Elif Ertekin

Full-stack quantification of variability in predicting ion transport properties using machine-learned interatomic potentials

Machine-learned interatomic potentials (MLIPs) have become the state-of-the-art for performing accurate, scalable molecular dynamics (MD) simulations. It is, therefore, crucial to understand and quantify the reliability of MLIPs for downstream property predictions. Uncertainty in predicted properties can arise from lim...