tmQMg* 数据集:74万个过渡金属综合体的兴奋国家财产
Hannes Kneiding1, David Balcells1
1Hylleraas Centre for Quantum Molecular Sciences, Department of Chemistry, University of Oslo, P.O. Box 1033, Blindern, 0315 Oslo, Norway.
Journal of chemical information and modeling
|October 24, 2025
概括
74,000个过渡金属复合物的新数据集tmQMg*提供了激发状态特性,以解决化学机器学习中的数据稀缺问题. 这个资源提升了过渡金属光化学的人工智能模型.
科学领域:
- 计算化学计算化学
- 材料科学 材料科学 材料科学
- 摄影化学的使用.
背景情况:
- 化学和材料科学中的机器学习应用受到有限的数据可用性阻碍.
- 开发预测模型需要化学和物理性质的全面数据集.
研究的目的:
- 介绍 tmQMg* 数据集,其中包含了 74,000 个单核过渡金属复合物的兴奋状态特性.
- 促进人工智能模型的开发,用于预测吸收光谱,电荷转移和solvatochromism.
主要方法:
- 在 ωB97xd/def2SVP 级使用时间依赖密度函数理论 (TD-DFT) 计算激发状态属性.
- 从剑桥结构数据库提取数据.
- 计算了电子激发的波长,强度和自然过渡轨道.
- 气相和乙中的量化溶色效应.
主要成果:
- tmQMg*数据集包括74,000个过渡金属复合物的兴奋状态属性.
- 数据包括紫外线,可见光和近红外激发,包括波长,强度和电荷转移特征.
- 量化了Solvatochromic效应,提供了关于溶剂依赖的光谱变化的见解.
结论:
- tmQMg*数据集解决了化学机器学习中对数据的关键需求.
- 这一资源将使人工智能驱动的过渡金属光化学发现取得进展.
- 方便创建更准确和多功能计算模型.
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