Engineering and Technology

Crystal Twins: Self-Supervised Learning for Crystalline Material Property Prediction

R. Magar, Y. Wang, et al.

Discover the groundbreaking Crystal Twins (CT) method developed by Rishikesh Magar, Yuyang Wang, and Amir Barati Farimani, which harnesses self-supervised learning to effectively predict material properties using large unlabeled datasets. This innovative approach, employing twin Graph Neural Networks, has shown remarkable improvements in GNN performance across 14 benchmarks.