Kubečka Jakub, Knattrup Yosef, Trolle Georg Baadsgaard, Reischl Bernhard, Lykke-Møller August Smart, Elm Jonas, Neefjes Ivo
Aarhus University, Department of Chemistry, Langelandsgade 140, Aarhus DK 8000, Denmark.
University of Helsinki, Institute for Atmospheric and Earth System Research (INAR)/Physics P.O.Box 64, Helsinki FI 00014, Finland.
ACS Omega. 2025 Aug 18;10(34):39148-39161. doi: 10.1021/acsomega.5c05634. eCollection 2025 Sep 2.
Accurately modeling the binding free energies associated with molecular cluster formation is critical for understanding atmospheric new particle formation. Conventional quantum-chemistry methods, however, often struggle to describe thermodynamic contributions, particularly in systems exhibiting significant anharmonicity and configurational complexity. We employed umbrella sampling, an enhanced-sampling molecular dynamics technique, to compute Gibbs binding free energies for clusters formed from a diverse set of new particle formation precursors, including sulfuric acid, ammonia, dimethylamine, and water. By performing umbrella sampling along the evaporation coordinate, using forces computed at the semiempirical GFN1-xTB level of theory, we effectively capture entropic effects such as vibrational anharmonicities and transitions between different configurational minima, while avoiding errors from symmetry overcounting. In addition, we explored machine-learning-enhanced umbrella sampling simulations using neural network potentials trained on higher-level quantum chemistry data, demonstrating the feasibility of this approach for improving accuracy while maintaining computational efficiency. Our results show improved agreement with experimental values compared to conventional methods. We also present examples of gas-to-particle uptake processes, providing insights into cluster and aerosol-surface chemistry using first-principles approaches rather than commonly used molecular-mechanics force fields. This study demonstrates the importance of accounting for dynamics in predicting molecular binding thermodynamics in complex environments and highlights the potential of combining physics-based simulations with machine learning for reliable and scalable predictions.
准确模拟与分子团簇形成相关的结合自由能对于理解大气中新粒子的形成至关重要。然而，传统的量子化学方法常常难以描述热力学贡献，特别是在表现出显著非谐性和构型复杂性的系统中。我们采用了伞形采样这一增强采样分子动力学技术，来计算由多种新粒子形成前体（包括硫酸、氨、二甲胺和水）形成的团簇的吉布斯结合自由能。通过沿着蒸发坐标进行伞形采样，使用在半经验GFN1-xTB理论水平计算的力，我们有效地捕捉了诸如振动非谐性和不同构型极小值之间转变等熵效应，同时避免了对称过度计数带来的误差。此外，我们探索了使用基于更高层次量子化学数据训练的神经网络势进行机器学习增强的伞形采样模拟，证明了这种方法在提高准确性同时保持计算效率的可行性。与传统方法相比，我们的结果显示与实验值的一致性得到了改善。我们还展示了气体到颗粒的摄取过程示例，使用第一性原理方法而非常用的分子力学力场，深入了解团簇和气溶胶-表面化学。这项研究证明了在复杂环境中预测分子结合热力学时考虑动力学的重要性，并突出了将基于物理的模拟与机器学习相结合以进行可靠且可扩展预测的潜力。
