focus on maximizing parallelism while still obtaining convergence. Scaling results are reported from execution on up to 15,000 GPUs using two scientific data sets from atom microscopy and protein folding applications, and also using the popular ImageNet data set. In terms of the traditional measure of parallel speed up, excellent scaling is observed up to 12,000 GPUs. Additionally, accounting for convergence rates of deep learning accuracy or error, a deep learning-specific metric called "learning speed up" is also tracked. The performance results indicate the need to evaluate parallel deep learning execution in terms of learning speed up, and point to additional directions for improved exploitation of high-end HPC systems. Computer science ; Parallel computing ; Deep learning ; Compiler ; Convergence (economics) ; Data parallelism ; Speedup ; Artificial intelligence ; Parallelism (grammar) ; Task parallelism ; Machine learning ; Programming language Machine Learning in Materials Science ; Advanced Memory and Neural Computing ; Ferroelectric and Negative Capacitance Devices
Authors: Viktor Ahlberg Gagnér | Ida Lundholm | María-José García-Bonete | Helena Rodilla | Ran Friedman | Vitali Zhaunerchyk | Gleb Bourenkov | T. Schneider | Jan Stake | Gergely Katona
Source: Scientific Reports (article, 2019-12-17)
Field: Materials Science — Materials Chemistry

Clustering of atomic displacement parameters in bovine trypsin reveals a distributed lattice of atoms with shared chemical properties Abstract Low-frequency vibrations are crucial for protein structure and function, but only a few experimental techniques can shine light on them. The main challenge when addressing protein dynamics in the terahertz domain is the ubiquitous water that exhibit strong absorption. In this paper, we observe the protein atoms directly using X-ray crystallography in bovine trypsin at 100 K while irradiating the crystals with 0.5 THz radiation alternating on and off states. We observed that the anisotropy of atomic displacements increased upon terahertz irradiation. Atomic displacement similarities developed between chemically related atoms and between atoms of the catalytic machinery. This pattern likely arises from delocalized polar vibrational modes rather than delocalized elastic deformations or rigid-body displacements. The displacement correlation between these atoms were detected by a hierarchical clustering method, which can assist the analysis of other ultra-high resolution crystal structures. These experimental and analytical tools provide a detailed description of protein dynamics to complement the structural information from static diffraction experiments. Delocalized electron ; Anisotropy ; Crystallography ; Cluster analysis ; Crystal structure ; Molecular physics ; Chemical physics ; Materials science ; Crystal (programming language) ; Molecular vibration ; Chemistry ; Physics ; Optics ; Raman spectroscopy ; Computer science Enzyme Structure and Function ; Protein Structure and Dynamics ; RNA and protein synthesis mechanisms
Authors: Beth A. Lindquist | Ryan B. Jadrich | Michael P. Howard | Thomas M. Truskett
Source: The Journal of Chemical Physics (article, 2019-09-10)
Field: Materials Science — Materials Chemistry

The role of pressure in inverse design for assembly Isotropic pairwise interactions that promote the self-assembly of complex particle morphologies have been discovered by inverse design strategies derived from the molecular coarse-graining literature. While such approaches provide an avenue to reproduce structural correlations, thermodynamic quantities such as the pressure have typically not been considered in self-assembly applications. In this work, we demonstrate that relative entropy optimization can be used to discover potentials that self-assemble into targeted cluster morphologies with a prescribed pressure when the iterative simulations are performed in the isothermal-isobaric ensemble. The benefits of this approach are twofold. First, the structure and the thermodynamics associated with the optimized interaction can be controlled simultaneously. Second, by varying the pressure in the optimization, a family of interparticle potentials that all self-assemble the same structure can be systematically discovered, allowing for a deeper understanding of self-assembly of a given target structure and providing multiple assembly routes for its realization. Selecting an appropriate simulation ensemble to control the thermodynamic properties of interest is a general design strategy that could also be used to discover interaction potentials that self-assemble structures having, for example, a specified chemical potential. Isotropy ; Inverse ; Pairwise comparison ; Entropy (arrow of time) ; Thermodynamic system ; Thermodynamic integration ; Thermodynamic process ; Molecular dynamics Block Copolymer Self-Assembly ; Pickering emulsions and particle stabilization ; Surface Chemistry and Catalysis
Authors: Hideki Kobayashi | Paul Rohrbach | Robert Scheichl | Nigel B. Wilding | Robert L. Jack
Source: The Journal of Chemical Physics (article, 2019-10-08)
Field: Materials Science — Materials Chemistry

Correction of coarse-graining errors by a two-level method: Application to the Asakura-Oosawa model We present a method that exploits self-consistent simulation of coarse-grained and fine-grained models in order to analyze properties of physical systems. The method uses the coarse-grained model to obtain a first estimate of the quantity of interest, before computing a correction by analyzing properties of the fine system. We illustrate the method by applying it to the Asakura-Oosawa model of colloid-polymer mixtures. We show that the liquid-vapor critical point in that system is affected by three-body interactions which are neglected in the corresponding coarse-grained model. We analyze the size of this effect and the nature of the three-body interactions. We also analyze the accuracy of the method as a function of the associated computational effort. Granularity ; Computer science ; Point (geometry) ; Function (biology) ; Statistical physics ; Mathematics ; Physics ; Geometry Material Dynamics and Properties ; Theoretical and Computational Physics ; Phase Equilibria and Thermodynamics