Multimodal Machine Learning for Soft High-k Elastomers under Data Scarcity

TL;DR

This paper introduces a multimodal machine learning framework that uses pretrained polymer representations and a curated dataset to predict properties of dielectric elastomers, addressing data scarcity and accelerating materials discovery.

Contribution

It presents a novel multimodal learning approach utilizing large-scale pretrained polymer embeddings for data-efficient property prediction of dielectric elastomers.

Findings

Effective few-shot prediction of dielectric and mechanical properties.

Curated high-quality dataset of acrylate-based dielectric elastomers.

Open-source implementation and dataset for community use.

Abstract

Dielectric materials are critical building blocks for modern electronics such as sensors, actuators, and transistors. With rapid advances in soft and stretchable electronics for emerging human- and robot-interfacing applications, there is a growing need for high-performance dielectric elastomers. However, developing soft elastomers that simultaneously exhibit high dielectric constants (k) and low Young's moduli (E) remains a major challenge. Although individual elastomer designs have been reported, structured datasets that systematically integrate molecular sequence, dielectric, and mechanical properties are largely unavailable. To address this gap, we curate a compact, high-quality dataset of acrylate-based dielectric elastomers by aggregating experimental results from the past decade. Building on this dataset, we propose a multimodal learning framework leveraging large-scale pretrained polymer representations. These pretrained embeddings transfer chemical and structural knowledge from vast polymer corpora, enabling accurate few-shot prediction of dielectric and mechanical properties and accelerating data-efficient discovery of soft high-$k$ dielectric elastomers. Our data and implementation are publicly available at: https://github.com/HySonLab/Polymers