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New neural operator accelerates density functional theory calculations

By PulseAugur Editorial · [2 sources] ·

Researchers have developed HamEvo, a novel neural operator designed to accelerate density functional theory (DFT) calculations by predicting Kohn-Sham Hamiltonians. This method achieves significant error reductions of 35-49% compared to existing baselines and can predict molecular orbital energies with high accuracy. HamEvo demonstrates impressive scalability, extending its capabilities to larger molecules with minimal fine-tuning and offering inference speeds up to 242 times faster than conventional DFT. AI

IMPACT Accelerates scientific discovery by enabling faster and more accurate molecular simulations.

RANK_REASON The cluster contains a research paper detailing a new method and its performance benchmarks.

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

COVERAGE [2]

  1. arXiv cs.AI TIER_1 English(EN) · Yunhong Lou, Xihang Yue, Xinran Wei, Tianqi Deng, Linchao Zhu ·

    A Fixed-Point Neural Operator for Size- and Functional-Transferable Hamiltonian Prediction

    arXiv:2606.14498v1 Announce Type: cross Abstract: Predicting the Kohn-Sham Hamiltonian with machine learning can accelerate density functional theory while retaining access to molecular orbitals, energy levels, and electronic-structure observables that energy-only surrogates cann…

  2. arXiv cs.AI TIER_1 English(EN) · Linchao Zhu ·

    A Fixed-Point Neural Operator for Size- and Functional-Transferable Hamiltonian Prediction

    Predicting the Kohn-Sham Hamiltonian with machine learning can accelerate density functional theory while retaining access to molecular orbitals, energy levels, and electronic-structure observables that energy-only surrogates cannot resolve. Yet element-wise agreement with the co…

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