Open Access. Powered by Scholars. Published by Universities.®
Nanoscience and Nanotechnology Commons™
Open Access. Powered by Scholars. Published by Universities.®
Articles 1 - 2 of 2
Full-Text Articles in Nanoscience and Nanotechnology
Structures And Energetics Of Silicon Nanotubes From Molecular Dynamics And Density Functional Theory, Amritanshu Palaria, Gerhard Klimeck, Alejandro Strachan
Structures And Energetics Of Silicon Nanotubes From Molecular Dynamics And Density Functional Theory, Amritanshu Palaria, Gerhard Klimeck, Alejandro Strachan
PRISM: NNSA Center for Prediction of Reliability, Integrity and Survivability of Microsystems
We use molecular dynamics with a first-principles-based force field and density functional theory to predict the atomic structure, energetics, and elastic properties of Si nanotubes. We find various low-energy and low-symmetry hollow structures with external diameters of about 1 nm. These are the most stable structures in this small-diameter regime reported so far and exhibit properties very different from the bulk. While the cohesive energies of the four most stable nanotubes reported here are similar (from 0.638 to 0.697 eV above bulk Si), they have disparate Young's moduli (from 72 to 123 GPa).
A Parallel Spectral Element Method For Dynamic Three-Dimensional Nonlinear Elasticity Problems, S. Dong, Z. Yosibash
A Parallel Spectral Element Method For Dynamic Three-Dimensional Nonlinear Elasticity Problems, S. Dong, Z. Yosibash
PRISM: NNSA Center for Prediction of Reliability, Integrity and Survivability of Microsystems
We present a high-order method employing Jacobi polynomial-based shape functions, as an alternative to the typical Legendre polynomial-based shape functions in solid mechanics, for solving dynamic three-dimensional geometrically nonlinear elasticity problems. We demonstrate that the method has an exponential convergence rate spatially and a second-order accuracy temporally for the four classes of problems of linear/geometrically nonlinear elastostatics/elastodynamics. The method is parallelized through domain decomposition and message passing interface (MPI), and is scaled to over 2000 processors with high parallel performance.