Exploring New Frontiers in Inverse Materials Design with Graph Neural Networks and Large Language Models Recent Advancements in the NIST-JARVIS Infrastructure ChemNLP: A Natural Language Processing based Library for Materials Chemistry Text Data NIST-JARVIS infrastructure for Improved Materials Design Quantum Computation for Predicting Electron and Phonon Properties of Solids Materials Design in the Age of Deep Learning and Quantum Computation Smart Metrics for High Performance Material Design Database of Topological Materials and Spin-orbit Spillage Elastic properties of bulk and low-dimensional materials using Van der Waals density functional and machine-learning High-throughput discovery of low-dimensional and topologically non-trivial materials Accelerated Materials Discovery & Characterization with Classical, Quantum and Machine learning approaches Physics inspired artificial intelligence/machine learning Computational Database for 3D and 2D materials to accelerate discovery Computational Discovery of Two-Dimensional Materials, Evaluation of Force-Fields and Machine Learning 
