Development of a Nonlocal Electron Transport Model in DT through TD-DFT Calculations and Machine Learning for Inertial Confinement Fusion

Accurate modeling of electron transport is fundamental to measure the total thermal conduction and ablation in inertial confinement fusion (ICF) laser-direct-drive (LDD) simulations. The nonlocal electron mean free path plays a central role in thermal conduction models; to improve the current calculation used in hydrodynamic codes, such as LILAC, we utilized Time-Dependent Density-Functional-Theory (TD-DFT) to calculate the electron stopping power and corresponding deposition range in dense DT plasmas. To adequately span the ICF conduction zone regime for DT, we used machine learning (ML) to develop a model to fit the TD-DFT SP data and extrapolate to fit the desired (ρ, T) regime. We directly compared our ML-based model to currently used models for the mean free path and performed 1D hydrodynamic simulations to understand the effects of our model for the electron deposition range on implosion dynamics.