Project Details
The development and advancement of theoretical/computational approaches are key to achieve the accuracy required to predict new materials with competitive degrees of freedom. Accurate theory for complex quantum materials demands the description of all competitive effects of comparable magnitude with similar
detail. In this project we focus on theoretical and computational studies using ab initio based approaches and high-performance supercomputing. Our overarching goal is to predict novel quantum materials and to understand the impact of defects, dopants, and interfaces on the properties of quantum materials with improved first-principles-based theory and computational approaches. This goal will be addressed by the following three specific aims: (1) Understand the impact of electronic correlation and localized defects on heterostructures, layered materials, and bulk systems, (2) Identify the changes in the ground state magnetic properties of materials induced by local and global symmetry breaking, and (3) Understand the interplay of topological properties with electronic correlation, magnetism, dopants and symmetry. We will apply and develop many-body approaches based on quantum Monte Carlo techniques (QMC) and large-scale Locally Self-consistent Multiple Scattering (LSMS) simulations and other state of the art codes. Computing assisted discovery methods based on machine learning will be developed to generate effective models and to predict novel quantum materials that are accessible through experimental synthesis and characterization approaches. This set of complementary techniques will allow us to have a comprehensive picture of materials systems across different energy and length scales involved in the experimental studies of quantum materials.
