During this presentation, we’ll discuss 1) large-scale materials database development such as JARVIS-DFT, which acts as a precursor for data-informatics applications, 2) graph neural networks for property predictions and force-field development, 2) computer vision tasks for atomistic image analysis, 3) large language models for materials science literature tasks, and 4) development of a universal platform for benchmarking materials design methods. First, we’ll learn about Atomistic Line Graph Neural Network (ALIGNN) that performs message passing on both the bond-distances as well as bond-angles. Next, we’ll discuss the AtomVision package which can be used to generate scanning tunneling microscope (STM) and scanning transmission electron microscope (STEM) datasets. Then, we will apply deep learning frameworks for image classification and object detection tasks with high accuracy. Furthermore, we will discuss about ChemNLP library for several language modeling tasks. Finally, we will focus on the JARVIS-Leaderboard benchmarking platform to systematically improve the above tasks. All of the above projects are part of the NIST-JARVIS infrastructure (https://jarvis.nist.gov/).