  The development of new materials is a core aspect of advancement in synthesis
and application for industry. There is a vast number of possible chemical
permutations of the basic elements that can be explored to synthesize materials
that possess attractive catalytic, mechanical and electrical properties that
may not be easily accessible to traditional experimental methods for various
reasons, including cost and time considerations. Nitrides, as examples, require
very stringent and precise conditions to successfully synthesize making their
experimental exploration very slow. In this paper, we employ the use of machine
learning algorithms to predict the bulk properties of Ternary Metal Nitrides
(TMN), specifically their bulk modulus which is correlated with the hardness of
the material. We were able to develop a consistent model with encouraging
accuracy, that was able to predict the bulk moduli of materials that previously
did not have computed values. The model was trained on $10^3$ ternary materials
with known elastic properties and defined structures, and was able to predict
the bulk modulus of $\thickapprox 1,000$ Ternary Metal Nitrides (TMNs) to
$\thickapprox 80\%$ accuracy. This approach is orders of magnitude faster than
the traditional computational approaches like density functional theory
(DFT)\cite{dft-paper} which makes exploratory identification of materials with
promising properties fast. We propose that such models be used to select
interesting candidates for high throughput computation from first principles.
