32k大气分子的原子结构,对应物和热力学特性
Vitus Besel1, Milica Todorović2, Theo Kurtén3
1University of Helsinki, Institute for Atmospheric and Earth System Research, Helsinki, 00014, Finland. vitus.besel@helsinki.fi.
Scientific data
|July 12, 2023
概括
一个新的数据集,GeckoQ,为超过31,000种对大气研究至关重要的低挥发性有机化合物 (LVOC) 提供了原子结构和热力学特性. 这个资源帮助机器学习应用程序研究新的粒子形成.
科学领域:
- 大气化学 大气化学
- 计算化学计算化学
- 数据科学数据科学数据科学
背景情况:
- 低挥发性有机化合物 (LVOC) 是大气过程的关键驱动因素,如新粒子形成 (NPF).
- 现有的LVOC数据集对于大气研究中的全面机器学习研究是不够的.
- 加快大气研究需要多功能和广泛的分子数据集.
研究的目的:
- 介绍GeckoQ数据集,这是大气相关的LVOCs的分子结构和性质的全面集合.
- 为大气科学中的机器学习应用提供有价值的资源.
- 促进对大气中NPF和LVOC行为的研究.
主要方法:
- 从氧化 α-pinene,toluene 和 decane 中生成了 31,637 个分子的原子结构.
- 使用COSMOconf程序进行了广泛的符合性采样.
- 使用密度函数理论 (DFT) 与导体样选模型 (COSMO) 计算了热力学特性,包括和蒸汽压力 (pSat).
主要成果:
- GeckoQ数据集包含700万个符合规则的模型,详细的结构和热力学数据.
- 和蒸汽压力 (pSat) 计算并与SIMPOL组贡献方法进行比较.
- 研究了分子结构和热力学特性之间的关系,验证了数据集.
- 展示了使用高斯过程回归的初步机器学习应用程序.
结论:
- GeckoQ数据集为推进大气研究提供了坚实的基础,特别是在NPF.
- 该数据集使得用于预测LVOC属性的增强机器学习模型开发成为可能.
- 这项工作是计算化学和大气科学之间的桥梁,为了解大气过程提供了关键数据.
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