Machine learning-driven prediction of strain engineering in graphene

Researchers develop revolutionary machine learning framework to rapidly predict atomistic structures and physical fields in 2D materials under continuous strain loading and defect engineering. New technique based on generative adversarial networks provides over 30x speedup compared to simulations and enables discovery of exotic quantum materials.