TBHubbard:用于金属有机框架的紧密结合和扩展的哈伯德模型数据集
Pamela Costa Carvalho1, Federico Zipoli2,3, Alan C Duriez4
1IBM Research, São Paulo, 04007-900, SP, Brazil.
Scientific data
|November 12, 2025
概括
这项研究将QMOF数据集与10,000个金属有机框架 (MOF) 的模拟电子结构数据增强. 这为机器学习和量子计算在材料科学中的应用提供了关键数据.
科学领域:
- 材料科学 材料科学 材料科学
- 计算化学计算化学
- 凝聚物质物理学 凝聚物质物理学
背景情况:
- 金属有机框架 (MOF) 由于其可调节的结构,在化学分离中提供了多种应用.
- 由于现有的晶体学数据的复杂性,MOFs的计算选受到阻碍.
- 机器学习 (ML) 和量子计算 (QC) 需要广泛的,精心策划的数据集才能有效应用.
研究的目的:
- 增强QMOF数据集,以模拟金属有机框架 (MOF) 的电子结构数据.
- 为了实现先进的计算选和特定应用的MOF的设计.
- 提供支持机器学习和量子计算任务的数据.
主要方法:
- 在MOF上应用紧固结合 (TB) 和密度函数理论 (DFT) 进行电子结构计算.
- 为10,000个MOF生成了TB表示.
- 开发了240个含过渡金属MOF的扩展哈伯德模型 (EHM) 表示,包括自相一致的U和V参数.
主要成果:
- 在QMOF数据集中为10,000个MOF提供了广泛的模拟电子结构数据.
- 为MOFs的一个子集生成了自相一致的Hubbard参数,这对于EHM至关重要.
- 建立了用于识别结构-属性相关性的计算工作流程.
结论:
- 增强的QMOF数据集有助于ML驱动的MOF的发现和设计.
- 提供的数据支持量子计算算法,利用TB的哈密尔顿和哈伯德参数.
- 数据的公开可用性促进了MOF计算科学中的可复制性和进一步研究.
相关概念视频
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