Machine learning (ML) is rapidly accelerating how we inverse design the microstructures of metamaterials for targeted and exotic properties (e.g., stress-strain response), bypassing time- and resource-intensive trial-and-error. However, ML for metamaterials design is facing some critical bottlenecks namely, what if the design parameterization and property representation are

- extremely high-dimensional,
- uninterpretable (e.g., text- or graph-based) to a numerical optimization algorithm, and/or
- discrete and discontinuous (e.g., ad hoc truss- and plate-based lattices)?

To address these challenges, we introduce a general ML framework for inverse design of metamaterials with high-dimensional and/or non-trivial parameterizations. The framework generally consists of two ML models – one to extract finite-sized and low-dimensional design-to-property maps and another one to invert the process and obtain property-to-design maps. Leveraging specially designed realizations of the general ML framework, we inverse design metamaterials based on implicit geometries, physical descriptors-based surface-energy minimizing microstructures, and graph-based truss lattices for target properties including anisotropic stress-strain response and topology.

Sid Kumar is an Assistant Professor at TU Delft in the Department of Material Science and Engineering since 2021. He obtained his Ph.D. in Aeronautics from Caltech in 2019 followed by a postdoc position at ETH Zürich. Previously, he obtained a dual M.S. in 2017 from Caltech in Aeronautics and Ecole Polytechnique (France) in Multiscale and Multiphysics Modeling, and a B.Tech. in Mechanical Engineering from IIT Delhi in 2014. He received the Foster and Coco Stanback fellowship in Engineering and Applied Science at Caltech and the University of Paris Saclay fellowship at Ecole Polytechnique. His research interests lie at the intersection of mechanics of materials, computational modeling, and machine learning — with a focus on inverse problems in (meta-)material design and modeling.