# A data-driven design for sound absorption of acoustic metamaterials based on large language models

## TL;DR

This paper introduces user-friendly strategies using large language models to design acoustic metamaterials for sound absorption, reducing the need for ML expertise.

## Contribution

The study proposes two novel data-driven strategies—agent interaction and LLM fine-tuning—for acoustic metamaterial design using LLMs.

## Key findings

- The agent interaction strategy enables ChatGPT to design acoustic metamaterials in one minute through dialogue.
- Fine-tuned LLMs outperform conventional ML models in accuracy for metamaterial design.
- Fine-tuned LLMs can evolve into specialized models for metamaterials through continuous training.

## Abstract

Machine learning (ML)-based data-driven approaches are extensively employed in forward and inverse acoustic metamaterial design, as evidenced by numerous research papers published in recent years. These studies require advanced ML knowledge and coding skills. Furthermore, the proposed ML models lack generalizability, being tailored to specific structures and hard to apply broadly, limiting practical applications. To address these issues, this study establishes two data-driven design strategies—agent interaction and large language model (LLM) fine-tuning—based on LLMs, eliminating the need for specialized ML knowledge. This approach provides a universal user-friendly strategy for acoustic metamaterial design. The agent interaction strategy enables ChatGPT to act as an independent agent, mapping structural parameters to sound absorption coefficients through simple text interactions, thereby facilitating both forward and inverse design. The LLM fine-tuning strategy involves retraining DeepSeek using acoustic metamaterial datasets, adjusting specific model parameters to enable performance prediction or inverse design. Results indicate that the agent interaction strategy can design acoustic metamaterials within one minute solely through dialogue and instruction. The fine-tuned LLM strategy yields design outcomes with higher accuracy compared to the conventional ML model. Additionally, the fine-tuned LLM can evolve into a specialized LLM for the metamaterial domain through continuous fine-tuning. The proposed strategies validate the application potential of LLMs in data-driven metamaterial design and provide significant guidance for advancing this field.