Artificial Intelligence Powered Material Search Engine

TL;DR

This paper presents a material search engine leveraging AI techniques on X-ray diffraction data, achieving high accuracy in predicting materials and aiming to improve interpretability and efficiency over traditional methods.

Contribution

The work introduces an ensemble AI approach for material prediction from X-ray data and designs a GNN-based architecture for better interpretability and accuracy.

Findings

Ensemble method achieves ~100% accuracy.

Random Forest, Naive Bayes, and Neural Network algorithms show high individual accuracies.

Proposes a GNN architecture to enhance interpretability and prediction quality.

Abstract

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.