Abstract: The search for new materials with tailored properties has long been a challenge due to the high computational and experimental costs involved. Inverse design approaches offer a promising alternative by enabling the creation of property-to-structure models, as opposed to the traditional structure-to-property model development. These approaches can overcome the limitations of conventional, funnel-like materials screening methods and accelerate the computational discovery of next-generation materials. In this talk, we will explore the application of graph neural networks (such as ALIGNN) and recent advances in large language models (like AtomGPT and DiffractGPT) for both forward and inverse materials design, with a focus on semiconductors and superconductors. We will also discuss the strengths and limitations of these methods. Finally, materials predicted by inverse design models will be validated using density functional theory prior to experimental synthesis and characterization. The above projects are part of the NIST-JARVIS infrastructure available at https://jarvis.nist.gov/. Brief Bio: Dr. Kamal Choudhary is a Staff Scientist in the Material Measurement Laboratory at the National Institute of Standards and Technology (NIST) in Maryland, USA. He earned his PhD in Materials Science and Engineering from the University of Florida in 2015 before joining NIST. His research focuses on atomistic materials design, employing classical, quantum, and machine learning methods. Notably, Dr. Choudhary developed the JARVIS database and tools (https://jarvis.nist.gov/), which are widely used by thousands of researchers globally. He serves as an associate editor for Nature NPJ Computational Materials. With over 80 published research articles in prestigious journals, he is an active member of the TMS, APS, and MRS societies. Additionally, Dr. Choudhary is an adjunct professor at Johns Hopkins University.