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arXiv 1809.01753cond-mat.mtrl-sci
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高通量DFT计算中Monkhorst-Pack k点和平面波截止的收敛性和机器学习预测

Convergence and machine learning predictions of Monkhorst-Pack k-points and plane-wave cut-off in high-throughput DFT calculations

Kamal Choudhary, Francesca Tavazza

摘要:

在这项工作中,我们开发了一个自动收敛程序的密度泛函(DFT)计算中的k点和平面波截止,并将其应用到超过30000种材料。用于自动收敛的计算框架可以将用户定义的输入作为收敛准则。对于k点,我们将每单元能量(EPC)收敛到0.001 eV/单元容差,并将结果与使用0.001 eV/原子的每原子能量(EPA)收敛标准获得的结果进行比较。从我们的结果的分析,我们可以将k点密度和平面波截止材料参数,如密度,带的斜率,带交叉的数量,最大平面波截止用于赝势产生,晶体系统,和材料中的独特物种的数量。我们还确定了一些物质种类,这些物质种类需要比其他物质种类更仔细地收敛。此外,我们统计调查的依赖性的k点和截止交换相关泛函。我们利用所有这些数据来训练机器学习模型,以预测广义材料的k点线密度和平面波截止。这将为用户提供一个良好的起点,以收敛DFT计算。使用的代码和融合数据可在以下网站上获得:https://jarvis.nist.gov/和https://github.com/usnistgov/jarvis。

英文摘要:

In this work, we developed an automatic convergence procedure for k-points and plane wave cut-off in density functional (DFT) calculations and applied it to more than 30000 materials. The computational framework for automatic convergence can take a user-defined input as a convergence criterion. For k-points, we converged energy per cell (EPC) to 0.001 eV/cell tolerance and compared the results with those obtained using an energy per atom (EPA) convergence criteria of 0.001 eV/atom. From the analysis of our results, we could relate k-point density and plane wave cut-off to material parameters such as density, the slope of bands, number of band-crossings, the maximum plane-wave cut-off used in pseudopotential generation, crystal systems, and the number of unique species in materials. We also identified some material species that would require more careful convergence than others. Moreover, we statistically investigated the dependence of k-points and cutoff on exchange-correlation functionals. We utilized all this data to train machine learning models to predict the k-point line density and plane-wave cut-off for generalized materials. This would provide users with a good starting point towards converged DFT calculations. The code used, and the converged data are available on the following websites: https://jarvis.nist.gov/, and https://github.com/usnistgov/jarvis .

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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