Rampi Ramprasad University of Connecticut The concept of rationally designing materials with novel properties through the effective use of computational methods and complementary experiments is an appealing notion, and forms the core of the recent U. S. White House Materials Genome Initiative. This paradigm for studying the materials and property space has the potential to mitigate the costs, risks and time involved in an Edisonian approach to the preparation and testing of potentially useful materials, and could yield valuable insights into the fundamental factors underlying materials behavior. Examples of past successful efforts along these lines will be provided. In particular, methods to explore the complex chemical space occupied by organic and organometallic polymers for electrostatic energy storage applications will be described. These investigations, performed using first principles computational methods and machine learning paradigms, combined with parallel experimental work, have lead to the rational design of new promising polymer dielectrics.