Hu Group at Georgia Tech MSE

Efficient and sustainable energy harvesting and utilization represent one of the most pressing scientific and engineering challenges of our time. A central objective in this field is the discovery of high-performance, low-cost materials that enable next-generation energy technologies. Achieving this goal, however, requires innovative strategies that move beyond traditional trial-and-error or Edisonian approaches to materials discovery. Our group at Georgia Tech develops integrated computational frameworks that combine high-throughput density functional theory (DFT), quantum Monte Carlo (QMC), data-driven screening, machine learning potentials (MLPs), and grand canonical (GC) modeling. These approaches provide atomic-level mechanistic insights into material behaviors and properties. Building on this understanding, we establish experimentally realizable design principles that accelerate the development of advanced energy technologies. The synergy between physics-based simulations and data-driven techniques enables rapid, targeted design of next-generation materials. Executing these workflows requires leveraging a broad range of computational resources, ranging from personal workstations to high-performance computing clusters such as Frontier at Oak Ridge National Laboratory (ORNL) with hundreds of thousands of CPU cores. These capabilities allow our group to perform large-scale, high-fidelity simulations that drive innovation in materials design for energy applications.