  Point defects have a strong influence on the physical properties of
materials, often dominating the electronic and optical behavior in
semiconductors and insulators. The simulation and analysis of point defects is
therefore crucial for understanding the growth and operation of materials
especially for optoelectronics applications. In this work, we present a
general-purpose Python framework for the analysis of point defects in
crystalline materials, as well as a generalized workflow for their treatment
with high-throughput simulations. The distinguishing feature of our approach is
an emphasis on a unique, unitcell, structure-only, definition of point defects
which decouples the defect definition and the specific supercell representation
used to simulate the defect. This allows the results of first-principles
calculations to be aggregated into a database without extensive provenance
information and is a crucial step in building a persistent database of point
defects that can grow over time, a key component towards realizing the idea of
a ``defect genome' that can yield more complex relationships governing the
behavior of defects in materials. We demonstrate several examples of the
approach for three technologically relevant materials and highlight current
pitfalls that must be considered when employing these methodologies, as well as
their potential solutions.
