SOURCE-LINKED INTELLIGENCE

Learning the Kohn-Sham map with neural operators for quasi-linear scaling density functional theory

arXiv · AI, language, vision and robotics · article · Aug 24, 2026 · UTC

Kohn--Sham density functional theory (DFT) underpins electronic-structure simulations, but repeated orbital diagonalizations lead to cubic scaling, restricting quantum calculations to modest scales only. Eliminating these auxiliary orbitals while retaining Kohn--Sham accuracy is the central goal of orbital-free DFT, but both analytical and machine-learning methods have so far fallen short. Prior learning approaches either try to learn the variational kinetic-energy functionals, which are ill-conditioned, or directly predict the ground state, which extrapolate poorly to larger systems. Instead,

Read original source ↗ Open in workspace

recordType

paper

region

Global