发表机构
Virginia Tech; Michigan State University; Georgia Institute of Technology; University of Texas at Dallas（弗吉尼亚理工大学; 密歇根州立大学; 佐治亚理工学院; 德克萨斯大学达拉斯分校）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
PyCC 是一个开源的量子化学 Python 参考包，支持耦合簇等方法，提供验证、教育和快速原型开发功能，并具备广泛的分子性质计算能力。
AI 中文摘要
PyCC 是一个基于 Python 的量子化学软件包，重点强调耦合簇及相关方法。经过过去五年的开发，PyCC 的目标是提供一个清晰的参考实现，作为生产代码的验证套件、新方法快速原型开发和测试的平台，以及该领域新入门者的教育资源。该软件包涵盖了闭壳层和开壳层单参考态，在空间轨道和自旋轨道公式中，针对耦合簇层级直至包含三激发的方法（如 CC3），以及 MP2 和 CI 方法。PyCC 提供了广泛的分子性质，例如解析的一阶和二阶能量导数，包括梯度、Hessian、极化率和原子极化张量，以及非能量导数性质，如速度规范原子极化张量和对角 Born-Oppenheimer 修正，外加动态线性响应函数和激发能。该软件包包含多项独特功能，例如实时 CC 传播（最高至 CC3）、用于在 MP2 和 CI 水平模拟振动圆二色光谱的原子轴向张量，以及包括微扰感知相关域的局域相关方法。代码设计围绕共享的 Wavefunction 基类和方法无关的导数层，使得新方法一旦提供其约化密度矩阵及其一阶响应，即可继承全部性质集。PyCC 完全开源，并在 BSD 3-clause 许可证下于 GitHub 上提供。
英文摘要
PyCC is a Python-based quantum chemistry package that emphasizes coupled cluster and related methods. Developed over the last five years, PyCC's purpose is to provide a clear reference implementation that will serve as a validation suite for production code, a platform for rapid prototyping and testing of new methods, and an educational resource for newcomers to the field. The package covers both closed- and open-shell single-determinant references in spatial- and spin-orbital formulations for the coupled cluster hierarchy through triples-including methods such as CC3, as well as for MP2 and CI methods. PyCC provides a broad range of molecular properties such as analytic first and second energy derivatives including gradients, Hessians, polarizabilities and atomic polar tensors, as well as non-energy-derivative properties, such as velocity-gauge atomic polar tensors and diagonal Born-Oppenheimer corrections, plus dynamic linear response functions, and excitation energies. The package includes a number of unique capabilities, such as real-time CC propagation (up through CC3), atomic axial tensors for simulating vibrational circular dichroism spectra at the MP2 and CI levels, and local correlation methods including perturbation-aware correlation domains. The code is designed around a shared Wavefunction base and a method-agnostic derivative layer that enables a new method to inherit the full property set once it supplies its reduced densities and their first-order responses. PyCC is fully open source and available on GitHub under the BSD 3-clause license.
