Revisiting the structure, interaction, and dynamical property of ionic liquid from the deep learning force field

Rational understanding of interaction and structure of ionic liquids (ILs) is vital for their application in super -capacitors. The force field trained by machine learning has aroused considerable interest in the molecular design of ILs, which can effectively balance the competition between computational accuracy and efficiency. In this work, a new deep learning force field (DPFF) for 10 different ILs was obtained, where the dataset for atomic energy and force was prepared via the ab initio molecular dynamics (MD) simulation. Using the trained DPFF, the ns-long MD simulations for various ILs were performed successfully. Combining the error analysis on atomic energy, distribution of bonds and angles, and potential energy, one can prove that the MD simulation with DPFF can describe the force and energy of ILs with ab initio precision. Meanwhile, the analysis of the vibrational spectrum and hydrogen bond suggests that the DPFF can also predict the coupling nature between coulombic and hydrogen bonding interactions within ILs reasonably. Furthermore, the DPFF for ILs is trained to extend to the bulk system. Hence, DPFF, possessing high accuracy and low computational cost, can serve as an effective tool for the molecular design of new ILs-based electrolytes for high-performance energy storage devices.

Automated summary

The deep learning force field (DPFF) can accurately describe the force and energy of ionic liquids, and reasonably predict the coupling nature between coulombic and hydrogen bonding interactions.