Szabadi András, Doknic Aleksandar, Netsch Jonathan, Pálvögyi Ádám Márk, Steinhauser Othmar, Schröder Christian
Department of Computational Biological Chemistry, Faculty of Chemistry, University of Vienna, Währingerstr. 17, A-1090 Vienna, Austria.
University of Vienna, Doctoral School in Chemistry (DoSChem), Währingerstr. 42, 1090 Vienna, Austria.
Phys Chem Chem Phys. 2023 Jul 26;25(29):19882-19890. doi: 10.1039/d3cp00932g.
We employ polarizable molecular dynamics simulations with the newly developed FFGenOpt parametrization tool to reproduce IR spectra of several ionic liquid cations and anions in the gas phase. Our results show that polarizable force fields in the bulk phase provide a reasonable compromise between computational effort and accuracy for investigating IR spectra when treating the transition from gas to liquid phase carefully. Although collectivity seems to play only a minor role, the liquid phase not only changes the electrostatic environment of the molecules but also introduces friction and intermolecular interactions altering the IR spectrum significantly. In addition to the classical force field approach, we also tested if the additional computational effort of machine learning potentials justifies their application in reproducing IR spectra. However, the main purpose of this work is to improve the quality of polarizable force fields concerning vibrations and not the prediction of IR spectra which can be better done with quantum-mechanical cluster approaches.
我们使用新开发的FFGenOpt参数化工具进行可极化分子动力学模拟，以重现几种离子液体阳离子和阴离子在气相中的红外光谱。我们的结果表明，当仔细处理从气相到液相的转变时，本体相中的可极化力场在研究红外光谱的计算量和准确性之间提供了合理的折衷。尽管集体效应似乎只起次要作用，但液相不仅改变了分子的静电环境，还引入了摩擦力和分子间相互作用，从而显著改变了红外光谱。除了经典力场方法外，我们还测试了机器学习势的额外计算量是否证明其在重现红外光谱中的应用是合理的。然而，这项工作的主要目的是提高与振动相关的可极化力场的质量，而不是用红外光谱预测，后者用量子力学簇方法可以做得更好。
