  The martensitic transformation in NiTi-based Shape Memory Alloys (SMAs)
provides a basis for shape memory effect and superelasticity, thereby enabling
applications requiring solid-state actuation and large recoverable shape
changes upon mechanical load cycling. In order to tailor the transformation to
a particular application, the compositional dependence of properties in
NiTi-based SMAs, such as martensitic transformation temperatures and
hysteresis, has been exploited. However, the compositional design space is
large and complex, and experimental studies are expensive. In this work, we
develop an interpretable piecewise linear regression model that predicts the
$\lambda_2$ parameter, a measure of compatibility between austenite and
martensite phases, and an (indirect) factor that is well-correlated with
martensitic transformation hysteresis, based on the chemical features derived
from the alloy composition. The model is capable of predicting, for the first
time, the type of martensitic transformation for a given alloy chemistry. The
proposed model is validated by experimental data from the literature as well as
in-house measurements. The results show that the model can effectively
distinguish between $B19$ and $B19^{\prime}$ regions for any given composition
in NiTi-based SMAs and accurately estimate the $\lambda_2$ parameter. Our
analysis also reveals that the weighted average of the quotient of the first
ionization energy and the Voronoi coordination number is a key compositional
characteristic that correlates with the $\lambda_2$ parameter and thermodynamic
responses, including the transformation hysteresis, martensite start
temperature, and critical temperature. The work herein demonstrates the
potential of data-driven methodologies for understanding and designing
NiTi-based SMAs with desired transformation characteristics.
