上海大学学报(自然科学版) ›› 2026, Vol. 32 ›› Issue (1): 1-16.doi: 10.12066/j.issn.1007-2861.2693

• 特稿 •    

高通量密度泛函理论计算的可重复性

鲁晨曦1, 李木森2,3, Jeffrey Robert Reimers1,3   

  1. 1. 上海大学 量子与分子结构国际中心, 上海 200444;
    2. 上海大学 材料基因组工程研究院, 上海 200444;
    3. 悉尼科技大学 数学物理学院, 悉尼 2007
  • 收稿日期:2025-06-10 发布日期:2026-03-16
  • 通讯作者: jeffrey Robert Reimers (1956-),男,教授,澳大利亚科学院院士,博士生导师,博士,研究方向为凝聚态物理. E-mail:jeffrey.reimers@uts.edu.au
  • 基金资助:
    国家自然科学基金青年科学基金资助项目(12404276);国家自然科学基金专项资助项目(12347164);中国博士后科学基金资助项目(2024T170541,GZC20231535)

Reproducibility of high-throughput density functional theory calculations

LU Chenxi1, LI Musen2,3, Jefirey Robert REIMERS1,3   

  1. 1. International Centre for Quantum and Molecular Structures, Shanghai University, Shanghai 200444, China;
    2. Materials Genome Institute, Shanghai University, Shanghai 200444, China;
    3. School of Mathematical and Physical Sciences, University of Technology Sydney, Sydney 2007, Australia
  • Received:2025-06-10 Published:2026-03-16

摘要: 密度泛函理论(density functional theory,DFT)的标准计算方案虽具有普适性,但因其代码实现存在差异,实际计算时通常需要手动调试参数,而高通量计算则采用预设流程进行参数设置.本工作以带隙为关键性质,揭示计算流程差异对高通量计算结果可重复性的影响.研究表明,满足可重复性基本要求的计算策略是应使用DFT计算优化结构而非实验结构,同时需要保证布里渊区积分网格精度.本研究为DFT计算的可重现性和结果可靠应用奠定了基础,对方法开发和人工智能模型训练均具有重要意义.

关键词: 可重复计算, 高通量计算, 密度泛函理论

Abstract: While standard computational protocols for density functional theory (DFT) have universal applicability, differences exist in code implementations. Specific applications require manual parameter optimization, whereas high-throughput calculations employ predefined workflows. This paper uses the bandgap as a key property to reveal the impact of computational workflow differences on the reproducibility of high-throughput calculation results. The study proposes basic requirements for ensuring reproducibility: using structures optimised using the same procedure as used to calculate properties and ensuring Brillouin zone integration grid accuracy. This research establishes a foundation for the reproducibility of DFT calculations and reliable application of results, which is of great significance for method development and artificial intelligence model training.

Key words: reproducible calculations, high-throughput computing, density functional theory (DFT)

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