Lehrstuhl für Theoretische Chemie, Ruhr-Universität Bochum, 44780 Bochum, Germany.
J Chem Phys. 2022 Aug 21;157(7):074302. doi: 10.1063/5.0100953.
The study of molecular impurities in para-hydrogen (pH) clusters is key to push forward our understanding of intra- and intermolecular interactions, including their impact on the superfluid response of this bosonic quantum solvent. This includes tagging with only one or very few pH, the microsolvation regime for intermediate particle numbers, and matrix isolation with many solvent molecules. However, the fundamental coupling between the bosonic pH environment and the (ro-)vibrational motion of molecular impurities remains poorly understood. Quantum simulations can, in principle, provide the necessary atomistic insight, but they require very accurate descriptions of the involved interactions. Here, we present a data-driven approach for the generation of impurity⋯pH interaction potentials based on machine learning techniques, which retain the full flexibility of the dopant species. We employ the well-established adiabatic hindered rotor (AHR) averaging technique to include the impact of the nuclear spin statistics on the symmetry-allowed rotational quantum numbers of pH. Embedding this averaging procedure within the high-dimensional neural network potential (NNP) framework enables the generation of highly accurate AHR-averaged NNPs at coupled cluster accuracy, namely, explicitly correlated coupled cluster single, double, and scaled perturbative triples, CCSD(T*)-F12a/aVTZcp, in an automated manner. We apply this methodology to the water and protonated water molecules as representative cases for quasi-rigid and highly flexible molecules, respectively, and obtain AHR-averaged NNPs that reliably describe the corresponding HO⋯pH and HO⋯pH interactions. Using path integral simulations, we show for the hydronium cation, HO, that umbrella-like tunneling inversion has a strong impact on the first and second pH microsolvation shells. The automated and data-driven nature of our protocol opens the door to the study of bosonic pH quantum solvation for a wide range of embedded impurities.
研究仲氢 (pH) 团簇中的分子杂质对于推动我们对内分子和分子间相互作用的理解至关重要，包括它们对这种玻色量子溶剂超流响应的影响。这包括仅用一个或很少几个 pH 进行标记、中间粒子数的微溶剂化状态以及用许多溶剂分子进行矩阵隔离。然而，玻色 pH 环境与分子杂质的（旋转）振动运动之间的基本耦合仍然知之甚少。量子模拟原则上可以提供必要的原子洞察力，但它们需要对所涉及的相互作用进行非常准确的描述。在这里，我们提出了一种基于机器学习技术的生成杂质⋯pH 相互作用势的数据驱动方法，该方法保留了掺杂剂物种的完全灵活性。我们采用成熟的绝热受阻转子 (AHR) 平均技术来包括核自旋统计对 pH 允许的旋转量子数的影响。将此平均程序嵌入高维神经网络势 (NNP) 框架中，使我们能够以自动化方式生成具有高准确性的 AHR 平均 NNP，即在耦合簇精度下，即明确相关的耦合簇单、双和缩放微扰三重，CCSD(T*)-F12a/aVTZcp。我们将这种方法应用于水和质子化水分子作为准刚性和高度灵活分子的代表性案例，并获得能够可靠描述相应 HO⋯pH 和 HO⋯pH 相互作用的 AHR 平均 NNP。使用路径积分模拟，我们表明对于水合氢离子，HO，伞形隧道反转对第一和第二 pH 微溶剂化壳具有强烈影响。我们协议的自动化和数据驱动性质为研究广泛的嵌入式杂质的玻色 pH 量子溶剂化开辟了道路。
