Toward a Robust and Generalizable Metamaterial Foundation Model
- URL: http://arxiv.org/abs/2507.02436v1
- Date: Thu, 03 Jul 2025 08:48:36 GMT
- Title: Toward a Robust and Generalizable Metamaterial Foundation Model
- Authors: Namjung Kim, Dongseok Lee, Jongbin Yu, Sung Woong Cho, Dosung Lee, Yesol Park, Youngjoon Hong,
- Abstract summary: We introduce the Metamaterial Foundation Model (MetaFO), a Bayesian transformer-based foundation model inspired by large language models.<n>By treating metamaterials as an operator that maps material properties to structural responses, MetaFO uncovers intricate structure-property relationships.<n>This scalable and generalizable framework marks a paradigm shift in AI-driven metamaterial discovery, paving the way for next-generation innovations.
- Score: 3.0049721990828084
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Advances in material functionalities drive innovations across various fields, where metamaterials-defined by structure rather than composition-are leading the way. Despite the rise of artificial intelligence (AI)-driven design strategies, their impact is limited by task-specific retraining, poor out-of-distribution(OOD) generalization, and the need for separate models for forward and inverse design. To address these limitations, we introduce the Metamaterial Foundation Model (MetaFO), a Bayesian transformer-based foundation model inspired by large language models. MetaFO learns the underlying mechanics of metamaterials, enabling probabilistic, zero-shot predictions across diverse, unseen combinations of material properties and structural responses. It also excels in nonlinear inverse design, even under OOD conditions. By treating metamaterials as an operator that maps material properties to structural responses, MetaFO uncovers intricate structure-property relationships and significantly expands the design space. This scalable and generalizable framework marks a paradigm shift in AI-driven metamaterial discovery, paving the way for next-generation innovations.
Related papers
- DiffuMeta: Algebraic Language Models for Inverse Design of Metamaterials via Diffusion Transformers [0.6531893282486697]
We present DiffuMeta, a generative framework integrating diffusion transformers with a novel algebraic language representation, encoding 3D geometries as mathematical sentences.<n>This compact, unified parameterization spans diverse topologies while enabling direct application of transformers to structural design.<n>Our approach enables simultaneous control over multiple mechanical objectives, including linear and nonlinear responses beyond training domains.
arXiv Detail & Related papers (2025-07-21T16:09:26Z) - A Survey of Model Architectures in Information Retrieval [64.75808744228067]
We focus on two key aspects: backbone models for feature extraction and end-to-end system architectures for relevance estimation.<n>We trace the development from traditional term-based methods to modern neural approaches, particularly highlighting the impact of transformer-based models and subsequent large language models (LLMs)<n>We conclude by discussing emerging challenges and future directions, including architectural optimizations for performance and scalability, handling of multimodal, multilingual data, and adaptation to novel application domains beyond traditional search paradigms.
arXiv Detail & Related papers (2025-02-20T18:42:58Z) - Generalized Factor Neural Network Model for High-dimensional Regression [50.554377879576066]
We tackle the challenges of modeling high-dimensional data sets with latent low-dimensional structures hidden within complex, non-linear, and noisy relationships.<n>Our approach enables a seamless integration of concepts from non-parametric regression, factor models, and neural networks for high-dimensional regression.
arXiv Detail & Related papers (2025-02-16T23:13:55Z) - Cliqueformer: Model-Based Optimization with Structured Transformers [102.55764949282906]
Large neural networks excel at prediction tasks, but their application to design problems, such as protein engineering or materials discovery, requires solving offline model-based optimization (MBO) problems.<n>We present Cliqueformer, a transformer-based architecture that learns the black-box function's structure through functional graphical models (FGM)<n>Across various domains, including chemical and genetic design tasks, Cliqueformer demonstrates superior performance compared to existing methods.
arXiv Detail & Related papers (2024-10-17T00:35:47Z) - Nonlinear Inverse Design of Mechanical Multi-Material Metamaterials Enabled by Video Denoising Diffusion and Structure Identifier [2.624762732763203]
This paper presents a novel framework for inverse multi-material design based on nonlinear stress-strain responses.
By incorporating multiple materials, plasticity, and large deformation, our innovative design method allows for enhanced control over the highly nonlinear mechanical behavior of metamaterials.
arXiv Detail & Related papers (2024-09-20T21:26:15Z) - ALPINE: Unveiling the Planning Capability of Autoregressive Learning in Language Models [48.559185522099625]
Planning is a crucial element of both human intelligence and contemporary large language models (LLMs)
This paper investigates the emergence of planning capabilities in Transformer-based LLMs via their next-word prediction mechanisms.
arXiv Detail & Related papers (2024-05-15T09:59:37Z) - VAE for Modified 1-Hot Generative Materials Modeling, A Step Towards
Inverse Material Design [0.0]
In inverse material design, where one seeks to design a material with a prescribed set of properties, a significant challenge is ensuring synthetic viability of a proposed new material.
We encode an implicit dataset relationships, namely that certain materials can be decomposed into other ones in the dataset.
We present a VAE model capable of preserving this property in the latent space and generating new samples with the same.
arXiv Detail & Related papers (2023-12-25T04:04:47Z) - Data-Driven Design for Metamaterials and Multiscale Systems: A Review [15.736695579155047]
Metamaterials are artificial materials designed to exhibit effective material parameters that go beyond those found in nature.
A compelling paradigm that could bring the full potential of metamaterials to fruition is emerging: data-driven design.
We organize existing research into data-driven modules, encompassing data acquisition, machine learning-based unit cell design, and data-driven multiscale optimization.
arXiv Detail & Related papers (2023-07-01T22:36:40Z) - DA-VEGAN: Differentiably Augmenting VAE-GAN for microstructure
reconstruction from extremely small data sets [110.60233593474796]
DA-VEGAN is a model with two central innovations.
A $beta$-variational autoencoder is incorporated into a hybrid GAN architecture.
A custom differentiable data augmentation scheme is developed specifically for this architecture.
arXiv Detail & Related papers (2023-02-17T08:49:09Z) - How to See Hidden Patterns in Metamaterials with Interpretable Machine
Learning [82.67551367327634]
We develop a new interpretable, multi-resolution machine learning framework for finding patterns in the unit-cells of materials.
Specifically, we propose two new interpretable representations of metamaterials, called shape-frequency features and unit-cell templates.
arXiv Detail & Related papers (2021-11-10T21:19:02Z) - Deep Generative Modeling for Mechanistic-based Learning and Design of
Metamaterial Systems [20.659457956055366]
We propose a novel data-driven metamaterial design framework based on deep generative modeling.
We show in this study that the latent space of VAE provides a distance metric to measure shape similarity.
We demonstrate our framework by designing both functionally graded and heterogeneous metamaterial systems.
arXiv Detail & Related papers (2020-06-27T03:56:55Z)
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.