AI/ML Directed High-Performance Actuation and Sensing in 3D/4D Printed Polymer Materials

There is high interest in wearable and flexible electronic polymeric devices with the potential to expand the design space with both materials and design complexity. AI/ML-directed polymer synthesis and fabrication can lead the way toward more directed and optimized properties in record time. This includes autonomous workflows that can be deployed from the molecular to the macroscopic stage of optimization. With applications in biomechanics, health monitoring, sensing, and energy harvesting it will be even more challenging to develop better structure-composition-processing-property relationships (SCPP) for research and development. This talk will focus on our efforts to fabricate elastomeric, thermoset, and thermoplastic materials that are used as actuation sensors and piezoelectric devices viat 3D/4D Printing. The examples are as follows; 1) Silicone flexible materials based on optimized rheology with Graphene and CNT compositions, 2) Flexible thermo-plastics polyurethane materials with motion sensing and actuation applications, 3) Shape memory polybenzoxazine nanocomposites for actuation and 4) high-peformance piezoelectric devices based on PVDF. The use of AI/ML with digital twin development further accelerates the research protocols in record time. Future use of AI/ML-directed autonomous flow chemistry synthesis methods and additive manufacturing protocol optimization is further described.