集群组:用于机器学习应用的大气分子集群的综合数据集
Yosef Knattrup1, Jakub Kubečka1, Daniel Ayoubi1
1Department of Chemistry, Aarhus University, Langelandsgade 140, 8000 Aarhus C, Denmark.
ACS omega
|July 24, 2023
概括
机器学习模型现在可以预测大气分子团的形成,这对于气候建模至关重要. 这种方法降低了计算成本,使气候预测和大气化合物的分析更准确.
科学领域:
- 大气化学 大气化学
- 气候科学 气候科学
- 计算化学的计算化学
背景情况:
- 大气中的分子集群影响全球气候,并为气候模型带来不确定性.
- 目前研究集群形成的量子化学方法是计算密集的,限制了系统的大小和范围.
- 对气溶颗粒形成的准确建模对于理解气候动态至关重要.
研究的目的:
- 开发一种机器学习 (ML) 模型,用于预测大气分子集群结构.
- 为训练ML模型创建一个大气相关集群结构的大数据库.
- 评估大气的ML模型的推断和可转移能力.
主要方法:
- 编制了大约25万个大气相关集群结构的数据库.
- 训练了一个使用FCHL19分子表示的内核回归 (KRR) ML模型.
- 评估了ML模型在推断到更大的集群,不同的分子和新的相互作用方面的表现.
主要成果:
- KRR ML模型表现出强大的能力来推断到更大的集群大小.
- 该模型准确地转移了酸相互作用,平均绝对误差低于1kcal/mol.
- 机器学习方法显示了预测失衡结构和系统与新相互作用的潜力.
结论:
- 核心脊回归模型为研究大气集群的量子化学方法提供了一个计算效率高的替代方案.
- 将ML集成到配置采样中可以显著降低计算费用.
- 这种基于ML的方法可以更准确地研究更广泛的大气化合物,提高气候模型的可靠性.
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