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Wei, Julia

Publications and source records attributed to Wei, Julia.

3 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Synthesis of Narrow SnTe Nanowires Using Alloy Nanoparticles

Topological crystalline insulator tin telluride (SnTe) provides a rich playground to examine interactions of correlated electronic states, such as ferroelectricity, topological surface states, and superconductivity. The study of SnTe nanowires may lead to even richer physics owing to the one-dimensional (1D) confinement effect and an increased contribution from the topological surface states. Thus, for transport measurements, SnTe nanowires must be synthesized with reduced diameters and high crystalline quality to ensure 1D confinement and phase coherence of the topological surface electrons. Here, we report a facile growth method to produce narrow SnTe nanowires with a high yield using alloy nanoparticles as growth catalysts. The average diameter of the SnTe nanowires grown using alloy nanoparticles is 85 nm, nearly a factor of three reduction compared to the average diameter of 240 nm when using gold nanoparticles as growth catalysts. Transport measurements reveal the effect of the nanowire diameter on the residual resistance ratio and magnetoresistance. Particularly, the ferroelectric transition temperature for SnTe evolves systematically with the nanowire diameter. In situ cryogenic cooling of narrow SnTe nanowires in a transmission electron microscope directly reveals the cubic to rhombohedral structural transition, which is associated with the ferroelectric transition. Thus, these narrow SnTe nanowires represent a model system to study electronic states arising from 1D confinement, such as 1D topological superconductivity and potential multiband superconductivity.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2020

Approximate Inverse Chain Preconditioner: Iteration Count Case Study for Spectral Support Solvers

As the growing availability of computational power slows, there has been an increasing reliance on algorithmic advances. However, faster algorithms alone will not necessarily bridge the gap in allowing computational scientists to study problems at the edge of scientific discovery in the next several decades. Often, it is necessary to simplify or precondition solvers to accelerate the study of large systems of linear equations commonly seen in a number of scientific fields. Preconditioning a problem to increase efficiency is often seen as the best approach; yet, preconditioners which are fast, smart, and efficient do not always exist. Following the progress of [1], we present a new preconditioner for symmetric diagonally dominant (SDD) systems of linear equations. These systems are common in certain PDEs, network science, and supervised learning among others. Based on spectral support graph theory, this new preconditioner builds off of the work of [2], computing and applying a V-cycle chain of approximate inverse matrices. This preconditioner approach is both algebraic in nature as well as hierarchically-constrained depending on the condition number of the system to be solved. Due to its generation of an Approximate Inverse Chain of matrices, we refer to this as the AIC preconditioner. We further accelerate the AIC preconditioner by utilizing precomputations to simplify setup and multiplications in the con-text of an iterative Krylov-subspace solver. While these iterative solvers can greatly reduce solution time, the number of iterations can grow large quickly in the absence of good preconditioners. Initial results for the AIC preconditioner have shown a very large reduction in iteration counts for SDD systems as compared to standard preconditioners such as Incomplete Cholesky (ICC) and Multigrid (MG). We further show significant reduction in iteration counts against the more advanced Combinatorial Multigrid (CMG) preconditioner. We have further developed no-fill sparsification techniques to ensure that the computational cost of applying the AIC preconditioner does not grow prohibitively large as the depth of the V-cycle grows for systems with larger condition numbers. Our numerical results have shown that these sparsifiers maintain the sparsity structure of our system while also displaying significant reductions in iteration counts.1 2

97 MATHEMATICS AND COMPUTING↗