NSF Award
2324168
Non-technical Description: In our materials-limited world, developing new materials is the foundation of technological progress. The current approach to materials design relies on trial-and-error exploration of chemicals and reactions to achieve a target function where structure–property relationships are identified only in retrospect is woefully inefficient. The proposed research will disrupt this status quo by developing a foundation for conceptually new processes, where discovered molecular codes will instruct the continuous flow synthesis of materials with encoded combinations of physical properties. Implementing digital codes in soft matter design will drive new synthetic pathways, stimulate new physical models, and catalyze fundamental shifts in various technologies, including personalized medicine, soft robotics, and sustainable materials fabrication. Collaboration with the Ackland Museum of Art will educate students on the connection between Materials Science concepts and Art in different cultures across human history. Building on this knowledge, participating students will create artwork merging Materials Science and Art. In addition, the DMREF team will develop the CREATE (Consider, Read, Elucidate the hypothesis, Analyze and interpret data, and Think of the next Experiment) method to give undergraduate students creative ownership of the research design and execution, as well as build an inclusive environment. This DMREF team also proposes to actively recruit and support women and underrepresented groups, including but not limited to people with disabilities and low-income students.<br/><br/>Technical Description: The project will pursue the development of programmable materials by integrating molecular design codes, automated synthesis, and forensic structural analysis. Structurally encoding time-dependent properties and retroactively deciphering network structure from the temporal response represents one of the intellectual challenges of the project. State-of-the-art network theory and computer simulations will define a set of codes that encompass the composition, architecture, and temporal evolution of soft materials. These quantitative descriptors will be implemented into Artificial Intelligence-guided materials design and synthesis platforms for polymer networks, wherein novel in-line characterization techniques and multiple feedback loops enable iterative multi-objective property optimization. The design-by-architecture approach will launch the programmable synthesis of polymer networks (elastomers, gels, and thermoplastics) with tailored combinations of properties encoded in molecular structure. In line with the Materials Genome Initiative mission, this effort will create an Artificial Intelligence-driven platform with multiple embedded feedback loops and an open-source Big Data search engine that enable materials design, synthesis, and forensics. This project is co-funded by the NSF Mathematical and Physical Sciences (MPS) Divisions of Materials Research (DMR) and Chemistry (CHE) through the Designing Materials to Revolutionize and Engineer our Future (DMREF) program, the DMR Polymers (POL) Program, and the MPS Office of Multidisciplinary Activities (MPS/OMA).<br/><br/>This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
