Hongjun Xiang

First-principles quantum transport simulations no longer stall at a few hundred atoms: predicting Hamiltonians with an $E(3)$-equivariant GNN unlocks linear scaling to 10,000+ atom devices with a $1,000\times$ speedup over conventional DFT-NEGF.

Adapting memory length during training stages can accelerate convergence to target loss, revealing a new dimension in optimizer design.