AI 中文总结
该研究用符号回归构建了可近似GW自能的解析函数，实现了复杂材料的低成本GW计算，验证了其在多类材料上的精度，确立了符号回归构建多体电子结构模型的可行性。
AI 中文摘要
预测材料中的准粒子能量需要对电子自能进行代价高昂的数值计算，这将计算限制在具有小晶胞的有序系统中。本文中，我们使用符号回归表明，GW自能可以通过基于物理动机的Kohn-Sham描述符的紧凑解析函数进行精确近似。这些表达式可从有序相中的单次GW计算中学习得到，并且在由量子和热涨落、弹性变形以及非晶无序引起的对称性破缺下仍保持精确。该进展使得可以以与半局部密度泛函理论相当的计算成本对具有数千个原子的复杂材料进行常规GW计算。我们在共价半导体、离子绝缘体和二维材料上验证了该方法的精度，结果表明符号回归是构建可预测、可解释且可迁移的多体电子结构模型的可行途径。
英文摘要
Predicting quasiparticle energies in materials requires expensive numerical evaluations of the electron self-energy. This limits calculations to ordered systems with small unit cells. Here, using symbolic regression, we show that the GW self-energy can be accurately approximated with compact analytical functions of physically motivated Kohn-Sham descriptors. These expressions can be learned from a single GW calculation in the ordered phase and remain accurate under symmetry breaking induced by quantum and thermal fluctuations, elastic deformations, and amorphous disorder. This development enables routine GW calculations of complex materials with thousands of atoms at a computational cost comparable to semi-local density functional theory. We demonstrate the accuracy of this approach for covalent semiconductors, ionic insulators, and two-dimensional materials. These results establish symbolic regression as a viable route to predictive, interpretable, and transferable many-body electronic structure models.
CommentsMain manuscript and supplemental material
Journal refPhys. Rev. B 114, 105112 (2026)
