Abstract: Advancements in AI/ML algorithms and high performance computing have empowered the use of data driven approaches for studying a variety of rich and intriguing phenomena in nanoscience, as well as for accelerating the design and discovery of materials. The flexibility of AI/ML data models coupled with the predictive power of physics based simulations pave the way for closing the loop on autonomous experimentation and possibly nanoscience discoveries that can
help close the loop on circular economy. This talk summarizes my research experience on materials modeling, imaging, and AI/ML method development, which is focused on addressing open issues and challenges in big data, autonomous experimentation, and inverse design of materials for a circular economy. Finally, my talk will touch upon the future of scientific software development, user friendly AI/ML tools, data management and their potential impact on the CNM user program.