All-electron machine-learned orbital-free density functional for periodic CHNO systems
We introduce STRUCTURES-P, a machine-learned all-electron orbital-free density functional for periodic CHNO systems. Electrostatics are evaluated analytically in an atom-centered Gaussian basis, while an equivariant network learns the kinetic and exchange-correlation energy from energies and gradients of non-ground-state densities. Minimizing the functional from a superposition of atomic densities recovers the Kohn-Sham ground state: on held-out crystals, the density error falls from 6.7% to 0.4% of the electron count and the energy error reaches 0.25 mHa per atom, half that of a same-architecture interatomic potential. A single functional can be trained for both isolated molecules and pe...
| Authors | Marc K. Ickler, Andreas Hermann, Fred A. Hamprecht |
|---|---|
| Primary category | physics.chem-ph |
| Published | 13 October 2026 |
| Last updated at source | 13 October 2026 |
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