Strongly correlated materials hold a promise of revolutionary functionalities ranging from energy transmission to superior thermoelectric performance. But understanding the properties and functionalities of these materials is difficult as standard analytical tools are not well suited for their study. What's needed is a tool by which a user can rapidly and easily characterize strongly correlated materials and so reveal their possible functionalities. The goal of this center is to produce such a tool in the form of a suite of software termed the Dynamical Mean Field Theory-Material Design Lab (DMFT-MatDeLab).
This tool will allow users to characterize materials using dynamic mean field theory (DMFT) with first principles input which has already been proven as a technique to describe material properties in strongly correlated systems. But its significant learning curve has limited its use to a select few. DMFT-MatDeLab will eliminate this barrier allowing scientists working on strongly correlated systems to theoretically characterize these systems and permit strongly correlated material design to flourish.
Combining our codes with both the rapid developments in HPC and experimental validation, the impact of this project will be felt in all the key areas targeted by the Department of Energy including energy storage, energy transmission, and strongly correlated electron materials. We will produce a tool that will allow scientists who are not expert numericists nor experts in DMFT to readily understand and predict the material properties of strongly correlated systems.
The development of the software DMFT-MatDeLab follows the scientific approaches detailed in the DOE report Computational Materials Science and Chemistry: Accelerating Discovery and Innovation through Simulation-Based Engineering and Science in a number of ways.