Learning the Kohn-Sham map with neural operators for quasi-linear scaling density functional theory
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, we identify the Kohn--Sham map as the right learning target for orbital-free DFT. It maps a Kohn...
| Authors | Danish Khan, Maurice D. Hanisch, Nikolai Argatoff et al. |
|---|---|
| Primary category | physics.chem-ph |
| Published | 24 August 2026 |
| Last updated at source | 24 August 2026 |
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