Recent research in materials science opens exciting perspectives to design novel quantum materials and devices, but it calls for quantitative predictions of properties which are not accessible in standard first principles packages. PAOFLOW, is a software tool that constructs tight-binding Hamiltonians from self-consistent electronic wavefunctions by projecting onto a set of atomic orbitals. The electronic structure provides numerous materials properties that otherwise would have to be calculated via phenomenological models. In this paper, we describe recent re-design of the code as well as the new features and improvements in performance. In particular, we have implemented symmetry operations for unfolding equivalent k-points, which drastically reduces the runtime requirements of first principles calculations, and we have provided internal routines of projections onto atomic orbitals enabling generation of real space atomic orbitals. Moreover, we have included models for non-constant relaxation time in electronic transport calculations, doubling the real space dimensions of the Hamiltonian as well as the construction of Hamiltonians directly from analytical models. Importantly, PAOFLOW has been now converted into a Python package, and is streamlined for use directly within other Python codes. The new object oriented design treats PAOFLOW’s computational routines as class methods, providing an API for explicit control of each calculation.

材料科学的最新研究为设计新型量子材料和器件开辟了令人兴奋的前景，但它需要对标准第一性原理包中无法获得的特性进行定量预测。PAOFLOW是一种软件工具，它通过投影到一组原子轨道上，从自洽电子波函数构建紧束缚哈密顿量。电子结构提供了许多材料特性，否则必须通过现象学模型进行计算。在本文中，我们描述了最近对代码的重新设计以及新功能和性能改进。特别是，我们已经实现了展开等效k 的对称操作-points，这大大降低了第一性原理计算的运行时间要求，并且我们提供了投影到原子轨道的内部例程，从而能够生成真实的空间原子轨道。此外，我们在电子输运计算中包含了非恒定弛豫时间模型，将哈密顿量的实际空间维度加倍，以及直接从分析模型构建哈密顿量。重要的是，PAOFLOW现在已转换为 Python 包，并且经过精简，可直接在其他 Python 代码中使用。新的面向对象设计将PAOFLOW的计算例程视为类方法，为每个计算的显式控制提供 API。