Predicting structure formation across length scales - from crystal structures to nanoscale assembly of hybrid materials.

Richard Hennig
Cornell University

Predictions of structure formation by computational methods have the potential to accelerate materials discovery and design. Here we present two strategies for structure discovery. The first approach - based on random search methods and evolutionary algorithms coupled to ab-initio relaxations on modern supercomputers - can accurately predict how atoms arrange into crystal structure without any prior information about the system. The second approach is based on a continuum description of materials by their composition or chemical fields and our newly developed self-consistent field approach for hybrid polymer/nanoparticle systems. We present results for the Li-Be system and for Eu under pressure and for the self-assembly of block-copolymer/nanoparticle hybrid materials. For Li-Be we discover several stable phases under pressures and observe a rather unexpected quasi-1D and 2D electronic structure in some of the compounds. For Eu we identify three phase transformations. Following the well-known bcc-to-hcp transition at 12 GPa, a mixed phase region is observed from 18 to 66 GPa until finally
a single orthorhombic (Pnma) phase persists from 66 to 92 GPa.The Pnma phase becomes superconducting above 84 GPa.
For the continuum description of materials, we developed a theoretical framework that unifies polymer field theory and density functional theory in order to efficiently predict ordered nanostructure formation in systems having considerable complexity in terms of molecular structures and interactions. We illustrate the power of our approach by applying it to study the self-assemblies of a block-copolymer/nanoparticle hybrid materials system and show that nanoparticle crystal formation is possible for sufficiently large interactions between the nanoparticles.

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