论文标题
与柔性分子的para-Hydrogen的神经网络相互作用电位
Neural Network Interaction Potentials for para-Hydrogen with Flexible Molecules
论文作者
论文摘要
$ Para $ hydrogen（$ p $ h $ \ rm_2 $）中的分子杂质的研究是促进我们对我们对内分子内和分子间相互作用的理解的关键，包括它们对这种玻色量量子溶剂的超流体响应的影响。这包括使用一个或几个$ p $ h $ \ rm_2 $的标签，微覆盖制度和矩阵隔离。但是，玻色粒$ p $ h $ \ rm_2 $环境与分子杂质的（ro-）振动运动之间的基本耦合仍然鲜为人知。量子模拟可以提供必要的原子见解，但是需要非常准确的相互作用描述。在这里，我们提出了一种数据驱动的方法，用于生成$杂质\ cdots p $ h $ \ rm_2 $基于机器学习技术的交互潜力，该技术保留了杂质的全部灵活性。我们采用了良好的绝热阻碍转子（AHR）平均技术，包括核自旋统计数据对$ p $ h $ \ rm_2 $的对称式旋转量子数的影响。将这种平均过程嵌入高维神经网络电位（NNP）框架中，可以以耦合的群集精度（即CCSD（T $^*$） - F12A/AVTZCP以自动化的方式生成高度准确的AHR均能NNPS。 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 H$\rm _2$O$\cdots p$H$\rm_2$ and H$\rm _3$O$^+\cdots p$H$\rm_2$ interactions.使用路径积分模拟，我们显示了氢阳离子阳离子的表明，类似伞状的隧道倒置对第一和第二$ p $ h $ \ rm_2 $ microsolvation壳具有很大的影响。我们协议的数据驱动性质为研究玻色粒$ p $ h $ \ rm_2 $量子溶剂的研究打开了大门，用于多种嵌入式杂质。
The study of molecular impurities in $para$-hydrogen ($p$H$\rm_2$) 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 one or very few $p$H$\rm_2$, the microsolvation regime, and matrix isolation. However, the fundamental coupling between the bosonic $p$H$\rm_2$ environment and the (ro-)vibrational motion of molecular impurities remains poorly understood. Quantum simulations can in provide the necessary atomistic insight, but very accurate descriptions of the involved interactions are required. Here, we present a data-driven approach for the generation of $impurity\cdots p$H$\rm_2$ interaction potentials based on machine learning techniques which retain the full flexibility of the impurity. 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 $p$H$\rm_2$. 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 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 H$\rm _2$O$\cdots p$H$\rm_2$ and H$\rm _3$O$^+\cdots p$H$\rm_2$ interactions. Using path integral simulations we show for the hydronium cation that umbrella-like tunneling inversion has a strong impact on the first and second $p$H$\rm_2$ microsolvation shells. The data-driven nature of our protocol opens the door to the study of bosonic $p$H$\rm_2$ quantum solvation for a wide range of embedded impurities.
