Identification of novel organic ferroelectrics: A study combining importance sampling with machine learning

TL;DR

This study combines importance sampling and machine learning to identify promising new organic ferroelectrics, validated by first-principles simulations, advancing the discovery of environmentally friendly ferroelectric materials.

Contribution

It introduces a data-driven approach using importance sampling and machine learning to efficiently predict and shortlist potential organic ferroelectrics from a large chemical space.

Findings

Successfully predicted spontaneous polarization of candidate compounds

Validated machine learning predictions with first-principles simulations

Identified new promising organic ferroelectric materials

Abstract

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.