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

• URL: http://arxiv.org/abs/2511.16135v1
• Date: Thu, 20 Nov 2025 08:15:10 GMT
• Title: CoSP: Reconfigurable Multi-State Metamaterial Inverse Design via Contrastive Pretrained Large Language Model
• Authors: Shujie Yang, Xuzhe Zhao, Yuqi Zhang, Yansong Tang, Kaichen Dong,
• Abstract summary: We propose an intelligent inverse design method based on contrastive pretrained large language model (LLM)<n>CoSP can design corresponding thin-film metamaterial structures for arbitrary multi-state, multi-band optical responses.
• Score: 35.987300328917456
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

Related papers

• Active Learning and Explainable AI for Multi-Objective Optimization of Spin Coated Polymers [0.1486780669929473]
  Spin coating polymer thin films to achieve specific mechanical properties is inherently a multi-objective optimization problem.<n>We present a framework that integrates an active Pareto front learning algorithm (PyePAL) with visualization and explainable AI techniques to optimize processing parameters.
  arXiv  Detail & Related papers  (2025-09-10T20:35:59Z)
• Inverse Design in Nanophotonics via Representation Learning [0.0]
  Inverse design in nanophotos has become a key tool for targeted electromagnetic (EM) responses.<n>Machine learning (ML) has emerged to address these bottlenecks effectively.<n>This review frames ML through the lens representation learning, classifying them into two categories: output-side and input-side approaches.
  arXiv  Detail & Related papers  (2025-07-01T08:10:05Z)
• MOFGPT: Generative Design of Metal-Organic Frameworks using Language Models [5.417632175667162]
  Metal-Organic Frameworks (MOFs) with application-specific properties remain a central challenge in materials chemistry.<n>We present a reinforcement learning-enhanced, transformer-based framework for the de novo design of MOFs.<n>By integrating property feedback into sequence generation, our method drives the model toward synthesizable, topologically valid MOFs.
  arXiv  Detail & Related papers  (2025-05-30T20:09:11Z)
• Large Language-Geometry Model: When LLM meets Equivariance [53.8505081745406]
  We propose EquiLLM, a novel framework for representing 3D physical systems.<n>We show that EquiLLM delivers significant improvements over previous methods across molecular dynamics simulation, human motion simulation, and antibody design.
  arXiv  Detail & Related papers  (2025-02-16T14:50:49Z)
• MAPS: Advancing Multi-Modal Reasoning in Expert-Level Physical Science [62.96434290874878]
  Current Multi-Modal Large Language Models (MLLM) have shown strong capabilities in general visual reasoning tasks.<n>We develop a new framework, named Multi-Modal Scientific Reasoning with Physics Perception and Simulation (MAPS) based on an MLLM.<n>MAPS decomposes expert-level multi-modal reasoning task into physical diagram understanding via a Physical Perception Model (PPM) and reasoning with physical knowledge via a simulator.
  arXiv  Detail & Related papers  (2025-01-18T13:54:00Z)
• RA-BLIP: Multimodal Adaptive Retrieval-Augmented Bootstrapping Language-Image Pre-training [55.54020926284334]
  Multimodal Large Language Models (MLLMs) have recently received substantial interest, which shows their emerging potential as general-purpose models for various vision-language tasks. Retrieval augmentation techniques have proven to be effective plugins for both LLMs and MLLMs. In this study, we propose multimodal adaptive Retrieval-Augmented Bootstrapping Language-Image Pre-training (RA-BLIP), a novel retrieval-augmented framework for various MLLMs.
  arXiv  Detail & Related papers  (2024-10-18T03:45:19Z)
• CoMMIT: Coordinated Multimodal Instruction Tuning [90.1532838391285]
  Multimodal large language models (MLLMs) generally involve cooperative learning between a backbone LLM and a feature encoder of non-text input modalities.<n>In this paper, we analyze the MLLM instruction tuning from both theoretical and empirical perspectives.<n>We propose a Multimodal Balance Coefficient that enables quantitative measurement of the balance of learning.
  arXiv  Detail & Related papers  (2024-07-29T23:18:55Z)
• Many-Shot In-Context Learning for Molecular Inverse Design [56.65345962071059]
  Large Language Models (LLMs) have demonstrated great performance in few-shot In-Context Learning (ICL) We develop a new semi-supervised learning method that overcomes the lack of experimental data available for many-shot ICL. As we show, the new method greatly improves upon existing ICL methods for molecular design while being accessible and easy to use for scientists.
  arXiv  Detail & Related papers  (2024-07-26T21:10:50Z)
• LAMBO: Large AI Model Empowered Edge Intelligence [71.56135386994119]
  Next-generation edge intelligence is anticipated to benefit various applications via offloading techniques. Traditional offloading architectures face several issues, including heterogeneous constraints, partial perception, uncertain generalization, and lack of tractability. We propose a Large AI Model-Based Offloading (LAMBO) framework with over one billion parameters for solving these problems.
  arXiv  Detail & Related papers  (2023-08-29T07:25:42Z)
• Explainable Equivariant Neural Networks for Particle Physics: PELICAN [51.02649432050852]
  PELICAN is a novel permutation equivariant and Lorentz invariant aggregator network. We present a study of the PELICAN algorithm architecture in the context of both tagging (classification) and reconstructing (regression) Lorentz-boosted top quarks. We extend the application of PELICAN to the tasks of identifying quark-initiated vs.gluon-initiated jets, and a multi-class identification across five separate target categories of jets.
  arXiv  Detail & Related papers  (2023-07-31T09:08:40Z)
• Deep-Learning Empowered Inverse Design for Freeform Reconfigurable Metasurfaces [3.728073286482581]
  We present a deep-learning empowered inverse design method for freeform reconfigurable metasurfaces. A convolutional neural network model is trained to predict the responses of free-shaped meta-atoms. An inverse-designed wideband reconfigurable metasurface prototype is fabricated and measured for beam scanning applications.
  arXiv  Detail & Related papers  (2022-11-11T15:01:32Z)
• Multifunctional Meta-Optic Systems: Inversely Designed with Artificial Intelligence [1.076210145983805]
  We present an artificial intelligence framework for designing multilayer meta-optic systems with multifunctional capabilities. We demonstrate examples of a polarization-multiplexed dual-functional beam generator, a second order differentiator for all-optical computation, and a space-polarization-wavelength multiplexed hologram.
  arXiv  Detail & Related papers  (2020-06-30T22:15:15Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.