Current Selections

Machine learning many-electron wave functions via backflow transformations

May 31st, 2020

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

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

3. Deep neural network solution of the electronic Schrödinger equation
Authors: J. Hermann, Z. Schätzle, and F. Noé

Recommended with a commentary by Markus Holzmann, Univ. Grenoble Alpes, CNRS, LPMMC
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This commentary may be cited as:
DOI: 10.36471/JCCM_May_2020_01

AI theorist? Not yet

May 31st, 2020

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
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This commentary may be cited as:
DOI: 10.36471/JCCM_May_2020_02

What drives superconductivity in twisted bilayer graphene?

May 31st, 2020

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

2. Decoupling superconductivity and correlated insulators in twisted bilayer graphene
Authors: Yu Saito, Jingyuan Ge, Kenji Watanabe, Takashi Taniguchi, and Andrea F. Young

3. Tuning electron correlation in magic-angle twisted bilayer graphene using Coulomb screening
Authors: Xiaoxue Liu, Zhi Wang, K. Watanabe, T. Taniguchi, Oskar Vafek, and J.I.A. Li

4. Nematicity and Competing Orders in Superconducting Magic-Angle Graphene
Authors: Yuan Cao, Daniel Rodan-Legrain, Jeong Min Park, Fanqi Noah Yuan, Kenji Watanabe, Takashi Taniguchi, Rafael M. Fernandes, Liang Fu, and Pablo Jarillo-Herrero

Recommended with a commentary by T. Senthil, Massachusetts Institute of Technology
|View Commentary (pdf)|

This commentary may be cited as:
DOI: 10.36471/JCCM_May_2020_03