DFT2kp：从从头算数据生成有效的kp模型
DFT2kp: effective kp models from ab-initio data
AI总结：
该工作开发了开源代码DFT2kp，直接从从头算波函数计算k·p有效哈密顿量的Kane和Luttinger参数，并通过对称性分析优化基组，适用于石墨烯等材料。
摘要：
$\mathbf{k}\cdot\mathbf{p}$ 方法与群论相结合，是获得晶体材料低能有效哈密顿量的高效途径。尽管哈密顿量系数被写为广义动量算符 $\mathbf{\pi}=\mathbf{p}+\mathbf{p}_{\rm SOC}$（包括自旋-轨道耦合修正）的矩阵元，但其数值必须从外部来源确定，例如实验或从头算方法。在此，我们开发了一个代码，直接从 Quantum ESPRESSO 提供的从头算波函数中显式计算 $\mathbf{k}\cdot\mathbf{p}$ 有效哈密顿量的 Kane（晶体动量线性）和 Luttinger（晶体动量二次）参数。此外，该代码分析波函数的对称性变换以优化最终的哈密顿量。这是代码中的一个可选步骤，其中数值上寻找酉变换 $U$，将基组旋转到用户告知的最优对称适应表示。在整篇论文中，我们详细介绍了方法论，并通过将其应用于一系列相关材料来展示代码的能力。特别地，我们展示了一个“动手”示例，说明如何对石墨烯（有和无自旋-轨道耦合）运行该代码。该代码是开源的，可在 https://gitlab.com/dft2kp/dft2kp 获取。
英文摘要：
The $\mathbf{k}\cdot\mathbf{p}$ method, combined with group theory, is an efficient approach to obtain the low energy effective Hamiltonians of crystalline materials. Although the Hamiltonian coefficients are written as matrix elements of the generalized momentum operator $\mathbfπ=\mathbf{p}+\mathbf{p}_{\rm SOC}$ (including spin-orbit coupling corrections), their numerical values must be determined from outside sources, such as experiments or ab initio methods. Here, we develop a code to explicitly calculate the Kane (linear in crystal momentum) and Luttinger (quadratic in crystal momentum) parameters of $\mathbf{k}\cdot\mathbf{p}$ effective Hamiltonians directly from ab initio wavefunctions provided by Quantum ESPRESSO. Additionally, the code analyzes the symmetry transformations of the wavefunctions to optimize the final Hamiltonian. This is an optional step in the code, where it numerically finds the unitary transformation $U$ that rotates the basis towards an optimal symmetry-adapted representation informed by the user. Throughout the paper, we present the methodology in detail and illustrate the capabilities of the code applying it to a selection of relevant materials. Particularly, we show a "hands-on" example of how to run the code for graphene (with and without spin-orbit coupling). The code is open source and available at https://gitlab.com/dft2kp/dft2kp.
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。
