发表机构
Dalhousie University; University of Cambridge（达尔豪斯大学; 剑桥大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出最小经验局域杂化泛函LHnz，仅用三个参数，在GMTKN55基准上超越所有全局杂化泛函，有效减少离域误差。
AI 中文摘要
离域误差被认为是密度泛函理论（DFT）中最大的未解决挑战。减少该误差最有前景的途径之一是发展局域杂化泛函，其中精确交换混合的比例依赖于位置。然而，现有的能够实现良好热化学精度的局域杂化泛函往往高度经验化，并且倾向于具有复杂的泛函形式，涉及范围分离、校准函数、幂级数展开甚至神经网络的某种组合。在本工作中，我们探索了避免此类复杂性的最小经验局域杂化泛函形式所能达到的极限。我们提出了“LHnz”泛函，它使用无色散交换和依赖于相关长度及有效交换-相关空穴归一化的局域混合比例。仅凭三个经验参数，LHnz在GMTKN55分子热化学基准测试中表现优于所有现有的全局杂化泛函，且没有大的离群值。
英文摘要
Delocalisation error has been argued to be the greatest outstanding challenge in density-functional theory (DFT). One of the most promising routes to minimise this error is development of local hybrid functionals, in which the fraction of exact-exchange mixing is position dependent. However, existing local hybrids capable of good thermochemical accuracy are often highly empirical, and tend to have complicated functional forms that involve some combination of range separation, calibration functions, power-series expansions, or even neural networks. In this work, we explore the limits of what can be achieved with a minimally empirical local hybrid functional form that avoids such complexities. The ``LHnz'' functional is proposed, which uses dispersionless exchange and a local mixing fraction dependent on the correlation length and effective exchange--correlation hole normalisations. With only three empirical parameters, LHnz is shown to outperform all existing global hybrid functionals for the GMTKN55 molecular-thermochemistry benchmark with no large outliers.
Comments11 pages, 3 figures, 4 tables
