PREDICTING REAL OPTIMIZED MATERIALS: TAILORING MOLECULAR POTENTIALS TO DISCOVER OPTIMUM MOLECULES

Grant Number:    
Funding Agency: DARPA (white paper)
PI: David N. Beratan, Weitao Yang, John D. Simon Department of Chemistry; Donald J. Rose Department of Computer Science; Michael J. Therien,Department of Chemistry,University of Pennsylvania
Effective Dates: 2004/05-2009/04
Amount: $1,100,000

Description: The goal of this proposal, simply put, is to revolutionize the way that we design new materials. Rather than settle for a “local optimization” within the framework of structure- function relationships for existing classes of materials, we propose a new strategy. If successful, our approach will sample molecular space globally, so that we may search for new molecules and materials with globally optimized properties. Our underlying hypothesis is that the practical limitations of synthesis and characterization of the factorial number of possible molecules will always restrict the screening of new materials to a trivial region of “molecular space.” As such, there is an urgent need for practical and novel approaches to discover new classes of optimized materials. The approach will proceed in two conceptually simple steps. First, we will determine the optimum potential function for a given property. Second, we will determine chemical structures that come closest to realizing this optimum potential..