CoSP: Reconfigurable Multi-State Metamaterial Inverse Design via Contrastive Pretrained Large Language Model

TL;DR

This paper introduces CoSP, a novel inverse design method for reconfigurable multi-state metamaterials using contrastive pretrained large language models, enabling natural language descriptions of complex optical properties.

Contribution

It presents a new contrastive pretraining approach that combines spectrum understanding with language modeling to improve the inverse design of multi-state metamaterials.

Findings

Successfully designs thin-film metamaterials for arbitrary multi-state optical responses

Demonstrates the model's ability to understand and describe complex spectra in natural language

Shows potential for versatile applications in reconfigurable metamaterial design

Abstract

Metamaterials, known for their ability to manipulate light at subwavelength scales, face significant design challenges due to their complex and sophisticated structures. Consequently, deep learning has emerged as a powerful tool to streamline their design process. Reconfigurable multi-state metamaterials (RMMs) with adjustable parameters can switch their optical characteristics between different states upon external stimulation, leading to numerous applications. However, existing deep learning-based inverse design methods fall short in considering reconfigurability with multi-state switching. To address this challenge, we propose CoSP, an intelligent inverse design method based on contrastive pretrained large language model (LLM). By performing contrastive pretraining on multi-state spectrum, a well-trained spectrum encoder capable of understanding the spectrum is obtained, and it subsequently interacts with a pretrained LLM. This approach allows the model to preserve its linguistic capabilities while also comprehending Maxwell's Equations, enabling it to describe material structures with target optical properties in natural language. Our experiments demonstrate that CoSP can design corresponding thin-film metamaterial structures for arbitrary multi-state, multi-band optical responses, showing great potentials in the intelligent design of RMMs for versatile applications.