Inverse Design

Inverse design leverages computational methods, primarily machine learning, to efficiently discover optimal designs for materials and devices with desired properties, bypassing traditional trial-and-error approaches. Current research focuses on applying various deep learning architectures, including variational autoencoders, diffusion models, and neural operators, to diverse applications such as metamaterial design, polymer synthesis, and photonic device engineering. This accelerates the design process, enabling the creation of novel materials and devices with tailored functionalities for various technological applications, ranging from energy harvesting to advanced robotics.