# When do machine-learned exchange-correlation improvements inherit into density-functional tight binding?

arXiv: https://arxiv.org/abs/2608.14875 · HTML: https://mlsanity.com/paper/2608.14875/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 1 of its 58 references that are in the map.
- First posted on arXiv 14 Aug 2026.

## Builds on (most cited first, 1 of 1)

- [Accurate and scalable exchange-correlation with deep learning](https://mlsanity.com/paper/2506.14665/) (2025)

## Abstract (arXiv)

Machine-learned exchange-correlation functionals correct band gaps at near-semilocal cost, while density-functional tight binding reaches the 103-106-atom regime; combining them assumes that a better parent yields a better parameterization, but we show it does not. Current-generation functionals are orbital-dependent generalized Kohn-Sham operators, whereas the parameterization channel is built on a multiplicative potential, preventing exact representation. Using the transfer ratio, the surviving fraction of a parent-level change, we find anti-transfer: coherently negative ratios across four covalent semiconductors move the gap in the wrong direction, consistent with a molecular proxy and an r2SCAN control. The minimal-basis overgap is dominated by the on-site convention rather than basis incompleteness; correcting the on-site block removes most of it, while one d-polarization shell closes a further 16-40%, depending on the placement of the empty d level, which no free-atom eigenvalue uniquely fixes. Occupied-manifold enhancements, ionic and closed-shell repulsive potentials, and rocksalt-oxide gaps inherit, whereas elemental and III-V covalent networks inherit neither gaps nor repulsive potentials and oxide networks inherit only the latter. We screen 23 elements and release the parameter sets, showing that the transfer ratio provides a cheap pre-test before any parameterization campaign.