Polymers for Extreme Conditions Designed Using Syntax-Directed Variational Autoencoders

TL;DR

This paper introduces a novel inverse design method using syntax-directed variational autoencoders and Gaussian process regression to generate polymers tailored for extreme conditions, enhancing materials discovery beyond traditional forward modeling.

Contribution

The work presents a new inverse design framework combining syntax-directed VAEs and GPR for targeted polymer discovery under extreme conditions, addressing limitations of conventional methods.

Findings

Successfully generated polymers for high temperature and electric field conditions.

Demonstrated the approach's ability to augment human ingenuity in materials design.

Applicable to other targeted properties and performance measures.

Abstract

The design/discovery of new materials is highly non-trivial owing to the near-infinite possibilities of material candidates, and multiple required property/performance objectives. Thus, machine learning tools are now commonly employed to virtually screen material candidates with desired properties by learning a theoretical mapping from material-to-property space, referred to as the \emph{forward} problem. However, this approach is inefficient, and severely constrained by the candidates that human imagination can conceive. Thus, in this work on polymers, we tackle the materials discovery challenge by solving the \emph{inverse} problem: directly generating candidates that satisfy desired property/performance objectives. We utilize syntax-directed variational autoencoders (VAE) in tandem with Gaussian process regression (GPR) models to discover polymers expected to be robust under three extreme conditions: (1) high temperatures, (2) high electric field, and (3) high temperature \emph{and} high electric field, useful for critical structural, electrical and energy storage applications. This approach to learn from (and augment) human ingenuity is general, and can be extended to discover polymers with other targeted properties and performance measures.