  We introduce a new computational methodology for the identification and
characterization of free volume within/around atomistic configurations. This
scheme employs a three-stage workflow, by which spheres are iteratively grown
inside of voxels, and ultimately converted to planar graphs, which are then
characterized via a graph-based order parameter. Our approach is
computationally efficient, physically intuitive, and universally transferable
to any material system. Validation of our methodology is performed on several
sets of materials problems: (1) classification of unique free volumes in
various crystal phases, (2) characterization of free volume defects in
metals/alloys, and (3) autonomous detection and classification of complex
surface defects during epitaxial growth simulations. Our method accurately
identifies and characterizes unique free volumes over a multitude of systems
and length scales, indicating its potential for future use in understanding the
relationship between free volume morphology and material properties under both
static and dynamic conditions.
