Abstract: Machine learning tools have tremendous potential to accelerate computationally complex physics-based simulations. One example is the need to accelerate materials discovery through first-principles Density Functional Theory (DFT) calculations. This seminar will encompass how machine learning interatomic potentials accelerate the discovery of crystalline nanoporous solids such as zeolites and metal-organic frameworks (MOFs) that are employed […]