Exploring Organic Ferroelectrics Using Data-driven Approaches

Recent advances in the synthesis of polar molecular materials have produced practical alternatives to ferroelectric ceramics, opening up exciting new avenues for the incorporation of such compounds into modern electronic devices. However, in order to fully realize the prospects of polar polymer and molecular crystals for modern technological applications, it is paramount to acquire diverse datasets of potential organic ferroelectrics such that the mechanisms governing the emergence of ferroelectricity can be studied. Here we propose to use data-driven approaches to judiciously shortlist candidates from a wide range of chemical space with ferroelectric functionalities. First, this investigation will be governed by identification of chemical similarities between existing molecular compounds exhibiting similar ferroelectric behavior. Second, we investigate machine learning (ML) and deep neural network models for estimating charge transfer effects in organic chemistry. The dipole moment and ferroelectric properties estimated by ML can then be used to supplement the data-driven screening of possible organic ferroelectrics.