COMPUTATIONAL MATERIALS SCIENCE NETWORK
of the
Department of Energy Basic Energy Sciences

COOPERATIVE RESEARCH TEAM
on

PREDICTIVE MODELING OF THE GROWTH AND PROPERTIES OF ENERGY-RELEVANT THIN FILMS AND NANOSTRUCTURES

In this proposal, a team of leading researchers with highly complementary expertise and a proven record of collaboration is assembled to address fundamentally important and computationally challenging issues in the broad areas of thin film growth and nanostructure formation, with emphasis on novel materials for renewable energy applications. The research thrusts are divided into two areas. The first area studies key materials and related computational issues in solar energy conversion for photovoltaic (PV) applications and water splitting via photocatalysis. Materials issues include semiconductor thin films with controlled morphology, dopant distributions, and band gaps. Control of thin film structure during growth is crucial to achieving high-performance materials in cost-effective PV and photocatalysis applications. In multi-junction solar cells, the control of dislocations is the key to further enhancement of cell efficiency; in polycrystalline PV thin films, grain boundaries and point defects may limit the performance of the systems. Predictive calculations provide a valuable tool for understanding the properties of these defects. Development of predictive theoretical techniques for such complex systems under nonequilibrium growth conditions demands a highly synergetic team effort of members covering different materials issues and length and time scales.

The second area is the first-principles based design of novel nanomaterials for energy storage. We will focus on two systems in this area: quantum metallic alloy films for hydrogen storage and novel carbon-based nanomaterials for energy applications. In the first systems, we will capitalize on the recent advances in precise control of the growth morphology of metal films in the quantum regime and use the tunable electronic densities at the Fermi level to tailor chemical reactions on the surfaces of such quantum catalysts for efficient decomposition of molecular hydrogen and high-capacity hydrogen storage. The second class of model systems will concern predictive design of light-element based nanomaterials, such as charged or metal-coated fullerenes and carbon nanotubes, metal-organic frameworks, as potential high-capacity hydrogen storage media. Here the challenge is to describe reliably the interaction energies of different natures, including weak/physical (van der Waals), chemical (Kubas), and/or electrostatic, between the molecular/atomic hydrogen and the nanoscale catalysts or storage materials; success in this area calls for team efforts of complementary expertise. The proposed research highly complements ongoing BES research programs and will be performed in close interaction with experimentalists for validation of conceptual advances, with the objective of advancing fundamental science in these areas.