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English(EN) A Fixed-Point Neural Operator for Size- and Functional-Transferable Hamiltonian Prediction

新的神经网络算子加速密度泛函理论计算

作者 PulseAugur 编辑部 · [2 个来源] ·

研究人员开发了HamEvo，这是一种新颖的神经网络算子，旨在通过预测Kohn-Sham哈密顿量来加速密度泛函理论（DFT）计算。与现有基线相比，该方法将误差降低了35-49%，并能高精度地预测分子轨道能量。HamEvo表现出令人印象深刻的可扩展性，只需少量微调即可将其能力扩展到更大的分子，并且推理速度比传统DFT快242倍。 AI

影响 通过实现更快、更准确的分子模拟来加速科学发现。

排序理由 该集群包含一篇详细介绍新方法及其性能基准的研究论文。

在 arXiv cs.AI 阅读 →

AI 生成摘要 · Google Gemini · 来自 2 个来源。 我们如何撰写摘要 →

新的神经网络算子加速密度泛函理论计算

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报道来源 [2]

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

    一种用于尺寸和功能可迁移哈密顿量预测的固定点神经网络算子

    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 ·

    一种用于尺寸和功能可迁移哈密顿量预测的固定点神经网络算子

    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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