Transferable SCF-Acceleration through Solver-Aligned Initialization Learning
The cost of Kohn-Sham density functional theory (KS-DFT) calculations scales with the number of solver iterations, which depends on the quality of the initial guess. Machine learning methods that predict initial guesses from molecular geometry can reduce this cost, but matrix-prediction models fail when extrapolating to larger molecules, degrading rather than accelerating convergence [Liu et al., 2025]. We show that this failure is a supervision problem, not an extrapolation problem: models trained on ground-state targets fit those targets well out of distribution, yet produce initial guesses that slow convergence. Solver-Aligned Initialization Learning (SAIL) resolves this for both Hamil...
| Authors | Eike S. Eberhard, Viktor Kotsev, Timm Güthle et al. |
|---|---|
| Primary category | cs.LG |
| Published | 23 April 2026 |
| Last updated at source | 8 May 2026 |
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