Machine learning many-electron wave functions via backflow transformations

1. Backflow Transformations via Neural Networks for Quantum Many-Body Wave-Functions Authors: D. Luo and B. K. Clark Phys. Rev. Lett. 122, 226401 (2019); DOI:10.1103/PhysRevLett.122.226401 arXiv:1807.10770 2. Ab-Initio Solution of the Many-Electron Schrödinger Equation with Deep Neural Networks Authors: D. Pfau, J. S. Spencer, A. G. de G. Matthews, and W. M. C. Foulkes arXiv:1909.02487 3. […]

AI theorist? Not yet

AI Feynman: A physics-inspired method for symbolic regression Authors: Silviu-Marian Udrescu and Max Tegmark Sci. Adv. 6 : eaay2631, 2020; DOI: 10.1126/sciadv.aay2631 Recommended with a commentary by Ilya Nemenman, Emory University |View Commentary (pdf)| This commentary may be cited as: DOI: 10.36471/JCCM_May_2020_02 https://doi.org/10.36471/JCCM_May_2020_02

What drives superconductivity in twisted bilayer graphene?

1. The interplay of insulating and superconducting orders in magic-angle graphene bilayers Authors: Petr Stepanov, Ipsita Das, Xiaobo Lu, Ali Fahimniya, Kenji Watanabe, Takashi Taniguchi, Frank H. L. Koppens, Johannes Lischner, Leonid Levitov, and Dmitri K. Efetov arXiv:1911.09198 2. Decoupling superconductivity and correlated insulators in twisted bilayer graphene Authors: Yu Saito, Jingyuan Ge, Kenji Watanabe, […]