  The project aims to use machine learning algorithms to fit the free
parameters of an isotopic scaling model to elemental observations. The
processes considered are massive star nucleosynthesis, Type Ia SNe, the
s-process, the r-process, and p-isotope production. The analysis on the
successful fits seeks to minimize the reduced chi squared between the model and
the data. Based upon the successful refinement of the isotopic parameterized
scaling model, a table providing the 287 stable isotopic abundances as a
function of metallicity, separated into astrophysical processes, is useful for
identifying the chemical history of them. The table provides a complete
averaged chemical history for the Galaxy, subject to the underlying model
constraints.
