PyGSC：一种用于通过缓解密度泛函近似中的局域化误差来校正Kohn-Sham轨道能的Python工具
PyGSC: A Python tool for correcting Kohn-Sham orbital energies by mitigating the delocalization error of density functional approximations
AI总结：
本文提出了一种改进的准粒子能密度泛函理论方法，开发了PyGSC工具，通过优化交换-相关势的微扰表达式，提升了分子系统的描述一致性，并在主族原子和G2/97分子上验证了其在预测电子亲和能和电离势方面的准确性。
摘要：
密度泛函近似（DFAs）存在局域化误差，限制了其预测电子亲和能（EAs）、电离势（IPs）和准粒子能的准确性。本文提出了一种改进的准粒子能密度泛函理论（QE-DFT）方法，通过改进交换-相关势的微扰表达式，从而更一致地描述分子系统。我们进一步开发了一个基于PySCF库的开源Python程序PyGSC，实现了改进的QE-DFT框架。主族原子和G2/97分子的基准测试表明，改进的QE-DFT方法优于原始DFAs，第三阶修正的均方误差在EAs和IPs预测中低于0.3 eV。应用于DNA/RNA碱基的偶极束缚态进一步验证了QE-DFT方法优于原始DFAs，为预测大分子系统的电子性质提供了一种高效且准确的方法。
英文摘要：
Density functional approximations (DFAs) suffer from delocalization error, which limits their accuracy in predicting electron affinities (EAs), ionization potentials (IPs), and quasiparticle energies. In this work, we present a theoretical refinement of the quasiparticle energies from density functional theory (QE-DFT) method by improving the perturbative expression for the exchange-correlation potential, leading to a more consistent description of molecular systems. We further develop an open-source Python program, PyGSC, built upon the PySCF library, which implements the modified QE-DFT framework. Benchmark tests on main-group atoms and G2/97 molecules demonstrate that the modified QE-DFT method outperforms the original DFAs, with third-order corrections achieving mean absolute deviations below 0.3 eV for EA and IP predictions. Application to dipole-bound states of DNA/RNA nucleobases further validates the superiority of the QE-DFT approach over original DFAs, offering an efficient and accurate approach for predicting electronic properties in large molecular systems.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
