High-dimensional neural network potential for liquid electrolyte simulations

Liquid electrolytes are the most common electrolyte classes used in Li-ion batteries, which are a critical technology of the modern world. However, we still lack the computational tools required to accurately calculate key properties of these materials (viscosity, ionic diffusivity) from first principles necessary to sup-port improved designs. In this work, we report a machine learning-based force field for liquid electrolyte simulations which bridges the gap between the accuracy of range-separated hybrid density functional theory and the efficiency of classical force fields. Predictions of material properties made with this force field are quantitatively accurate compared to experimental data. Our model uses the QRNN deep neural network architecture, which includes both long-range interactions and global charge equilibration. The training dataset is composed solely of non-periodic DFT, allowing the practical use of an accurate theory (here, ωB97X-D3BJ/def2-TZVPD) which would be prohibitively expensive for generating large datasets with periodic DFT. In this report we focus on seven common carbonates and LiPF6, but this methodology has very few assumptions and can be readily applied to any liquid electrolyte system. This provides a promising path forward for large scale atomistic modeling of many important battery chemistries.