SOURCE-LINKED INTELLIGENCE
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 \textit{Complete Neural Electronic Initializer} must satisfy for practical end-to-end PAW DFT acceleration. Applying these criteria to prior work reveals two missing structure-dependent components, augmentation occupancies and spin initialization, that prevent existing methods from providing complete reference-free initialization. Controlled ablations show that omitting these compone
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- arXiv · AI, language, vision and robotics · 2026-09-18T13:30:29.000Z
First collected: 2026-09-23T13:51:27.104Z. This is not the publication date.