  Fueled by the widespread adoption of Machine Learning (ML) and the
high-throughput screening of materials, the data-centric approach to materials
design has asserted itself as a robust and powerful tool for the in-silico
prediction of materials properties. When training models to predict material
properties, researchers often face a difficult choice between a model's
interpretability or its performance. We study this trade-off by leveraging four
different state-of-the-art ML techniques: XGBoost, SISSO, Roost, and TPOT for
the prediction of structural and electronic properties of perovskites and 2D
materials. We then assess the future outlook of the continued integration of ML
into materials discovery and identify key problems that will continue to
challenge researchers as the size of the literature's datasets and complexity
of models increases. Finally, we offer several possible solutions to these
challenges with a focus on retaining interpretability and share our thoughts on
magnifying the impact of ML on materials design.
