Metamaterials genome: progress towards a community toolbox for ai metamaterials discovery

Earnshaw, Jacob; Syrotiuk, Nicholas; Duncan, Oliver; Kaczmarczyk, Lukasz; Scarpa, Fabrizio; Szyniszewski, Stefan

Abstract

Understanding the limits of the design space is a key aspect in optimising complex hierarchical structures and is vital for exploring and designing novel Metamaterials. Simultaneously, abundant data (mostly text, images, and location) aggregated by multinational corporations accelerated the development of machine learning and artificial intelligence technologies. Although increasingly conceptually advanced, the origins of machine learning can be traced back to traditional statistical methods and datacentric analysis. These techniques have been used in fields where establishing relationships and using differential equations or closed-form descriptions have been challenging due to the systems’ complexity. However, well-established and validated physics-based modelling tools offer direct solutions for various physical domains relevant to metamaterials. What is the right place for the emerging machine learning techniques in that context?

Citation

Earnshaw, J., Syrotiuk, N., Duncan, O., Kaczmarczyk, L., Scarpa, F., & Szyniszewski, S. (2024, April). Metamaterials genome: progress towards a community toolbox for ai metamaterials discovery. Presented at 2024 UK Association for Computational Mechanics Conference, Durham, UK

• Presentation Conference Type: Conference Paper (published)
• Conference Name: 2024 UK Association for Computational Mechanics Conference
• Start Date: Apr 10, 2024
• End Date: Apr 12, 2024
• Acceptance Date: Jan 26, 2024
• Online Publication Date: Apr 25, 2024
• Publication Date: Apr 25, 2024
• Deposit Date: Jun 21, 2024
• Publicly Available Date: Jul 11, 2024
• Pages: 70-73
• Book Title: UKACM Proceedings 2024
• DOI: https://doi.org/10.62512/conf.ukacm2024.025
• Public URL: https://durham-repository.worktribe.com/output/2488120