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• W4292092508 abstract "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 $impuritycdots p$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." @default.
• W4292092508 created "2022-08-17" @default.
• W4292092508 creator A5026599330 @default.
• W4292092508 creator A5053711163 @default.
• W4292092508 creator A5072717593 @default.
• W4292092508 creator A5090285803 @default.
• W4292092508 date "2022-08-17" @default.
• W4292092508 modified "2023-10-17" @default.
• W4292092508 title "Neural network interaction potentials for <i>para</i>-hydrogen with flexible molecules" @default.
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• W4292092508 doi "https://doi.org/10.1063/5.0100953" @default.
• W4292092508 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/35987576" @default.
• W4292092508 hasPublicationYear "2022" @default.
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