  Data-driven approaches to solve problems in materials science have gained
immense popularity in recent times due to their ability to predict unknown
material properties and uncover relationships between structure and property.
Machine learning algorithms like GBRT, random forest and neural networks have
had tremendous success in predicting target properties of materials and design
of structures for various applications. However, a major drawback for achieving
results within the required accuracy using these algorithms has been the need
for large datasets which can be challenging for problems when data is not
sufficiently available for training the models. In this work, we propose the
use of a hierarchical clustering algorithm which can work considerably better
on materials science problems with small dataset constraints. We apply the
algorithm to screen out promising double perovskite materials as candidates for
solar cells.
