标题： 基于从头计算的紧束缚模型的优化 显示英文标题

标题： Optimization of Ab-Initio Based Tight-Binding Models

摘要： 固体的电子结构可以通过像密度泛函理论这样的标准方法进行常规计算。 然而，在界面、晶界或接触几何等复杂情况下，需要借助更简化的电子结构模型。 紧束缚模型使用一组减少的轨道，并旨在通过短程跃迁过程来近似电子结构。 例如，最大局部化Wannier函数常用于此目的。 然而，它们的准确性受到需要解耦电子能带的限制。 在这里，我们开发并研究了一种不同的方法来获得紧束缚模型，该方法受到机器学习技术的启发。 模型参数被优化，以尽可能准确地再现从头算能带结构数据，同时使用尽可能少的模型参数。 该方法被证明可以产生比最大局部化Wannier函数范围更小、轨道更少但精度相同甚至更高的模型。 我们认为，这种方法对于自动化构建紧束缚模型尤其有用，特别是在大规模材料计算中。 显示英文摘要

摘要： The electronic structure of solids can routinely be calculated by standard methods like density functional theory. However, in complicated situations like interfaces, grain boundaries or contact geometries one needs to resort to more simplified models of the electronic structure. Tight-binding models are using a reduced set of orbitals and aim to approximate the electronic structure by short range hopping processes. For example, maximally localized Wannier functions are often used for that purpose. However, their accuracy is limited by the need to disentangle the electronic bands. Here, we develop and investigate a different procedure to obtain tight-binding models inspired by machine-learning techniques. The model parameters are optimized in such a way as to reproduce ab-initio band structure data as accurately as possible using an as small as possible number of model parameters. The procedure is shown to result in models with smaller ranges and fewer orbitals than maximally localized Wannier functions but same or even better accuracy. We argue that such a procedure is more useful for automated construction of tight-binding models particularly for large-scale materials calculations.