  Recent advances in the synthesis of polar molecular materials have produced
practical alternatives to ferroelectric ceramics, opening up exciting new
avenues for their incorporation into modern electronic devices. However, in
order to realize the full potential of polar polymer and molecular crystals for
modern technological applications, it is paramount to assemble and evaluate all
the available data for such compounds, identifying descriptors that could be
associated with an emergence of ferroelectricity. In this work, we utilized
data-driven approaches to judiciously shortlist candidate materials from a wide
chemical space that could possess ferroelectric functionalities. An
importance-sampling based method was utilized to address the challenge of
having a limited amount of available data on already known organic
ferroelectrics. Sets of molecular- and crystal-level descriptors were combined
with a Random Forest Regression algorithm in order to predict spontaneous
polarization of the shortlisted compounds. First-principles simulations were
performed to further validate the predictions obtained from the machine
learning model.
