This award was made on a 'small' category proposal submitted in response to the ITR solicitation, NSF-02-168. It supports computational and theoretical research and education aiming to elucidate novel phenomena in multifunctional materials. Based on the understanding obtained, the PI will attempt to design new materials with specific functionality. Research will focus on magnetoelectric materials, in particular magnetically ordered ferroelectrics. Specific questions that will be addressed by this work include: (1) What is the origin of the unusual ferroelectricity in the antiferromagentic hexagonal perovskite manganites? (2) Are these 'alternative' ferroelectric materials more widespread, and can our understanding of them be used to design novel ferroelectric materials? (3) Does a self-interaction corrected exchange-correlation functional predict the properties of magnetic materials accurately within the density functional formalism? Among the methods that the PI will use is a combination of standard density functional theory and density functional linear response implementations. The PI will contribute to extensions of density functional theory methods, including developing a self-interaction-corrected improvement to the exchange-correlation functional and incorporating spin-orbit coupling into the plane-wave pseudopotential formalism. Computer codes developed in the course of this work will be made available to the broader materials community. %%% This award was made on a 'small' category proposal submitted in response to the ITR solicitation, NSF-02-168. It supports computational and theoretical research and education on a class of multifunctional or smart materials that are at once ferroelectric and magnetic. The research will use computational methods and involves developing new algorithms to extend and improve density functional theory based methods. Resulting computer codes will be made available to the broader materials science community. Magnetoelectronic multiferroic materials have potential applications to spintronics and computer memory media and may impact future generations of information technology. This award involves graduate level education and helps to train the next generation of computational materials scientists. ***
