Leveraging Topological Constraints to Accelerate Spontaneous Electric Polarization Calculations
Abigail Poteshman, University of Chicago
This project leverages topological constraints to automate and accelerate a new algorithm to compute a property of materials known as “spontaneous electric polarization.” This property is used to identify a class of materials known as “ferroelectrics,” which have applications in neuromorphic computing, non-volatile memory devices, and tunable capacitors. Beyond identifying ferroelectrics, the algorithm I am developing has the potential to efficiently generate high-quality training data for machine learning tasks, including exploring structure-property relationships to discover novel materials with nontrivial topological phases. This algorithm takes as input electronic wavefunctions from ab initio electronic structure codes, which solve high-dimensional eigenvalue equations to obtain electronic probability distributions based on a material's nuclear coordinates. On this poster, I demonstrate how enforcing topological constraints across the electronic wavefunctions during our calculation enables us to circumvent a numerical branch selection issue that plagues the current state-of-the-art procedure to calculate spontaneous electric polarization. I also show that this algorithm drastically reduces the computational resources necessary for computing this quantity. By automating and accelerating this calculation, we can increase the range, scope, and size of materials for which spontaneous electric polarization can be calculated given the capacity of modern HPC resources.