液态水中芳香分子溶剂化达到耦合簇精度：π相互作用与疏水性的平衡
Aromatic Molecule Solvation in Liquid Water with Coupled Cluster Accuracy: The Balance of Pi-Interactions and Hydrophobicity
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中文总结 AI 辅助
研究水中芳香分子溶剂化中π相互作用与疏水性平衡问题，采用基于图形原子簇扩展训练机器学习原子间势的方法，得到CCSD(T)质量的MLIP，能再现相关能量和力，揭示常用方法不足，为高精度模拟打开大门。
中文摘要 AI 辅助
水中的芳香族有机溶质在疏水溶剂化和定向O-H⋯π氢键之间呈现出微妙的平衡，但广泛使用的力场和最先进的密度泛函方法难以提供这些关键相互作用的一致图景。我们引入一种数据高效的拟合策略，基于图形原子簇扩展来训练机器学习原子间势（MLIP），用于凝聚相模拟中具有CCSD(T)精度的水性芳香分子，仅使用有限分子簇。将该方法应用于甲苯水溶液（C₆H₅CH₃）。所得具有CCSD(T)质量的MLIP再现了体相中的耦合簇能量和力，并揭示常用方法未捕捉到亲水性和疏水性溶剂化之间的关键平衡，扭曲了芳香分子与环境的相互作用。代表性生物分子力场严重低估疏水溶剂化壳层并使界面水取向错误，同时高估π接触，导致溶剂化平衡不一致。即使是混合DFT和MP2也高估了水-π氢键断裂的势垒。我们的工作流程为水溶液的CCSD(T)质量凝聚相模拟提供了一条实用、通用的途径，构建的相互作用势为生物分子环境中π接触和疏水效应的一致、高精度基准研究打开了大门，例如蛋白质和DNA在水环境中的溶剂化。
英文摘要
Aromatic organic solutes in water exhibit a delicate balance between hydrophobic solvation and directional O-H$\cdots π$ hydrogen bonds, yet widely used force fields and state-of-the-art density functional approaches struggle to provide a consistent picture of these pivotal interactions. We introduce a data-efficient upfitting strategy to train a machine learning interatomic potential (MLIP) based on the graph atomic cluster expansion for aqueous aromatic molecules with CCSD(T) accuracy for condensed phase simulations, using only finite molecular clusters. We apply our method to aqueous toluene (C$_6$H$_5$CH$_3$). The resulting CCSD(T)-quality MLIP reproduces coupled cluster energies and forces in bulk and reveals that commonly employed methods do not capture the crucial balance between hydrophilic and hydrophobic solvation, distorting the interactions of aromatic molecules with their environment. Representative biomolecular force fields substantially understructure the hydrophobic solvation shell and misorient interfacial water, while overestimating $π$-contacts, yielding an inconsistent solvation balance. Even hybrid DFT and MP2 overestimate barriers to breaking of water-$π$ hydrogen bonds. Our workflow provides a practical, general route to CCSD(T)-quality condensed-phase simulations of aqueous solutions, and thus constructed interaction potentials now open the door to consistent, highly accurate benchmark studies of $π$-contacts and hydrophobic effects in biomolecular contexts such as solvation of proteins and DNA in aqueous environments.
