Abstract
The capabilities of polymer science and computational chemistry are reaching a point of convergence. New computer hardware and novel computational methods have created opportunities to investigate new polymeric materials, as well as to model and predict their properties. The recent arrival of massively parallel computers and new algorithms for sharing computational tasks among many processors now bring simulation sizes on the order of 109 atoms to within reasonable time limits and will allow for new studies in emerging fields such as molecular nanotechnology.
| Original language | English |
|---|---|
| Pages (from-to) | 93-100 |
| Number of pages | 8 |
| Journal | Computers and Mathematics with Applications |
| Volume | 35 |
| Issue number | 7 |
| DOIs | |
| State | Published - Apr 1998 |
Funding
Recent progress toward miniaturization has demanded new ways of thinking about mechanical devices. This is particularly true in the technologies of sensors-on-a-chip and information storage, where micro-electromechanical systems (MEMS) are recognized as major, new areas for development. The logical extension of this technology is into the area of nano-scale devices such as bearings and gears, in which the whole component is comprised of only a few hundreds or thousands of atoms. This area of study, often referred to as molecular nanotechnology, has the potential to revolutionize chemistry, materials science, biology, and many other fields by creating an entirely new set of atomically precise mechanical devices and molecular machines. Molecular Research sponsored by the Division of Materials Sciences, Office of Basic Energy Sciences, U.S. Department of Energy under Contract DF~AC05-84OR21400 with Lockheed Martin Energy Systems, Inc. Also supported in part by an appointment to the Oak Ridge Postdoctoral Research Associates Program administered jointly by Oak Ridge National Laboratory and the Oak Ridge Institute for Science and Education. The authors acknowledge the use of Intel Paragon supercomputers located in the Center for Computational Sciences at ORNL, funded by the DOEs Mathematical, Information, and Computational Sciences (MICS) Division of the Office of Computational and Technology Research.
Keywords
- Geometric statement function method
- Molecular dynamics
- Symplectic integration