Complete Neural Electronic Initialization Accelerates Materials DFT
We present the first complete machine learning method for accelerating plane-wave density functional theory (DFT) in materials under the projector augmented wave (PAW) formalism. We formalize seven criteria that a Complete Neural Electronic Initializer must satisfy for practical end-to-end PAW DFT acceleration. Applying these to prior work reveals two structure-dependent components, augmentation occupancies and spin initialization, whose absence prevents existing acceleration methods from providing complete reference-free initialization. We show that omitting these components can eliminate or reverse the acceleration obtained via models that only predict the smooth valence density. We sat...
| Authors | Felix Ærtebjerg, Jonas Elsborg, Arghya Bhowmik |
|---|---|
| Primary category | cond-mat.mtrl-sci |
| Published | 18 September 2026 |
| Last updated at source | 27 September 2026 |
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