Data-Driven Design for Metamaterials and Multiscale Systems: A Review

Abstract

Metamaterials are artificial materials designed to exhibit effective material parameters that go beyond those found in nature. Composed of unit cells with rich designability that are assembled into multiscale systems, they hold great promise for realizing next-generation devices with exceptional, often exotic, functionalities. However, the vast design space and intricate structure-property relationships pose significant challenges in their design. A compelling paradigm that could bring the full potential of metamaterials to fruition is emerging: data-driven design. In this review, we provide a holistic overview of this rapidly evolving field, emphasizing the general methodology instead of specific domains and deployment contexts. We organize existing research into data-driven modules, encompassing data acquisition, machine learning-based unit cell design, and data-driven multiscale optimization. We further categorize the approaches within each module based on shared principles, analyze and compare strengths and applicability, explore connections between different modules, and identify open research questions and opportunities.

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Cited by in corpus (11)

• Experiment-informed finite-strain inverse design of spinodal metamaterials
• Generative Inverse Design of Metamaterials with Functional Responses by Interpretable Learning
• Data driven approaches in nanophotonics: A review of AI-enabled metadevices
• Inverse design of spinodoid structures using Bayesian optimization
• Chat to Chip: Large Language Model Based Design of Arbitrarily Shaped Metasurfaces
• Data-driven multifidelity topology design with multi-channel variational auto-encoder for concurrent optimization of multiple design variable fields
• Fixed-Attention Mechanism for Deep-Learning-Assisted Design of High-Degree-of-Freedom 3D Metamaterials
• TorchGDM: A GPU-Accelerated Python Toolkit for Multi-Scale Electromagnetic Scattering with Automatic Differentiation
• Transfer-learned Kolosov-Muskhelishvili Informed Neural Networks for Fracture Mechanics
• Numerical and data-driven modeling of spall failure in polycrystalline ductile materials
• Hetero-EUCLID: Interpretable model discovery for heterogeneous hyperelastic materials using stress-unsupervised learning