Abstract: Nuclear physics inputs are at the heart of many applied and fundamental science applications. These range from modeling nucleosynthesis in astrophysical environments to predicting the stability of unknown superheavy elements to the simulation of nuclear reactors. In many of these examples, important nuclear properties cannot always be measured, often because of the cost and difficulty of running experiments with very short-lived, radioactive nuclides. A predictive theory of nuclear structure, reaction and decays is, therefore, indispensable. In recent years, thanks in part to advances in high-performance computing, nuclear density functional theory (DFT) has become the tool of choice to study properties of nuclei across the entire nuclear chart within a fully quantum-mechanical framework.

The goal of this presentation is to give an overview of the ever-growing scope of DFT applications from nuclear masses to gamma- and beta decay to fission. I will also highlight how to leverage machine learning and artificial intelligence to enable the quantification and propagation of theoretical uncertainties to model predictions.