  Data-driven methodology has become a key tool in computationally predicting
material properties. Currently, these techniques are priced high due to
computational requirements for generating sufficient training data for
high-precision machine learning models. In this study, we present a Support
Vector Regression (SVR)-based machine learning model to predict the stability
of silicon (Si) - alkaline metal alloys, with a strong emphasis on the
transferability of the model to new silicon alloys with different electronic
configurations and structures. We elaborate on the role of the structural
descriptor in imparting transferability to the model that is trained on limited
data (~750 Si alloys) derived from the Material Project database. Three popular
descriptors, namely X-Ray Diffraction (XRD), Sine Coulomb Matrix (SCM), and
Orbital Field Matrix (OFM), are evaluated for representing Si alloys. The
material structures are represented by descriptors in the SVR model, coupled
with hyperparameter tuning techniques like Grid Search CV and Bayesian
Optimization (BO), to find the best performing model for predicting total
energy, formation energy and packing fraction of the Si alloy systems. The
models are trained on Si alloys with lithium (Li), sodium (Na), potassium (K),
magnesium (Mg), calcium (Ca), and aluminum (Al) metals, where Si-Na and Si-Al
systems are used as test structures. Our results show that XRD, an
experimentally derived characterization of structures, performs most reliably
as a descriptor for total energy prediction of new Si alloys. The study
demonstrates that by qualitatively selection of training data, using
hyperparameter tuning methods, and employing appropriate structural
descriptors, the data requirements for robust and accurate ML models can be
reduced.
