  Confinement can substantially alter the physicochemical properties of
materials by breaking translational isotropy and rendering all physical
properties position-dependent. Molecular dynamics (MD) simulations have proven
instrumental in characterizing such spatial heterogeneities and probing the
impact of confinement on materials' properties. For static properties, this is
a straightforward task and can be achieved via simple spatial binning. Such an
approach, however, cannot be readily applied to transport coefficients due to
lack of natural extensions of autocorrelations used for their calculation in
the bulk. The prime example of this challenge is diffusivity, which, in the
bulk, can be readily estimated from the particles' mobility statistics, which
satisfy the Fokker-Planck equation. Under confinement, however, such statistics
will follow the Smoluchowski equation, which lacks a closed-form analytical
solution. This brief review explores the rich history of estimating profiles of
the diffusivity tensor from MD simulations and discusses various approximate
methods and algorithms developed for this purpose. Beside discussing heuristic
extensions of bulk methods, we overview more rigorous algorithms, including
kernel-based methods, Bayesian approaches, and operator discretization
techniques. Additionally, we outline methods based on applying biasing
potentials or imposing constraints on tracer particles. Finally, we discuss
approaches that estimate diffusivity from mean first passage time or committor
probability profiles, a conceptual framework originally developed in the
context of collective variable spaces describing rare events in computational
chemistry and biology. In summary, this paper offers a concise survey of
diverse approaches for estimating diffusivity from MD trajectories,
highlighting challenges and opportunities in this area.
