Article,
De Novo Atomistic Discovery of Disordered Mechanical Metamaterials by Machine Learning
Affiliations
- [1] AIMSOLID Research, Wuhan, 430223, China [NORA names: China; Asia, East];
- [2] Sichuan University [NORA names: China; Asia, East];
- [3] Southern University of Science and Technology [NORA names: China; Asia, East];
- [4] Aalborg University [NORA names: AAU Aalborg University; University; Denmark; Europe, EU; Nordic; OECD];
- [5] University of California, Berkeley [NORA names: United States; America, North; OECD];
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Abstract
Architected materials design across orders of magnitude length scale intrigues exceptional mechanical responses nonexistent in their natural bulk state. However, the so-termed mechanical metamaterials, when scaling bottom down to the atomistic or microparticle level, remain largely unexplored and conventionally fall out of their coarse-resolution, ordered-pattern design space. Here, combining high-throughput molecular dynamics (MD) simulations and machine learning (ML) strategies, some intriguing atomistic families of disordered mechanical metamaterials are discovered, as fabricated by melt quenching and exemplified herein by lightweight-yet-stiff cellular materials featuring a theoretical limit of linear stiffness-density scaling, whose structural disorder-rather than order-is key to reduce the scaling exponent and is simply controlled by the bonding interactions and their directionality that enable flexible tunability experimentally. Importantly, a systematic navigation in the forcefield landscape reveals that, in-between directional and non-directional bonding such as covalent and ionic bonds, modest bond directionality is most likely to promotes disordered packing of polyhedral, stretching-dominated structures responsible for the formation of metamaterials. This work pioneers a bottom-down atomistic scheme to design mechanical metamaterials formatted disorderly, unlocking a largely untapped field in leveraging structural disorder in devising metamaterials atomistically and, potentially, generic to conventional upscaled designs.