Unravelling the Extreme Mechanics of Hierarchical Polymers using Self-Driving Labs

The development of advanced materials is extremely slow considering the pressing needs presented by societal challenges. A major reason for this pace is the vastness of possible compositions/processing conditions that must be navigated as part of the discovery/design/development process, which motivates the development of new approaches to accelerate the materials development pipeline. Our work focuses on the use of self-driving labs (SDL), or the combination of automation to perform experiments without human intervention and machine learning to select experiments that best progress toward a user-defined goal. In this talk, we overview our progress applying self-driving labs towards the development of materials that absorb mechanical energy. In particular, materials and architectures that are tough are ubiquitous as protective equipment and structural elements. If such materials could be engineered to absorb more energy per unit weight or volume while remaining easy to produce out of sustainable materials, they could find fruitful application in a number of fields. To explore this, we develop a SDL that combines additive manufacturing and mechanical testing and explore the toughness of 3D printed components. First, we use this platform to benchmark the acceleration provided through the use of SDL and find that this process allows us to identify high performing structures ~60 times faster than grid-based searching, which comprised the first experimental benchmarking of SDLs. Subsequently, we incorporated finite element analysis (FEA) into this SDL to search in a physics-aware fashion and observe further acceleration. Finally, we describe an extensive experimental campaign in which we find structures composed of biodegradable polymers that obtain superlative energy absorbing efficiency.