Computational Molecular Science: Designing Improved Materials for Applications in Energy, Pharmaceutics and Desalination

Sunday, November 7, 2010
Hall 1 (Salt Palace Convention Center)
Amish J. Patel, Chemical and Biological Engineering, Rensselaer Polytechnic Institute, Troy, NY

Computational molecular science has been invaluable in helping us understand the underpinnings of diverse phenomena, ranging from nanotechnology to biology, spanning a wide spectrum of time and length scales. With the tremendous growth in computing power now available, we are on the verge of being able to use computation as a tool for designing novel materials. Novel polymer electrolytes provide the promise of delivering a lithium battery with high energy-density that could revolutionize the way we consume energy. However, judiciously chosen candidates need to be examined, using state-of-the-art modeling techniques in order to guide the search for the optimal electrolyte material. Similarly, in order to design drugs that target a specific site, we need to develop a fast and robust technique that can scan a library of candidates and suggest one that will bind optimally. This requires a detailed understanding of hydration of ligands and proteins and their association in solution. Finally, the rapid growth in world population has put an enormous burden on one of its most precious resources: potable water. Sea water is available in abundance, but in order to make it suitable for human consumption, it has to be desalinated. Carbon nanotubes have been shown to be promising candidates to tackle this task. Increasing interest in this field has led to a rapid growth in the available chemistries with which these nanotubes can be decorated. We need to identify the underlying principles of ion solvation and exclusion from nanotubes in the presence of the decorated moieties, in order to design nanotubes with maximal separation at minimal cost. While these are challenging problems, it is also extremely important that we expend every resource in order to overcome these challenges. I propose to employ a combination of statistical mechanics and molecular computation in order to realize this goal. -->

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