In the past few years, design of mechanical metamaterials has been empowered by computational tools that have allowed the community to overcome limitations of human intuition. By leveraging efficient optimization algorithms and computational physics models, it is now possible to explore vast design spaces, achieving new material functionalities with unprecedented performance. Here, we present our viewpoint on the state of the art of computational metamaterials design, discussing recent advances in topology optimization and machine learning design with respect to challenges in additive manufacturing.Computational tools have recently empowered mechanical metamaterials design. In this Perspective, advances to these approaches are discussed, notably mechanism-based design, topology optimization, the use of machine learning and the challenges for additive-manufactured metamaterial structures.

Computational design of mechanical metamaterials / S. Bonfanti, S. Hiemer, R. Zulkarnain, R. Guerra, M. Zaiser, S. Zapperi. - In: NATURE COMPUTATIONAL SCIENCE. - ISSN 2662-8457. - 4:8(2024), pp. 574-583. [10.1038/s43588-024-00672-x]

Computational design of mechanical metamaterials

S. Bonfanti
Primo
;
R. Zulkarnain;R. Guerra;S. Zapperi
Ultimo
2024

Abstract

In the past few years, design of mechanical metamaterials has been empowered by computational tools that have allowed the community to overcome limitations of human intuition. By leveraging efficient optimization algorithms and computational physics models, it is now possible to explore vast design spaces, achieving new material functionalities with unprecedented performance. Here, we present our viewpoint on the state of the art of computational metamaterials design, discussing recent advances in topology optimization and machine learning design with respect to challenges in additive manufacturing.Computational tools have recently empowered mechanical metamaterials design. In this Perspective, advances to these approaches are discussed, notably mechanism-based design, topology optimization, the use of machine learning and the challenges for additive-manufactured metamaterial structures.
Settore PHYS-04/A - Fisica teorica della materia, modelli, metodi matematici e applicazioni
   Centre of Excellence in Multifunctional Materials for Industrial and Medical Applications
   NOMATEN
   European Commission
   Horizon 2020 Framework Programme
   857470

   Tribo-Electricity: a New Route for Tribology (TRIEL)
   TRIEL
   MINISTERO DELL'UNIVERSITA' E DELLA RICERCA
   2022EY22PZ_001
2024
27-ago-2024
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2434/1109135
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