Mixed Stochastic-Deterministic Time-Dependent Density Functional Theory: Application to Stopping Power of Warm Dense Carbon

TL;DR

This paper introduces a mixed stochastic-deterministic time-dependent density functional theory approach to accurately and efficiently model the stopping power of warm dense carbon, aiding diagnostics and fusion research.

Contribution

The paper presents a novel mixed stochastic-deterministic TD-DFT method that improves computational efficiency while maintaining accuracy for warm dense matter applications.

Findings

Significant improvement over previous models in energy loss predictions

Effective application to warm dense carbon

Enhanced computational efficiency

Abstract

Warm dense matter (WMD) describes an intermediate phase, between condensed matter and classical plasmas, found in natural and man-made systems. In a laboratory setting, WDM needs to be created dynamically. It is typically laser or pulse-power generated and can be difficult to characterize experimentally. Measuring the energy loss of high energy ions, caused by a WDM target, is both a promising diagnostic and of fundamental importance to inertial confinement fusion research. However, electron coupling, degeneracy, and quantum effects limit the accuracy of easily calculable kinetic models for stopping power, while high temperatures make the traditional tools of condensed matter, e.g. Time-Dependent Density Functional Theory (TD-DFT), often intractable. We have developed a mixed stochastic-deterministic approach to TD-DFT which provides more efficient computation while maintaining the required precision for model discrimination. Recently, this approach showed significant improvement compared to models when compared to experimental energy loss measurements in WDM carbon. Here, we describe this approach and demonstrate its application to warm dense carbon stopping acr