Multimodal Machine Learning for Soft High-k Elastomers under Data Scarcity
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- dielectric elastomers. Our data and implementation are publicly available at: https://github.com/HySonLab/Polymers
Cite
@article{arxiv.2601.18032,
title = {Multimodal Machine Learning for Soft High-k Elastomers under Data Scarcity},
author = {Brijesh FNU and Viet Thanh Duy Nguyen and Ashima Sharma and Md Harun Rashid Molla and Chengyi Xu and Truong-Son Hy},
journal= {arXiv preprint arXiv:2601.18032},
year = {2026}
}