This project will harness the methods of artificial intelligence (AI) to design structures and tools for bionanotechnology. Bionanostructures are small, artificial self-assembled devices that can perform tasks at nanoscale. Like a robot in a factory combining materials to build a car, these nanostructures can put together materials such as proteins, gold nanoparticles, and DNA molecules with promising applications in diagnostics, therapeutics and material science. Currently, the design of bionanostructures is a tedious iterative process, often based on costly and time-consuming trial and error approaches. This project will develop novel techniques based on generative AI algorithms to automate design and characterization of these complex nanodevices built out of DNA. It will thus allow construction of more complex devices as well as scaling up and simplifying the process, thus enabling large-scale manufacturing of new types of bionanotechnology for a variety of applications. The overarching goal of this project is to harness generative AI methods for automated design of nucleic acid nanostructures and experimentally verify them by realizing nanoscale devices. To speed up the design process, this project will introduce the following applications of AI into the bionanotechnology field: 1) Develop new methods to speed-up computational characterization of nucleic acid nanostructures based on generative deep neural network architectures trained on data from coarse-grained mode