Abstract
Lack of memory (locality in time) is a major limitation of almost all present time-dependent density functional approximations. By using semiclassical dynamics to compute correlation effects within a density-matrix functional approach, we incorporate memory, including initial-state dependence, as well as changing occupation numbers, and predict more observables in strong-field applications.
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