NONTECHNICAL SUMMARY This project develops key tools for designing new materials that can execute intelligent tasks – e.g. sensing input stimuli, reconfiguring their structure in response, and generating targeted mechanical behaviors such as global shape change. Such robot-material hybrids could quickly respond to protect humans and structures from damage, or even perform search and rescue tasks in dangerous environments. In order to transform on command, these materials must be active, harnessing energy. This project also focuses on disordered active materials -- where the constituent molecules or particles are jumbled up -- because, unlike crystals, these materials don’t need to change their volume or shape when transitioning from flowing to arrested states, simplifying their design and deployment. Currently, such materials cannot be self-assembled because scientists do not yet have a fundamental description of the design space -- i.e. how active forces, interactions between components, and local structure can be tuned to generate targeted large-scale responses. To address this gap, the PI will use computational and theoretical tools to predict how disordered materials deform and rearrange under active, self-generated patterns of forces. This will involve developing a new approach for finding defects – regions where the material is likely to flow -- in disordered active materials. The PI will then use this “defect field” as a key quantity in a set of large-scale d