Computational Science Laboratory Projects
Center for Computational Design of Functional Strongly Correlated Materials and Theoretical Spectroscopy
Strongly correlated materials hold a promise of revolutionary functionalities ranging from energy transmission to superior thermoelectric performance. However to understand the properties and functionalities of strongly correlated materials is a difficult task. Standard analytical tools are not well suited for the study of these materials – these were instead designed to understand materials when interactions are weak and the single electron picture is adequate to their description. While strongly correlated materials can be understood, it is always an arduous, time intensive task. This in turn makes material discovery difficult if not impossible. What is then needed is a tool by which a user can rapidly and easily characterize strongly correlated materials and so reveal their possible functionalities. It is the goal of this proposal to produce such a tool in the form of a suite of software termed DMFT-Material Design Lab or DMFT-MatDeLab for short. This tool will allow users to characterize materials using dynamic mean field theory (DMFT) with first principles input. First principles+DMFT have already been proven as a technique to describe material properties in strongly correlated systems. But its inherent learning curve has limited it’s use to a select few. DMFT-MatDeLab will eliminate this barrier allowing scientists working on strongly correlated systems to theoretically characterize these systems and concomitantly permit strongly correlated material design to flourish.
DMFT-MatDeLab will contain a host of tools. These tools will run the gamut, from allowing a user to do a rapid characterization of a material to permitting a more careful, more intensive, analysis. DMFT-MatDeLab will include multiscale capabilities including DMFT derived Landau-Ginzburg theories and DMFT infused molecular dynamics.
The Computational Science Laboratory is assisting in the effort to improve performance of the code. Preliminary work has been done to implement portions of the code using Cuda enabling utilization of GPUs. This early effort has already yielded performance increases of 1.8 to 3.5 times speedup in the sections of the code ported to GPU.