IDMate：基于有限温度响应界的窗口分辨自洽场提案筛选

中文摘要 AI 辅助

我们引入IDMate，这是一种Kohn-Sham框架，用于筛选具有有限温度响应界和确定性参考图恢复的近似自洽场（SCF）态。电荷中性分解将电子条件化与混合器放大分离，同时保持不动点的秩一干预测试它们在线性动力学中的作用。对于固定哈密顿量、精确电子数和温度，Mermin自由能泛函的强凸性在选定的光谱窗口中产生后验密度矩阵界。在3586次应力试验中，1942个提案被接受，且没有一个超过窗口准则。在试验独立性假设下，单侧95%零事件上界为8.4×10^-4；将70个相关梯级视为独立单元时，每个梯级的上界为4.2×10^-2。在三个参考构型中，集成筛选规则替代了10次参考图评估，重现了参考评分轨迹，并满足所有终端比较准则。窗口与全空间偏差相差6至11个数量级；全空间距离对窗口决策进行分类，得到描述性合并AUC为0.694（行自助法95%置信区间[0.544, 0.831]）。预定义的跨谱系工作模型未达到其接受准则，且在预定义的电荷成本核算下，串行提案构建使生产实现的成本为其全参考反事实的7.87倍。IDMate建立了严格的精确迹窗口界、经过测试的接受或恢复规则，以及用于净加速的明确全空间和实现目标。

英文摘要

We formulate a finite-temperature residual test in IDMate that bounds window-resolved electronic errors without a spectral-gap assumption. For a fixed Hamiltonian and exact electron number, strong convexity of the matrix Fermi entropy bounds the density-matrix distance and free-energy error within a selected window. The bound remains finite at spectral crossings and extends to weighted k points with a shared chemical potential. We also derive a distance correction for particle-number mismatch. Across $3{,}586$ stress trials in $70$ seeded perturbation ladders, $1{,}942$ proposals satisfy the screen with no observed violation of the $0.05$ window-distance criterion plus its numerical allowance. This criterion differs from the uncorrected exact-trace bound, which six of ten historical in-loop candidates exceed at the numerical-error scale. An accept-or-recover loop replaces ten reference-map evaluations while meeting terminal comparison criteria in three configurations that include oracle-subspace controls. Additional candidates built only from preceding-iteration orbitals yield two acceptances and one abstention. The silicon candidate has a window distance of $1.891\times10^{-13}$ but a normalized real-space density error of $3.754\%$. Analytic examples separate errors from complement occupations and interblock coupling. Window-level accuracy therefore does not imply full-state accuracy; the screen tests compressed proposal quality, independently of nonlinear SCF convergence or net acceleration.

发表机构

• School of Materials Science and Engineering, Beihang University（北京航空航天大学材料科学与工程学院）
• National Key Laboratory of Artificial Intelligence for Material Science, Beihang University（北京航空航天大学人工智能材料科学全国重点实验室）
• Tianmushan Laboratory, Beihang University（北京航空航天大学天目山实验室）
• Center for Bioinspired Science and Technology, Hangzhou International Innovation Institute, Beihang University（北京航空航天大学杭州创新研究院仿生科学与技术中心）

机构由 AI 辅助整理，请以论文原文为准。

补充信息

备注

52 pages, 4 figures