Advances at the Intersection of Density Functional Theory and Artificial Intelligence
This Collection offers an early glimpse into how artificial intelligence and machine learning can further unlock the potential of density functional theory, a highly successful computational method with broad applications.
Guy Hay Kalifa, Dor Kopelevitch, Amir Stern, and Yoav Sagi
Phys. Rev. A 114, L030801 (2026)
Hubert J. Jóźwiak, Ian Stevenson, Sebastian Will, and Tijs Karman
Phys. Rev. A 114, 033315 (2026)
Kristian Seegert, Yi Yu, Mikkel Heuck, and Jesper Mørk
Phys. Rev. A 114, 033507 (2026)
Martti Hanhisalo, Atri Halder, Tero Setälä, and Andreas Norrman
Phys. Rev. A 114, L031701 (2026)
Davide Rinaldi, Radim Filip, Dario Gerace, and Giacomo Guarnieri
Phys. Rev. A 114, L030601 (2026)
Ryotaro Niwa, Zane Marius Rossi, Philip Taranto, and Mio Murao
Phys. Rev. A 114, L030401 (2026)
Hiroki Nakabayashi, Hayato Kinkawa, Takano Taira, and Naomichi Hatano
Phys. Rev. A 114, L020202 (2026)
Xiao-Lin Li, Ming Gong, Yu-Hao Wang, and Li-Chen Zhao
Phys. Rev. A 114, L021303 (2026)
Marcella L. Xavier, Felipe A. Pinheiro, and Romain Bachelard
Phys. Rev. A 114, L021101 (2026)
Matthew Duschenes, Roger G. Melko, Juan Carrasquilla, and Raymond Laflamme
Phys. Rev. A 114, 022434 (2026)
Yaroslav V. Kartashov, Vladimir V. Konotop, and Dmitry A. Zezyulin
Phys. Rev. A 114, L021302 (2026)
50 Years of Physical Review A: The Legacy of Three Classics
Physicists working in optics, atomic and molecular physics, and quantum information reflect on landmark papers and how they influence research today.
Special Feature in Physics





























.png)


.png)










