METASET: Exploring Shape and Property Spaces for Data-Driven Metamaterials Design

TL;DR

This paper introduces METASET, a method for selecting diverse, representative subsets of unit cells in shape and property spaces to improve data-driven design of metamaterials, reducing dataset bias and enhancing search efficiency.

Contribution

METASET employs similarity metrics and Determinantal Point Processes to efficiently select diverse unit cell subsets, enabling scalable and unbiased metamaterials design.

Findings

Smaller, diverse subsets improve search and structural performance.

METASET effectively distills unique subsets regardless of similarity metrics.

Diverse subsets are publicly available for designers.

Abstract

Data-driven design of mechanical metamaterials is an increasingly popular method to combat costly physical simulations and immense, often intractable, geometrical design spaces. Using a precomputed dataset of unit cells, a multiscale structure can be quickly filled via combinatorial search algorithms, and machine learning models can be trained to accelerate the process. However, the dependence on data induces a unique challenge: An imbalanced dataset containing more of certain shapes or physical properties can be detrimental to the efficacy of data-driven approaches. In answer, we posit that a smaller yet diverse set of unit cells leads to scalable search and unbiased learning. To select such subsets, we propose METASET, a methodology that 1) uses similarity metrics and positive semi-definite kernels to jointly measure the closeness of unit cells in both shape and property spaces, and 2) incorporates Determinantal Point Processes for efficient subset selection. Moreover, METASET allows the trade-off between shape and property diversity so that subsets can be tuned for various applications. Through the design of 2D metamaterials with target displacement profiles, we demonstrate that smaller, diverse subsets can indeed improve the search process as well as structural performance. By eliminating inherent overlaps in a dataset of 3D unit cells created with symmetry rules, we also illustrate that our flexible method can distill unique subsets regardless of the metric employed. Our diverse subsets are provided publicly for use by any designer.