2026
Advancing Universal Deep Learning for Electronic-Structure Hamiltonian Prediction of Materials
ICLR 2026poster
Deep learning methods for electronic-structure Hamiltonian prediction have offered significant computational efficiency advantages over traditional density functional theory (DFT), yet the diversity of atomic types, structural patterns, and the high-dimensional complexity of Hamiltonians pose substa…