Case Studies of Multiphysics AI-Driven Inverse Design of Multifunctional Nano Products

This study presents two case studies validating a hybrid intelligence-guided Capillary Force Lithography (CFL) framework for fabricating multifunctional nanostructures. The framework integrates physics-based modeling with data-driven learning to control nanopillar height via spatially programmed UV exposure. A rubber-based soft robotic hand achieved a friction coefficient of 0.471 and bactericidal effectiveness of 0.916. A second design targeting structural coloration and grip reached a wavelength of 400 nm and a friction coefficient of 0.5576. Results confirm the framework's effectiveness in producing application-specific nanostructures, demonstrating its potential for scalable use in soft robotics and precision microengineering.