QM simulations to identify improved photovoltaic materials
Noa Marom leads a Carnegie Mellon University team in an Argonne Early Science Project with plans to use Aurora, Argonne's exascale supercomputer, to find materials that can increase the efficiency of solar cells. They use machine learning tools extensively in their research and are working with the developers of BerkeleyGW, SISSO, and Dragonfly software to prepare to run on the Aurora system.
According to Marom, “The goal of our research is to find new materials that make photovoltaic solar cells more efficient. The quest for any new materials that can enable new technologies is challenging. The materials we are researching have unique properties that make them suitable for use in solar cells, and these properties are very rare and difficult to find out of the wide array of possible materials. We are trying to accelerate the process of material discovery through computer simulation on high-performance computers (HPC) using sophisticated quantum-mechanical simulation software and machine learning (ML) tools. We are excited that our project has been accepted as one of the projects that will run on the future Aurora supercomputer as part of the Argonne ESP program. Our multi-institution team is currently modifying algorithms and workflows so they will be able to run on Aurora.”