Graph-based Learning and design of Advanced Mechanical Metamaterials

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The emergence of additive manufacturing techniques has enabled the creation of complex 3D shapes with topological feature sizes spanning length scales from nanometres upwards. These manufacturing technologies have facilitated the creation of new materials (metamaterials) with previously unattainable properties such as light and recoverable ceramics. However, defects (deviations from the design) caused by manufacturing variabilities proliferate in topologically complex printed samples comprising millions of micro-scale elements. Traditional numerical simulations do not capture these a priori unknown defects, and thus the measured properties of fabricated metamaterials invariably deviate substantially from the designed/simulated properties. This low fidelity of the metamaterial simulation tools has left a vast portion of the metamaterial design space untapped. Leveraging recent foundational advances in machine learning and graph neural networks (GNNs), now is the ideal time for designing new additively manufactured materials with fully tailorable static and dynamic properties wherein, for example, a designer inputs a wave transmission spectrum from which a metamaterial that fully replicates the input response in an experimental setting is inversely designed and printed. Graph-based data-driven methods can address this challenge by their ability to learn from experimental data and efficiently encode the 3D material topology. The proposal will break new ground by exploiting breakthroughs in graph-based generative machine learning models to inversely generate metamaterials and thereby fuse the field of GNNs with mechanics, materials science, and additive manufacturing. This represents a fundamentally new realm of engineered material creation and discovery paradigm that will bridge the longstanding gap between simulation and experimental data of 3D printed metamaterials. The project will lay the scientific foundations for new engineering material designs and solutions.