Machine learning many-electron wave functions via backflow transformations

Posted in Journal Articles on May 31, 2020 at 2:13 pm by JCCMP

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
|View Commentary (pdf)|

This commentary may be cited as:
DOI: 10.36471/JCCM_May_2020_01

Guidelines for Comments by Members:
Members are invited to comment on the chosen papers and refer only to papers intimately related to the papers selected. Other comments and suggestions can be transmitted to the organizers through the 'Guest book' link. These comments will be put on the web-site and the archives so that they can be read by other members. Just as in the Guidelines to the Corresponding Members, we suggest that the comments be confined to substantive issues of science and in order to illuminate the subject matter. A collegial and respectful tone is suggested. Issues of priority should not be raised in the comments.

Leave Your Comment Below

Your email address will not be published. Required fields are marked *

This site uses Akismet to reduce spam. Learn how your comment data is processed.