Atomic fingerprints for high throughput screening of 2D monolayers

In materials science, artificial intelligence is used to create machine learning models to predict material properties and discover different compounds. An atomic fingerprint is designed to reflect the structure and atomic features of 2D monolayer crystals. A neural network-based model is trained using fingerprints and the computational 2D materials database to predict 2D monolayers. The model predicted the formation energies of crystalline compounds, including some previously unexplored monolayers, which could be potential candidates for future technologies.