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Language-inspired machine learning approach for solving strongly correlated problems with dynamical mean-field theory

Hovan Lee1,*, Zelong Zhao2, George H. Booth2, Weifeng Ge3,†, and Cedric Weber2

  • *Contact author: hovan.lee@rhul.ac.uk
  • †Contact author: wfge@fudan.edu.cn

Linear Foundation Model for Quantum Embedding: Data-Driven Compression of the Ghost Gutzwiller Variational Space

Samuele Giuli, Hasanat Hasan, Benedikt Kloss, Marius S. Frank, Tsung-Han Lee, Olivier Gingras, Yong-Xin Yao, and Nicola Lanatà

PRX Intelligence 1 013001 (2026)

Neural networks as low-cost surrogates for impurity solvers in quantum embedding methods

Rohan Nain, Philip M. Dee, Kipton Barros, Steven Johnston, and Thomas A. Maier

Phys. Rev. B 114 225114 (2026)

Deep learning-based prediction of self-energies from ab initio dynamical mean-field theory for real materials with minimal data sets

Pragati Mitra and Hrishit Banerjee

npj Comput Mater 12 339 (2026)

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