Transformer-based operator learning framework for self-energy in strongly correlated systems (opens in new tab)

Author(s): Yuanran Zhu, Peter Rosenberg, Zhen Huang, Hardeep Bassi, Chao Yang, and Shiwei ZhangHere, the authors introduce a Transformer-based framework for learning the electronic self-energy in strongly correlated systems. A distinctive feature of the approach is the use of complementary, readily generated training datasets spanning weak-, intermediate-, and strong-coupling regimes. The dimension-agnostic Transformer architecture enables system-size generalization, allowing self-energy oper...