  The Fe14Nd2B-based permanent magnets are technologically sought-after for
energy conversion due to their unparalleled high energy product (520 kJ/m3).
For such 14:2:1 phases of different compositions, determining the magnetization
from the measured magnetic moment is often bottlenecked by lack of mass
density. We present a machine learning (ML) mass density model for 14:2:1
phases using chemical composition-based features (representing 33 elements) and
optionally lattice parameter (LP) features. The datasets for training and
testing contain 190 phases (177 compositionally different) with their
literature reported densities and LP. With an ML model with merely
compositional features, we achieved a low mean-absolute-error of 0.51% on an
unseen test-dataset.
