arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 2104.01738cond-mat.mtrl-sciphysics.comp-ph
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金属卤化物钙钛矿结构的高效密度泛函紧束缚GFN1-xTB方法

Efficient Computation of Metal Halide Perovskites Properties using the Extended Density Functional Tight Binding: GFN1-xTB Method

J. M. Vicent-Luna, S. Apergi, S. Tao

摘要:

近年来,用于光电应用的金属卤化物钙钛矿(MHP)由于其优异的性能而引起了科学界的关注。对它们的物理化学特性的基本了解对于提高它们的效率和稳定性至关重要。原子模拟和分子模拟分别在描述MHPs的光电性质和动力学行为方面发挥了重要作用。然而,MHP中的动力学和光电性质的复杂相互作用需要在相对大的系统中同时建模电子和离子,这需要高的计算成本,有时标准量子力学方法(例如密度泛函理论(DFT))负担不起。在这里,我们探讨了最近开发的密度泛函紧束缚(DFTB)方法,GFN 1-xTB,模拟MHP的适用性,目的是探索一种有效的替代DFT。GFN 1-xTB的性能计算结构,振动和光电性能的几个MHP的基准对实验和DFT计算。在一般情况下,这种方法产生准确的预测的许多性质的研究MHP,这是可比的DFT和实验。然而,我们也确定了一些缺点,与特定的几何形状和化学成分。尽管如此,我们认为GFN 1-xTB的可调性是解决任何观察到的问题的关键,我们提出了具体的目标,其改进将使这种方法成为研究MHP及其他问题的强大计算工具。

英文摘要:

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 features 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 behaviour of MHPs, respectively. However, the complex interplay of the dynamical and optoelectronic properties in MHPs requires the simultaneous modelling 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 (DFTB) 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. However, we also identify a few shortcomings, related to specific geometries and chemical compositions. Nevertheless, we believe that the tunability of GFN1-xTB is the key to resolving any observed issues and we propose specific targets, whose refinement will turn this method into a powerful computational tool for the study of MHPs and beyond.

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。

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