Massive Assessment of the Binding Energies of Atmospheric Molecular Clusters,Journal of Chemical Theory and Computation

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Massive Assessment of the Binding Energies of Atmospheric Molecular Clusters
Journal of Chemical Theory and Computation ( IF 5.7 ) Pub Date : 2022-11-23 , DOI: 10.1021/acs.jctc.2c00825
Andreas Buchgraitz Jensen ₁ , Jakub Kubečka ₁ , Gunnar Schmitz ₂ , Ove Christiansen ₁ , Jonas Elm _{1,

3}

Affiliation

Quantum chemical studies of the formation and growth of atmospheric molecular clusters are important for understanding aerosol particle formation. However, the search for the lowest free-energy cluster configuration is extremely time consuming. This makes high-level benchmark data sets extremely valuable in the quest for the global minimum as it allows the identification of cost-efficient computational methodologies, as well as the development of high-level machine learning (ML) models. Herein, we present a highly versatile quantum chemical data set comprising a total of 11 749 (acid)_1–2(base)_1–2 cluster configurations, containing up to 44 atoms. Utilizing the LUMI supercomputer, we calculated highly accurate PNO-CCSD(F12*)(T)/cc-pVDZ-F12 binding energies of the full set of cluster configurations leading to an unprecedented data set both in regard to sheer size and with respect to the level of theory. We employ the constructed benchmark set to assess the performance of various semiempirical and density functional theory methods. In particular, we find that the r²-SCAN-3c method shows excellent performance across the data set related to both accuracy and CPU time, making it a promising method to employ during cluster configurational sampling. Furthermore, applying the data sets, we construct ML models based on Δ-learning and provide recommendations for future application of ML in cluster configurational sampling.

中文翻译：

大气分子团结合能的大规模评估

大气分子簇的形成和生长的量子化学研究对于理解气溶胶粒子的形成很重要。然而，搜索最低自由能集群配置非常耗时。这使得高级基准数据集在寻求全局最小值方面极具价值，因为它允许识别具有成本效益的计算方法，以及高级机器学习 (ML) 模型的开发。在此，我们提出了一个高度通用的量子化学数据集，包含总共 11 749（酸）_1-2（碱）_1-2簇结构，最多包含 44 个原子。利用 LUMI 超级计算机，我们计算了全套集群配置的高精度 PNO-CCSD(F12*)(T)/cc-pVDZ-F12 结合能，从而在绝对大小和理论水平。我们使用构建的基准集来评估各种半经验和密度泛函理论方法的性能。特别地，我们发现 r ²-SCAN-3c 方法在与准确性和 CPU 时间相关的数据集上显示出出色的性能，使其成为在集群配置采样期间采用的有前途的方法。此外，应用数据集，我们构建了基于 Δ 学习的 ML 模型，并为 ML 在集群配置抽样中的未来应用提供了建议。

更新日期：2022-11-23

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