jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Otto Lindblom
Source: Työväentutkimus Vuosikirja (article, 2020-01-01)
Field: Materials Science — Materials Chemistry

A Computational Study of Isotope Exchange as a Tritium Removal Tool in Tungsten Due to its exceptional thermal properties and irradiation resistance, tungsten is the material of choice for critical plasma-facing components in many leading thermonuclear fusion projects. Owing to the natural retention of hydrogen isotopes in materials such as tungsten, the safety of a fusion device depends heavily on the inventory of radioactive tritium in its plasma-facing components. The proposed methods of tritium removal typically include thermal treatment of massive metal structures for prolonged timescales. A novel way to either shorten the treatment times or lower the required temperatures is based performing the removal under an H-2 atmosphere, effectively exchanging the trapped tritium for non-radioactive protium.&#13;\n&#13;\nIn this thesis, we employ molecular dynamics simulations to study the mechanism of hydrogen isotope exchange in vacancy, dislocation and grain boundary type defects in tungsten. By comparing the results to simulations of purely diffusion-based tritium removal methods, we establish that hydrogen isotope exchange indeed facilitates faster removal of tritium for all studied defect types at temperatures of 500 K and above. The fastest removal, when normalising based on the initial occupation of the defect, is shown to occur in vacancies and the slowest in grain boundaries. Through an atom level study of the mechanism, we are able to verify that tritium removal using isotope exchange depends on keeping the defect saturated with hydrogen.&#13;\n&#13;\nThis study also works to show that molecular dynamics indeed is a valid tool for studying tritium removal and isotope exchange in general. Using small system sizes and spatially-parallelised simulation tools, we have managed to model isotope exchange for timescales extending from hundreds of nanoseconds up to several microseconds. Tungsten ; Tritium ; Isotope ; Radiochemistry ; Hydrogen isotope ; Chemistry ; Metallurgy ; Nuclear physics ; Materials science ; Physics Fusion materials and technologies ; Nuclear Materials and Properties ; Chemical Synthesis and Characterization
Authors: Mariia Karabin | Steven J. Stuart
Source: The Journal of Chemical Physics (preprint, 2020-09-15)
Field: Materials Science — Materials Chemistry

Simulated annealing with adaptive cooling rates As one of the most robust global optimization methods, simulated annealing has received considerable attention with many variations that attempt to improve the cooling schedule. This paper introduces a variant of molecular dynamics-based simulated annealing that is useful for optimizing atomistic structures, and makes use of the heat capacity of the system, determined on the fly during optimization, to adaptively control the cooling rate. This adaptive cooling approach is demonstrated to be more computationally efficient than classical simulated annealing when applied to Lennard-Jones clusters. The increase in efficiency is approximately a factor of two for clusters with 25–40 atoms, and improves as the size of the system increases. Simulated annealing ; Adaptive simulated annealing ; Annealing (glass) ; Schedule ; Computer science ; Mathematical optimization ; Global optimization ; Materials science ; Algorithm ; Mathematics ; Metallurgy Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; nanoparticles nucleation surface interactions
Authors: Raffi Alexander Yessayan
Source: NCSU Libraries Repository (North Carolina State University Libraries) (article, 2020-07-27)
Field: Materials Science — Materials Chemistry

Implementation and Optimization of Massively Parallel Spatial Domain Decompositions for Existing Deterministic Neutron Transport Codes on Cartesian and Tetrahedral Grids. The needs of nuclear industry and the national security complex have driven a desire for increasingly high-fidelity simulations of neutron transport phenomena.Complicating this, deterministic neutron transport methods capable of providing these high-fidelity solutions, such as the three-dimensional discrete ordinates approximation, can require massive amounts of computational resources to evaluate.Rapid and continuous growth of available high-performance computing (HPC) resources has provided the raw computing power necessary to realize these simulation capabilities.However, it still falls to the developers of production-level deterministic neutron transport software to develop parallel computing algorithms and methods capable of fully utilizing these resources.Furthermore, as these HPC systems are expensive, shared resources, developers must aim to implement software that not only runs fast and yields accurate solutions but is also highly efficient.This already challenging task is made only more complicated by the long development histories of many target codes.With many production transport codes having years, or even decades, of verified software, the cost of a complete overhaul can be immeasurably high.As a result, there exists a significant and demonstrated need for methods which are capable of more fully utilizing modern HPC resources and which can be easily retrofit into existing production codes.This class of methods allows for existing software to better capitalize on the hardware computing resources available to it while, simultaneously, minimizing the amount of testing required to ensure validation and verification are maintained.Addressing this need, this dissertation deeply analyzes and optimizes the performance of an existing massively parallel code, PIDOTS, and then demonstrates that the analysis and optimizations can be generalized for application to other codes.These generalized methods can be used to improve the performance of an already parallel code or to retrofit massively parallel capabilities into an existing code as is demonstrated with the THOR code.Motivated by a perceived performance defect found during weak-scaling tests of PIDOTS, a detailed evaluation of massively parallel communication performance on leadership-class HPCs is performed.This evaluation informs the development of an optimal blocking communication Massively parallel ; Tetrahedron ; Computer science ; Domain (mathematical analysis) ; Cartesian coordinate system ; Computational science ; Parallel computing ; Grid ; Neutron transport ; Theoretical computer science ; Algorithm ; Distributed computing ; Mathematics ; Physics ; Neutron ; Geometry ; Mathematical analysis Graphite, nuclear technology, radiation studies ; Parallel Computing and Optimization Techniques ; Nuclear reactor physics and engineering
Authors: K. Satō | Kenji Tsuruta
Source: Bulletin of the American Physical Society (article, 2020-03-05)
Field: Chemistry — Analytical Chemistry

Optimization of Molecular Characteristic using Continuous Representation of Molecules by Variational Autoencoder with Discriminator Autoencoder ; Discriminator ; Representation (politics) ; Molecule ; Computer science ; Algorithm ; Statistical physics ; Biological system ; Physics ; Artificial intelligence ; Quantum mechanics ; Artificial neural network ; Biology Spectroscopy and Chemometric Analyses ; Analytical Chemistry and Chromatography ; Advanced Chemical Sensor Technologies
Authors: Tim Hsu | William K. Epting | Hokon Kim | Harry Abernathy | Gregory Hackett | Anthony D. Rollett | A. Salvador | Elizabeth A. Holm
Source: arXiv (Cornell University) (preprint, 2020-06-22)
Field: Materials Science — Materials Chemistry

Microstructure Generation via Generative Adversarial Network for\n Heterogeneous, Topologically Complex 3D Materials Using a large-scale, experimentally captured 3D microstructure dataset, we\nimplement the generative adversarial network (GAN) framework to learn and\ngenerate 3D microstructures of solid oxide fuel cell electrodes. The generated\nmicrostructures are visually, statistically, and topologically realistic, with\ndistributions of microstructural parameters, including volume fraction,\nparticle size, surface area, tortuosity, and triple phase boundary density,\nbeing highly similar to those of the original microstructure. These results are\ncompared and contrasted with those from an established, grain-based generation\nalgorithm (DREAM.3D). Importantly, simulations of electrochemical performance,\nusing a locally resolved finite element model, demonstrate that the GAN\ngenerated microstructures closely match the performance distribution of the\noriginal, while DREAM.3D leads to significant differences. The ability of the\ngenerative machine learning model to recreate microstructures with high\nfidelity suggests that the essence of complex microstructures may be captured\nand represented in a compact and manipulatable form.\n Microstructure ; Materials science ; Tortuosity ; Generative grammar ; Computer science ; Artificial intelligence ; Geometry ; Composite material ; Mathematics ; Porosity Machine Learning in Materials Science ; Generative Adversarial Networks and Image Synthesis ; Domain Adaptation and Few-Shot Learning
Authors: Kazutoshi Yamada | Utsuka Shohei | Chigawa Takaho | Takashi Kuraishi | Kenji Takizawa | E Tezduyar Tayfun
Source:  (article, 2020-01-01)
Field: Materials Science — Materials Chemistry

High-Fidelity Advection-Diffusion Modeling for Soot Clogging in Diesel Oxidation Catalysts Diesel particulate filter ; Soot ; Clogging ; Diffusion ; Advection ; Diesel fuel ; Catalysis ; Environmental science ; Materials science ; Waste management ; Chemistry ; Thermodynamics ; Combustion ; Engineering ; Geography Catalytic Processes in Materials Science ; Catalysis and Oxidation Reactions ; Catalysis and Hydrodesulfurization Studies
Authors: Pankaj Rajak | Aravind Krishnamoorthy | Ankit Mishra | Rajiv K. Kalia | Aiichiro Nakano | Priya Vashishta
Source: arXiv (Cornell University) (preprint, 2020-09-14)
Field: Materials Science — Materials Chemistry

Predictive Synthesis of Quantum Materials by Probabilistic Reinforcement Learning Predictive materials synthesis is the primary bottleneck in realizing new functional and quantum materials. Strategies for synthesis of promising materials are currently identified by time-consuming trial and error approaches and there are no known predictive schemes to design synthesis parameters for new materials. We use reinforcement learning to predict optimal synthesis schedules, i.e. a time-sequence of reaction conditions like temperatures and reactant concentrations, for the synthesis of a prototypical quantum material, semiconducting monolayer MoS$_{2}$, using chemical vapor deposition. The predictive reinforcement leaning agent is coupled to a deep generative model to capture the crystallinity and phase-composition of synthesized MoS$_{2}$ during CVD synthesis as a function of time-dependent synthesis conditions. This model, trained on 10000 computational synthesis simulations, successfully learned threshold temperatures and chemical potentials for the onset of chemical reactions and predicted new synthesis schedules for producing well-sulfidized crystalline and phase-pure MoS$_{2}$, which were validated by computational synthesis simulations. The model can be extended to predict profiles for synthesis of complex structures including multi-phase heterostructures and can also predict long-time behavior of reacting systems, far beyond the domain of the MD simulations used to train the model, making these predictions directly relevant to experimental synthesis. Reinforcement learning ; Bottleneck ; Computer science ; Probabilistic logic ; Materials science ; Nanotechnology ; Biological system ; Machine learning ; Artificial intelligence Machine Learning in Materials Science ; Software Engineering Research ; Ferroelectric and Negative Capacitance Devices
Authors: Sabarinathan P Subramaniyan | Muhammad A. Imam | Pavana Prabhakar
Source:  (article, 2020-09-09)
Field: Materials Science — Polymers and Plastics

Effect of Fiber Packing on Moisture Diffusivity and Tortuosity in Fiber Reinforced Composites Carbon Fiber Reinforced Polymer (CFRP) composites are widely used in marine environment due to their high specific strength, increased durability and corrosion resistance property. But these materials when subjected to seawater absorb moisture that results in the reduction of material strength due to matrix plasticization and reduced interfacial strength due to de-bonding at the fiber-matrix interface. To ensure long-term durability and damage tolerance, it is important to understand the impact of moisture diffusion and moisture-induced damage in composites. To that end, the goal of this paper is to elucidate the importance of fiber packing and orientation on moisture diffusion such that CFRP composites can be optimally design for marine applications. In this paper, we developed computational models within finite element method to study the long-term response of CFRP composites subjected to moisture conditions. We developed twodimensional micromechanical diffusion models with varying fiber volume fraction and different fiber distribution like square, hexagonal and random array to study the influence of fiber packing on the effective diffusivity and moisture saturation. Specifically, we elucidated the influence of tortuous diffusion pathways that manifest due to these micromechanical geometric properties of CFRP. Materials science ; Composite material ; Durability ; Moisture ; Tortuosity ; Fiber ; Volume fraction ; Diffusion ; Thermal diffusivity ; Shrinkage ; Porosity Natural Fiber Reinforced Composites ; Mechanical Behavior of Composites ; Epoxy Resin Curing Processes
Authors: Yuming Gu | Ziteng Liu | Changzhou Yu | Xu Gu | Lili Xu | Yang Gao | Jing Ma
Source: The Journal of Physical Chemistry (article, 2020-01-01)
Field: Chemistry — Inorganic Chemistry

Zeolite Adsorption Isotherms Predicted by Pore Channel and Local Environmental Descriptors: Feature Learning on DFT Binding Strength Fast prediction of adsorption isotherms is of great importance in the structural characterization and property prediction of zeolites prior to the synthesis of the target zeolite. Here, we employ the feature learning (FL) method to simulate the adsorption isotherms through density functional theory data generation of binding strength of nitrogen molecule adsorption in zeolites. Three features, that is, the size of adsorption cavities, the geometry of the pore apertures, and the local geometric distortion, are identified to control the binding strength, qualitatively in terms of a polynomial generative model. The local distortion of the Si–O–Si linkage is correlated with the electrostatic polarization of the nitrogen molecule upon adsorption in the zeolite cage. The electrostatic polarization from local zeolite environment can be further adjusted by the Si/Al ratios and thus can enhance the binding strength even twice as the silicalite zeolites when Si/Al = 47. The predicted adsorption isotherms of MFI and MWW zeolites from the feature-learned binding energies are in qualitative agreement with the experimental Brunauer–Emmett–Teller detection data. For 200,429 zeolites, including 248 known zeolites reported in the International Zeolite Association (IZA), the predicted adsorption isotherms are classified into three groups, among which the zeolites with the medium-sized three-dimensional channel architectures are favorable for nitrogen adsorption. The proposed FL scheme is a practical tool to give a quick prediction of adsorption isotherms and high-throughput screening of porous materials for adsorption-related applications. Zeolite ; Adsorption ; Chemistry ; Molecule ; Polarization (electrochemistry) ; Inorganic chemistry ; Computational chemistry ; Physical chemistry ; Organic chemistry ; Catalysis Zeolite Catalysis and Synthesis ; Metal-Organic Frameworks: Synthesis and Applications ; Mesoporous Materials and Catalysis
Authors: Hassan Asnaoui | Mohammed Khalis | R Ravindran | M Ganapathirao | I Pop | H Asnaoui | M Khalis | A Laaziri | J Chen | J Yoon | S Yiacoumi | L Ait Mlouk | M Ait Hou | Y Sag | B Akael | T Kutsal | M Kavanda | E Fakoor | S Mahzoonc | S Mansooreh | M Huysmans | A Dassargues | B Naja | G Volesky | I Tan | H Spinner | R Leyva-Ramos | J Rangel-Mendez | L Bernal-Jacome | M Berber Mendoz | P Cardew | D Van Der Leer | N Weatherill | R Sharp | C Hayes | E Williamson | L Adams | M Hashemzadeha | A Nilchib | A Hassania | F Chuanqing | Y Ling | K Wang | Z Liu | Y Chenga | L Wanga | B Panga | J Jiang | H Cid | C Ortiz | J Pizarro | J Moreno-Pirajn | X Li | L Zhang | Z Yang | P Wang | Y Yan | J Ran | N Khori | T Hadibarata | M Elshikh | A Al-Ghamdi | Z Yusop | A Emam | L Foud | A Kamal-Eldeen | M Ibrahim | M Seif
Source: Biointerface Research in Applied Chemistry (article, 2020-03-14)
Field: Chemistry — Organic Chemistry

Study of the transport modeling by diffusion of pollutants through porous materials This paper presents how distance function dispersal produces diffusion range expansion of the contamination. We construct a stratified diffusion model, which describes the dynamics of the size distribution of colonies created by distance migrants of metal ions. The model consists of a Fick's equation combined with a Navier-stokes. The model provides an estimate of range expansion in terms of the rate adsorption due to neighborhood diffusion, the leap time and the geometric form. The results explain various types of nonlinear range expansion observed in materials or biomaterials. Diffusion ; Range (aeronautics) ; Nonlinear system ; Statistical physics ; Porous medium ; Mechanics ; Biological dispersal ; Materials science ; Porosity ; Thermodynamics ; Physics ; Composite material Surfactants and Colloidal Systems ; Electrostatics and Colloid Interactions ; Groundwater flow and contamination studies
Authors: Bayrem Zitouni | Lennart von Germersheim
Source:  (article, 2020-08-21)
Field: Chemistry — Physical and Theoretical Chemistry

Desorption, diffusion, reflections, and reemission: the outgassing molecules elusive behaviors As usually known, the physics in space have a tendency to behave differently from the physics on earth. In fact, Free Molecular Flow are an important hurdle to cross in order to completely gasp molecules behaviors. Outgassing for example is one of these phenomena that deeply impacts satellite performances and remains complex to model regardless of the numerous successful optical satellites missions. Despite the efforts spent last years, on-ground outgassing testing still gives little information about the driving mechanism of outgassing. Is it controlled by desorption? By diffusion? Is it a mix of both? And once extracted from the material bulk, what are these molecules? Are they carbon based molecules? Are they a mix of different species? And once they leave the materials, how the molecules condensate on the sensitive surface? How are they reemitted? Is it governed by reflection or by a residence time? In addition, the configuration on a spacecraft is far more complicated than in a simple set-up: huge temperature gradients exists, a material with low outgassing criteria (TML, RML, CVCM) can still be a threat to the whole mission if it is located next to a sensitive optical surface… Meanwhile, OHB conducted several numerical investigations that this article will summarize. Based on latest JAXA1 and ONERA² experiences, a diffusion model have been implemented and connected to the view factors calculations of a closed cavity. The model is based on the Fick’s law and a numerical calculation was performed thanks to an Euler/Crank-Nicolson method. Compared to the desorption modelling, the diffusion results showed quite important differences especially in the starting of the outgassing. To able to judge between the two approaches, the numerical model have been confronted to an outgassing set-up called the “CNES mock-up”. To make things more complicated, the “reemission” part is also challenging. Depending on the configuration to model, reflection of molecules can be the driving mechanism and the residence time approach is too slow to catch up with the real observed behavior. Concrete examples will be presented in this article to open the discussion on possible approaches to better model and understand outgassing. Outgassing ; Desorption ; Diffusion ; Molecule ; Materials science ; Atomic physics ; Chemical physics ; Chemistry ; Physics ; Adsorption ; Physical chemistry ; Thermodynamics ; Quantum mechanics Various Chemistry Research Topics
Authors: I. K Ayinla | Alafara A. Baba | Halimah Funmilayo Babamale | Subrat Kumar Padhy | Oseni Adio | G. A Ananfi | R.O Ilesanmi | B.C. Tripathy
Source: Ife Journal of Science (article, 2020-01-20)
Field: Materials Science — Materials Chemistry

Hydrometallurgical treatment of biotite ore for production of magnesium hydroxide (Mg(OH)&lt;sub&gt;2&lt;/sub&gt;) for industrial value addition Magnesium hydroxide is a popular inorganic compound used in wide range of applications such as: flame retardant in polymers, special ceramics, fillers in bleaching agent and a raw material for preparation of magnesium oxide, which were currently imported into Nigeria from foreign countries using hard earned currency. Thus, this current study focused on dissolution kinetics of a biotite ore treated via leaching in sulphuric acid leachant. During leaching, parameters such as leachant concentration, particle size and reaction temperature on the extent of ore dissolution were examined. At optimal leaching conditions (2.5 mol/L H2 SO4 , 80°C), 98.23 % of the initial 10 g/L ore reacted within 120 minutes. The shrinking core dissolution model was adopted to evaluate the various dissolution parameters for better understanding of the rate determining step. Experimental results indicated that the reaction rate of the leaching process was controlled by diffusion model, with calculated activation energy (Ea) of 38.56 kJ/mol. By considering the solubility differences of corresponding elements, a two-step precipitation process was applied to remove Fe and Al and recovery of Mg. Subsequently, magnesium hydroxide was prepared by precipitation from the leach solution with ammonia as the precipitant. X-ray diffraction (XRD) and scanning electron microscope (SEM) analyses showed that the high purity Mg(OH)2 existed in the form of flower-like spherical aggregation. Furthermore, the final solution of ammonium–potassium chlorate can be used for agricultural compound fertilizer.Keywords: inorganic, optimal, leaching, ammonium-potasium chlorate, fertilizer Leaching (pedology) ; Dissolution ; Ammonium hydroxide ; Magnesium ; Chemistry ; Nuclear chemistry ; Potassium hydroxide ; Inorganic chemistry ; Metallurgy ; Materials science ; Organic chemistry ; Geology Magnesium Oxide Properties and Applications ; Metal Extraction and Bioleaching ; Recycling and utilization of industrial and municipal waste in materials production