发表机构
University of California at Berkeley; Lawrence Berkeley National Laboratory; Tsinghua University（加州大学伯克利分校; 劳伦斯伯克利国家实验室; 清华大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出DeepH-GW，一个基于深度学习的框架，直接从原子结构预测GW准粒子哈密顿量，结合平面波精度与线性标度推理，实现meV级误差的能带预测及跨尺度迁移，支持大规模多体激发态模拟。
AI 中文摘要
准确的准粒子电子结构是理解材料激发态性质以及解释光电、量子与输运现象的基础。然而，第一性原理GW计算对于大型或构型复杂的系统而言，计算强度仍然很高。在此，我们介绍了DeepH-GW，一个深度学习框架，它直接从原子结构预测有效的GW准粒子哈密顿量。基于DeepH的局域等变消息传递架构，DeepH-GW通过实空间哈密顿量重构接口，在高保真平面波GW计算上进行训练。该方法将平面波方法的系统精度和广泛化学适用性与线性标度神经网络推理相结合。在所考察的系统中，DeepH-GW重现了准粒子能带结构，误差在几个meV量级。此外，我们表明，尽管多体相互作用具有内在的非局域性，DeepH-GW表现出从相对较小的训练结构到显著更大的超胞的强跨尺度迁移能力。我们证明，该框架可以通过对含热位移的超胞进行预测，准确捕获GW级别的电子-声子带隙重整化，展示了该方法的威力。因此，DeepH-GW为大规模多体模拟和激发态电子结构基础模型提供了一条实用途径。
英文摘要
Accurate quasiparticle electronic structures are the foundation for understanding excited-state properties of materials and explaining optoelectronic, quantum, and transport phenomena. First-principles GW calculations nevertheless remain computationally intensive for large or configurationally complex systems. Here we introduce DeepH-GW, a deep-learning framework that predicts an effective GW quasiparticle Hamiltonian directly from atomic structure. Building on the local, equivariant message-passing architecture of DeepH, DeepH-GW is trained on high-fidelity plane-wave GW calculations through a real-space Hamiltonian-reconstruction interface. This approach combines the systematic accuracy and broad chemical applicability of plane-wave methods with linear-scaling neural-network inference. Across the systems examined, DeepH-GW reproduces quasiparticle band structures with errors on the order of a few meV. Moreover, we show that, despite the intrinsic nonlocality of many-body interactions, DeepH-GW exhibits strong cross-scale transferability from relatively small training structures to substantially larger supercells. We demonstrate that the framework can accurately capture GW-level electron-phonon band-gap renormalization through predictions in supercells with thermal displacements, illustrating the power of the approach. DeepH-GW therefore provides a practical route toward large-scale many-body simulations and foundation models for excited-state electronic structure.
