NER: Scalable techniques for massively parallel nanomaterial simulations for long-time behavior

This proposal was received in response to Nanoscale science and engineering initiative NSF 03-043, category NER. The investigators address a class of problems involving an assembly of carbon nanotubes, wherein interfaces play a key role. This class of problems pertains to long time scale simulations in which classical transition state theories are not applicable. The solution strategy is based on harnessing the power of massive parallelism. Conventional parallelization through spatial decomposition will not be effective since that will lead to fine granularity. The proposed effort is aimed at time-parallelization using a predictor-verifier approach. One of the key research issues is to develop appropriate predictors. Successful results from this endeavor can be integrated with multi-scale simulations that can predict material properties to time scales several orders of magnitude greater than that today.

The above research effort has applications in the areas of nanocoatings, nanosensors, nanoelectronics and nanocomposites. In the current stage of the development of nanotechnology, computation (theory, modeling and simulations) is playing a leading role, compared to experiments, because of size effects. One of the stumbling blocks to the widespread use of computational simulations is the difficulty in achieving realistic time scales. This research addresses this key issue. The specific applications mentioned above are but a few of the currently envisioned applications of nanotechnology for which this research on nanoscale interfaces will be directly applicable. Some of the fundamental understanding of both physics and computations has potential use for a wider class of applications, including nano-biotechnology.