通过检查金属氧化状态发现"计算准备"的MOF数据库中的高结构错误率
Andrew J White1, Marco Gibaldi1, Jake Burner1
1Department of Chemistry and Biomolecular Sciences, University of Ottawa, Ottawa, Canada K1N 9A4.
Journal of the American Chemical Society
|May 16, 2025
概括
许多金属有机框架 (MOF) 数据库包含化学无效的结构. 我们的MOSAEC算法准确地检测出这些错误,
科学领域:
- 材料科学
- 计算化学
- 晶体学
背景情况:
- 准备好计算的数据库对于高通量选 (HTS) 和材料发现中的机器学习至关重要.
- 金属有机框架 (MOF) 数据库的结构完整性在很大程度上未得到量化,这可能会影响HTS和ML模型的可靠性.
- 现有的MOF数据库可能包含化学无效的结构,导致不准确的计算选结果.
研究的目的:
- 引入MOSAEC,一种基于金属氧化状态的新算法,用于检测化学无效的MOF结构.
- 在领先的MOF数据库和HTS研究中量化结构错误的普遍性.
- 使用MOF提高计算材料发现的可靠性.
主要方法:
- 开发MOSAEC算法,通过分析金属氧化状态来识别化学无效的MOF结构.
- 手动验证MOSAEC与来自CORE数据库的14796个MOF结构.
- 检查了14个主要的MOF数据库中的190多万个结构,并分析了8个HTS研究中的结构.
主要成果:
- 在手动验证过程中,MOSAEC在检测化学无效的MOF结构方面取得了96%的准确性.
- 在分析的14个主要的MOF数据库中发现结构错误率超过40%.
- 分析显示,最近的HTS研究中52%的高性能MOF候选物是化学无效的.
结论:
- MOSAEC算法有效地识别化学无效的MOF结构,提高计算选的数据质量.
- 主要数据库和HTS研究中的大量MOF结构包含错误,需要数据验证.
- 通过HTS和机器学习提高MOF数据库的结构准确性至关重要.
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