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Machine learning many-electron wave functions via backflow transformations
2020
Journal Club for Condensed Matter Physics
The goal of determining the electronic structure of molecules and materials by solving the many-body Schrödinger equation has challenged theoretical physics and chemistry over the last century and driven the development of powerful approximations and computational methods. In the three papers above [1, 2, 3], the authors show how deep learning architectures can systematically improve many-body wave functions for Quantum Monte Carlo calculations, and benchmark their accuracy on the Hubbard model
doi:10.36471/jccm_may_2020_01
fatcat:wrfr6xvihvhrzcdemurzxhcrly