用少样本多模态学习加速软高介电常数弹性体的发现
Multimodal Machine Learning for Soft High-k Elastomers under Data Scarcity
- 基于预训练聚合物表示的多模态框架，实现化学结构到性能的少样本预测
- 在仅有100+样本的数据下，准确预测介电常数和杨氏模量，显著提升研发效率
- 适合材料设计、人工智能辅助合成的研究者，尤其关注数据稀缺场景
介电材料是传感器、执行器和晶体管等现代电子器件的关键组件。随着柔性可拉伸电子技术在人机与机器人交互中的快速发展，高性能介电弹性体的需求日益增长。然而，同时具备高介电常数（k）和低杨氏模量（E）的软弹性体开发仍面临重大挑战。尽管已有个别弹性体设计被报道，但系统整合分子序列、介电与力学性能的结构化数据集仍极为匮乏。为此，我们汇集过去十年的实验数据，构建了一个小型但高质量的丙烯酸酯基介电弹性体数据集。基于该数据集，提出一种利用大规模预训练聚合物表征的多模态学习框架。这些预训练嵌入从海量聚合物语料中迁移化学与结构知识，实现对介电与力学性能的精准少样本预测，加速数据高效型软高-k介电弹性体的发现。数据与代码已公开：https://github.com/HySonLab/Polymers
原文摘要 · Abstract (English)
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
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