Polymers for Extreme Conditions Designed Using Syntax-Directed Variational Autoencoders
This work addresses the materials discovery problem for polymer scientists and engineers, offering a generalizable method for targeted property design, though it builds incrementally on existing inverse problem approaches.
The paper tackled the challenge of designing polymers for extreme conditions by solving the inverse problem to directly generate candidates with desired properties, using syntax-directed VAEs and GPR to discover polymers robust under high temperatures and electric fields, with specific performance metrics reported for generated candidates.
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.