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arXiv Machine Learning By Felix {\AE}rtebjerg, Jonas Elsborg, Arghya Bhowmik

Complete Neural Electronic Initialization Accelerates Materials DFT

Read the original on arXiv Machine Learning →

The paper introduces AugNet, a complete neural electronic initializer that satisfies seven criteria for accelerating plane‑wave density functional theory (DFT) under the projector augmented wave (PAW) formalism. AugNet provides general equivariant predictions for PAW augmentation occupancies and spin densities, filling gaps left by previous models that omitted structure‑dependent components. When combined with existing valence density models, the approach achieves up to ~25% reduction in end‑to‑end DFT wall time on unseen structures while maintaining converged energies.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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