  Many data-driven applications in material science have been made possible
because of recent breakthroughs in artificial intelligence(AI). The use of AI
in material engineering is becoming more viable as the number of material data
such as X-Ray diffraction, various spectroscopy, and microscope data grows. In
this work, we have reported a material search engine that uses the interatomic
space (d value) from X-ray diffraction to provide material information. We have
investigated various techniques for predicting prospective material using X-ray
diffraction data. We used the Random Forest, Naive Bayes (Gaussian), and Neural
Network algorithms to achieve this. These algorithms have an average accuracy
of 88.50\%, 100.0\%, and 88.89\%, respectively. Finally, we combined all these
techniques into an ensemble approach to make the prediction more generic. This
ensemble method has a ~100\% accuracy rate. Furthermore, we are designing a
graph neural network (GNN)-based architecture to improve interpretability and
accuracy. Thus, we want to solve the computational and time complexity of
traditional dictionary-based and metadata-based material search engines and to
provide a more generic prediction.
