Nonlinear effects in light-ion stopping powers within real-time time-dependent density functional theory
Alina Kononov, Thomas W. Hentschel, Stephanie B. Hansen, Andrew D. Baczewski
TL;DR
The paper addresses the breakdown of linear-response scaling in electronic stopping powers for light ions in warm dense matter by leveraging real-time TDDFT to benchmark proton, alpha, and fractional-charge projectiles in aluminum and carbon. It shows nonlinear corrections of order about $10\%$ near the Bragg peak, challenging the common $S_Z(v) \propto Z^2$ prescription, and assesses several effective-charge models (e.g., Bohr, modified Bohr, Gus'kov) against TDDFT data. The study finds that some cases exhibit $Z_{\mathrm{eff}} > Z$, indicating nonlinearities beyond partial neutralization, and highlights material- and condition-dependent behavior that restricts universal modeling. Fractional-charge TDDFT tests help isolate linear-response contributions, underscoring the need to incorporate Barkas-type and higher-order effects into efficient stopping-power models for fusion and warm dense matter applications. These insights guide the development of more accurate, computationally efficient models that remain valid across relevant temperature, density, and projectile-velocity regimes.
Abstract
Electronic stopping power models describing fuel heating processes in inertial fusion energy concepts typically assume linear-response behavior through quadratic scaling with the projectile charge. We report the results of real-time time-dependent density functional theory (TDDFT) calculations indicating that even for low-Z ions, nonlinear processes modify stopping powers in warm dense matter by about 10% near and below the Bragg peak. By describing partial neutralization of slow ions, analytic effective charge models capture some qualitative aspects of the TDDFT results but do not always offer quantitative accuracy. Cases where the effective charge inferred from TDDFT exceeds the bare ion charge suggest that more complex nonlinear effects also contribute. These findings will inform future improvements to more efficient stopping power models.
