Identifying challenges and opportunities of in-memory computing on large HPC systems
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Here, the synthesis and characterization of Ni 2 Zn 11 -type γ-brasses with composition Mn 2+x Zn 11–x (x = 0.06–0.60) are reported. The synthesis follows standard high temperature methods and characterization by single crystal X-ray diffraction (SCXRD) and powder X-ray Diffraction (PXRD) techniques as well as Energy Dispersive Spectroscopy (EDS). First principles electronic structure calculations showed preferential heteroatomic Mn–Zn bonding and repulsive effects of Zn–Zn 3 d –3 d orbital overlap that influence the metal atom distribution in the structure. Local bonding environments and the relationship of Mn 2+x Zn 11-x to other γ-brasses containing 3 d metals such as PdCoZn 11 and Ni 2 Zn 11 are discussed.
Uranium compounds α-Ba 2 [UO 2 (PO 4 ) 2 ] (1), β-Ba 2 [UO 2 (PO 4 ) 2 ] (2), and Ba 2 [UO 2 (AsO 4 ) 2 ] (3) were synthesized by H 3 BO 3 /B 2 O 3 flux reactions, though boron is not incorporated into the structures. Phases 1 and 2 are topologically identical, but 1 is heavily distorted with respect to 2. An unusual UO 7 pentagonal bipyramid occurs in 1, exhibiting a highly distorted equatorial configuration and significant bending of the uranyl group, due to edge-sharing with one neighboring PO 4 3– tetrahedron. Compound 2 contains more normal square bipyramids that share corners with four neighboring PO 4 3– tetrahedra, but the uranyl cation UO 2 2+ is tilted relative to the equatorial plane. Experimental evidence as well as density functional theory (DFT) calculations suggest that 1 is more stable than 2. In theory, 1 and 2 can interconvert by forming/releasing the shared edge between the uranyl polyhedron and the phosphate tetrahedron. Finally, similar fundamental building blocks in β-Ba 2 [UO 2 (PO 4 ) 2 ] and Ba 2 [UO 2 (AsO 4 ) 2 ] indicate a possible evolution of uranyl-based structures from chain to layer type and formation of an accretional series.
Cylindrical or rod-like particles are promising materials for the applications of fillers in nanocomposite materials and additives to control rheological properties of colloidal suspensions. Recent advances in particle synthesis allows for cylinders to be manufactured with short-ranged attractions to study the gelation as a function of packing fraction, aspect ratio and attraction strength. In order to aid in the analysis of small-angle scattering experiments of rod-like particles, computer simulation methods were used to model these particles with specialized Monte Carlo algorithms and tabular superquadric potentials. The attractive interaction between neighboring rods increases with the amount of locally-accessible surface area, thus leading to patchy-like interactions. We characterize the clustering and percolation of cylinders as the attractive interaction increases from the homogenous fluid at relatively low attraction strength, for a variety of aspect ratios and packing fractions. Comparisons with the experimental scattering results are also presented, which are in agreement.
Electronic systems contained within sealed metallic enclosures, such as nuclear waste containers where wire penetration is not an option, require a through-wall data transfer technology to communicate. One such technology utilizes piezoelectric ultrasonic transducers to communicate through the barrier using elastic waves. Currently demonstrated ultrasonic through-wall data communication techniques have relied on having a direct interface with the ultrasonic transducers on either side. Here, this work studies the coupling of an antenna to such an ultrasonic communication system. A computationally efficient model of a piezobarrier ultrasonic communication system and antenna using a simplified antenna model and a transfer matrix piezobarrier model is utilized to perform a representative parameter space search. The system efficiency and bandwidth trends are shown for the system parameters as well as operating frequency. The tradeoff between efficiency and bandwidth is also presented. A system configuration within the parameter space is fabricated and demonstrated to achieve a data rate of up to 100 kb/s.
Here, in this article, we present a finite-time stopping criterion for consensus algorithms in networks with dynamic communication topology. Prior state of the art has established convergence to the consensus value; however, the asymptotic convergence of these algorithms poses a challenge in practical settings where the response from agents is required in finite time. To this end, we propose a maximum-minimum protocol that propagates the global maximum and minimum values of agent states (while running the consensus algorithm) in the network. This article focuses on establishing that the global maximum and minimum values are strictly monotonic even for a dynamic topology, and they can be used to distributively ascertain the closeness to convergence in finite time. We rigorously show that each node can have access to the global maximum and minimum by running the proposed maximum-minimum protocol to realize a finite-time stopping criterion for the otherwise asymptotic consensus algorithm. The practical utility of the algorithm is illustrated through experiments where each agent is instantiated by a NodeJS socket.io server.
Visual analytics and interactive machine learning both try to leverage the complementary strengths of humans and machines to solve complex data exploitation tasks. These fields overlap most significantly when training is involved: the visualization or machine learning tool improves over time by exploiting observations of the human-computer interaction. This paper focuses on one aspect of the human-computer interaction that we call user-driven sampling strategies. Unlike relevance feedback and active learning sampling strategies, where the computer selects which data to label at each iteration, we investigate situations where the user selects which data is to be labeled at each iteration. User-driven sampling strategies can emerge in many visual analytics applications but they have not been fully developed in machine learning. We discovered that in user-driven sampling strategies suggest new theoretical and practical research questions for both visualization science and machine learning. In this paper we identify and quantify the potential benefits of these strategies in a practical image analysis application. We find user-driven sampling strategies can sometimes provide significant performance gains by steering tools towards local minima that have lower error than tools trained with all of the data. Furthermore, in preliminary experiments we find these performance gains are particularly pronounced when the user is experienced with the tool and application domain.
The work explored the linking of modern developing machine learning techniques (manifold learning and in particular diffusion maps) with traditional PDE modeling/discretization/scientific computation techniques via the equation-free methodology developed by the PI. The result (in addition to several PhD degrees, two of them by CSGF Fellows) was a sequence of strong developments - in part on the algorithmic side, linking data mining with scientific computing, and in part on applications, ranging from PDE discretizations to molecular dynamics and complex network dynamics.
Abstract not provided.
During the final funding period of the above-mentioned grant, a number of key milestones were achieved as they relate to the Separation Science program. Below are the descriptions of those efforts and the papers published in support of them (see also submitted publication list). There was a strong focus during the final funding period was on hydrated protons and the unique properties of acidic solutions, which as pertinent to many separations processes.
Molecule size correlates with the number of electrons on electronic energies and strength of anharmonicity on vibrational properties, however, it is challenging to address using classical computing. In this study, variational quantum eigensolver (VQE) algorithm was implemented on a quantum simulator to quantify electronic and vibrational energies and reaction pathways of CO2 + NH3 = NH2COOH. The VQE-based Hartree-Fock-Embedding algorithm was adopted to benchmark electronic energies for a series of molecules (doi.org/10.1063/5.0188249) and quantify the reaction energy profile of the CO2 capture reaction (doi.org/10.1116/5.0137750). The generated reaction profile is in good agreement with the classical high-level Coupled-Cluster-Singles-and-Doubles (CCSD) results. The quantum computing algorithm also helps enhance the calculation of vibrational ground-state energies by considering the many-body coupling using the Vibrational Self-Consistent Field method, providing results for CO2 and NH3 molecules with accuracy comparable to the direct diagonalization method. Our approach indicates quantum computing can be applied to solve practical problems.
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Abstract not provided
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