Predicting material properties is a laborious and expensive process, especially when it comes to synthesizing and validating novel materials in the physical world. However, there is a promising solution offered by Large Language Models [LLM] & other Neural Networking [NN] techniques, which significantly reduce the time and cost involved in materials development. Through the utilization of LLM & other NN algorithms, inputs such as crystal structure, chemical composition, and electronic band structures can be used to train models that predict computationally challenging or costly properties. Moreover, Machine Learning [ML] empowers scientists to generate innovative ideas for intricate tasks like solid-state synthesis and crystal structure determination, unveiling previously inaccessible physical insights. This efficient approach allows scientists to explore extensive chemical and configurational search spaces in pursuit of stable, affordable, and high-performance materials. Our engine, powered by LLM and High Performance Computing [HPC], facilitates atomistic as well as molecular generative materials design. With the support of Azure computing resources, the engine seamlessly integrates various stages of the materials design process, including hypothesis generation, calculation setup, experiment execution, informatics analysis, and database development. The need for human intervention is minimized, enabling a more efficient and streamlined workflow.