| Abstract Scope |
Tough materials are ubiquitous in protective equipment and structural elements. The more energy they can absorb per unit weight or volume translates to additional safety, cost effectiveness, or sustainability. However, measuring toughness requires physical testing, making design cycles slow and costly. To accelerate this process, we develop a self-driving lab (SDL) that combines additive manufacturing and mechanical testing to study the toughness of 3D printed components in a high-throughput and autonomous fashion. First, we benchmark the acceleration afforded by this SDL and it to be ~60 times faster than grid-based searching, which comprised the first experimental benchmarking of SDLs. Subsequently, we incorporate finite element analysis into this SDL to search in a physics-aware fashion. We also use the resulting databases to identify structures with superlative impact performance using transfer learning. Finally, we conduct an extensive experimental campaign in which we find structures that have superlative energy absorbing efficiency. |