Development of an ab initio learned model of electron deposition range in deuterium-tritium plasmas through time-dependent density functional theory calculations and machine learning.

Abstract

Accurate hydrodynamic modeling for laser-direct-drive (LDD) inertial confinement fusion (ICF) relies on precise calculations of the electron thermal conduction in all target materials. The nonlocal stopping range of electrons in ICF plasmas directly influences thermal conduction; yet, no first-principles model exists for the electron mean free path in the conduction-zone regime. This work utilized time-dependent stochastic density functional theory (TD-sDFT) to calculate the electron stopping power in deuterium-tritium (DT) plasmas at (ρ,T) conditions relevant to the conduction zone and the compressed shell in ICF. Using a combination of our TD-sDFT data and established analytical models, we developed and trained an artificial neural network to create a global model for the nonlocal electron deposition range, λ_{E}. We compared our machine learning (ML)-based model for λ_{E} to the currently used modified Lee-More model in LDD radiation-hydrodynamic codes, such as lilac, and saw an overall decrease in the deposition range. To understand the effects of λ_{E} on LDD ICF implosion dynamics, we implemented the ML-based model into lilac; specifically, we looked at designs consistent with a current experiment on the OMEGA laser and for a newly designed LDD-ICF target for the future OMEGA-Next facility. In both cases, we saw an overall drop in predicted ablation pressure, peak areal density, and neutron yield due to the reduced thermal conduction (smaller λ_{E}) in DT plasmas. Comparisons with the experiment on OMEGA are also made.