In recent years, metal halide perovskites (MHPs) for optoelectronic applications have attracted the attention of the scientific community due to their outstanding performance. The fundamental understanding of their physicochemical properties is essential for improving their efficiency and stability. Atomistic and molecular simulations have played an essential role in the description of the optoelectronic properties and dynamical behavior of MHPs, respectively. However, the complex interplay of the dynamical and optoelectronic properties in MHPs requires the simultaneous modeling of electrons and ions in relatively large systems, which entails a high computational cost, sometimes not affordable by the standard quantum mechanics methods, such as density functional theory (DFT). Here, we explore the suitability of the recently developed density functional tight binding method, GFN1-xTB, for simulating MHPs with the aim of exploring an efficient alternative to DFT. The performance of GFN1-xTB for computing structural, vibrational, and optoelectronic properties of several MHPs is benchmarked against experiments and DFT calculations. In general, this method produces accurate predictions for many of the properties of the studied MHPs, which are comparable to DFT and experiments. We also identify further challenges in the computation of specific geometries and chemical compositions. Nevertheless, we believe that the tunability of GFN1-xTB offers opportunities to resolve these issues and we propose specific strategies for the further refinement of the parameters, which will turn this method into a powerful computational tool for the study of MHPs and beyond.

中文翻译：

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使用密度泛函紧密结合有效计算卤化物钙钛矿的结构和电子特性：GFN1-xTB 方法

近年来，用于光电应用的金属卤化物钙钛矿（MHP）由于其出色的性能引起了科学界的关注。对其理化性质的基本了解对于提高其效率和稳定性至关重要。原子和分子模拟分别在描述 MHP 的光电特性和动力学行为方面发挥了重要作用。然而，MHP 中动力学和光电特性的复杂相互作用需要在相对较大的系统中同时对电子和离子进行建模，这需要很高的计算成本，有时标准量子力学方法无法承受，例如密度泛函理论 (DFT) ）。这里，我们探索了最近开发的密度泛函紧结合方法 GFN1-xTB 的适用性，用于模拟 MHP，目的是探索 DFT 的有效替代方案。GFN1-xTB 在计算多个 MHP 的结构、振动和光电特性方面的性能以实验和 DFT 计算为基准。通常，该方法对所研究的 MHP 的许多属性产生准确的预测，这与 DFT 和实验相当。我们还确定了计算特定几何形状和化学成分的进一步挑战。尽管如此，我们相信 GFN1-xTB 的可调性为解决这些问题提供了机会，我们提出了进一步细化参数的具体策略，