Office: Osborne A-444
Research Website: materials.uccs.edu
Address: 1420 Austin Bluffs Pkwy, Colorado Springs CO, 80918
The discovery of new materials drives new technological advances. There is significant interest in designing new materials as evidenced by the presidential initiative on "The Material Genome Project" and the establishment of exascale materials co-design centers at the department of energy laboratories. The development of supercomputers has enabled a new materials design process, which a large number of compounds are first screened computationally (high-throughput screening), using reliable quantum mechanical methods such as density functional theory (DFT); then the compounds that yield the most promising properties are synthesized in the laboratory. This new design paradigm can significantly reduce the cost of developing new materials.
High-throughput DFT calculations have been used to screen potential materials for high-performance piezoelectrics, high-strength alloys, Li-ion battery electrodes, etc. However, the majority of DFT calculations today are limited to a system size N, of hundreds of atoms. This limitation stems from the cubic-scaling computational complexity in conventional DFT implementations. In addition, these methods are often restricted to periodic boundary conditions, due to the use of the plane- wave basis functions. From a mechanical point of view, the properties of interest often derive from defects that involve far beyond hundreds of atoms, and cause non-periodic perturbations in the crystal structure. The size and periodicity restriction can be lifted using a real-space approach, combined with an algorithm that has linear-scaling complexity.
My research goal is to significantly reduce the computational cost of the real-space linear-scaling calculations for Kohn-Sham DFT, to pave the way for developing a fast ab initio molecular dynamics code that can be used to screen materials and model of defects at realistic concentrations. My research interests include real-space and linear-scaling methods for Kohn-Sham density functional theory (KS-DFT), numerical methods for eigen-solvers for large sparse matrices, applications of KS-DFT to study defects in materials and to improve the performance of Lithium-ion batteries.