Abstract for the Fourth Foresight Conference on Molecular Nanotechnology.


Bobby G. Sumpter (a) Donald W. Noid (b) Robert E. Tuzun

Chemical and Analytical Sciences Division Oak Ridge National Laboratory Oak Ridge, TN 37831-6197

Simulation of the internal dynamics of molecular-based materials provide much of the needed data for developing and testing fundamental concepts and designs of nano-machines or components. However, in order to examine details pertaining to the operation of such devices, the simulation time and size must sample that comparable to the object and its expected operation time.

Thus, it is clear that new avenues to the molecular dynamics (MD) method, which has traditionally been plagued by a size-time scale bottleneck, must be formulated without losing the fundamental foundation of the method (Hamiltonian dynamics). In this poster we discuss the details of our recently developed simulation procedures that provide significant enhancements to the MD method and to the analysis of MD data. These techniques, called geometric statement functions, high resolution spectral methods, symplectic integration, and computational neural networks, have been employed to study large polymer crystals (up to 300,000 atoms for nanosecond time scales) and other molecular systems of importance to nanotechnology (in particular, various components of nano-machines). An important aspect of these techniques is that the simulations are very efficient (orders of magnitude faster than previous methods) and exact (classical constants of the motion, such as energy and momentum, are conserved). Data analysis tools based on high resolution spectral estimators and computational neural networks are used to extract the maximum content of information generated from the MD simulations (or computational experiments). The overall result is an MD method capable of simulating hundreds of thousands of atoms for hundreds of nanoseconds (with every degree of freedom) on standard workstations.

* This work was supported by the Division of Materials Sciences, Office of Basic Energy Sciences, U. S. Department of Energy, under Contract No. DE-AC05- 84OR21400 with Lockheed Martin Energy Systems, Inc.