Material Search Engine

Material search engines leverage artificial intelligence to accelerate materials discovery and characterization by efficiently searching and analyzing vast datasets. Current research focuses on developing robust machine learning models, including graph neural networks and transformer-based architectures like BERT, to predict material properties from diverse data sources such as crystallographic information, spectroscopic measurements, and textual descriptions from scientific literature. These tools aim to overcome limitations of traditional methods by improving the accuracy and speed of material identification and property prediction, ultimately facilitating advancements in materials science and engineering.