Lehrstuhl für Theoretische Chemie, Ruhr-Universität Bochum, 44780 Bochum, Germany.
J Chem Phys. 2018 Mar 14;148(10):102310. doi: 10.1063/1.4996819.
The design of accurate helium-solute interaction potentials for the simulation of chemically complex molecules solvated in superfluid helium has long been a cumbersome task due to the rather weak but strongly anisotropic nature of the interactions. We show that this challenge can be met by using a combination of an effective pair potential for the He-He interactions and a flexible high-dimensional neural network potential (NNP) for describing the complex interaction between helium and the solute in a pairwise additive manner. This approach yields an excellent agreement with a mean absolute deviation as small as 0.04 kJ mol for the interaction energy between helium and both hydronium and Zundel cations compared with coupled cluster reference calculations with an energetically converged basis set. The construction and improvement of the potential can be performed in a highly automated way, which opens the door for applications to a variety of reactive molecules to study the effect of solvation on the solute as well as the solute-induced structuring of the solvent. Furthermore, we show that this NNP approach yields very convincing agreement with the coupled cluster reference for properties like many-body spatial and radial distribution functions. This holds for the microsolvation of the protonated water monomer and dimer by a few helium atoms up to their solvation in bulk helium as obtained from path integral simulations at about 1 K.
由于相互作用的相当弱但强烈各向异性的性质，准确设计氦溶质相互作用势以模拟超流氦中化学复杂分子的溶剂化作用一直是一项繁琐的任务。我们表明，通过使用有效的氦-氦相互作用对和灵活的高维神经网络势（NNP）的组合，可以解决这一挑战，该 NNP 以对加的方式描述氦和溶质之间的复杂相互作用。与具有能量收敛基集的耦合簇参考计算相比，该方法使氦与水合氢离子和 Zundel 阳离子之间的相互作用能的平均绝对偏差达到 0.04 kJ mol，具有极好的一致性。势的构建和改进可以以高度自动化的方式进行，这为应用于各种反应性分子以研究溶剂化对溶质以及溶质诱导的溶剂结构化的影响打开了大门。此外，我们表明，该 NNP 方法与耦合簇参考在许多体空间和径向分布函数等性质上具有非常一致的结果。这适用于从大约 1 K 处的路径积分模拟获得的质子化水单体和二聚体的几个氦原子的微溶剂化以及它们在 bulk helium 中的溶剂化。
