Enhancing ReaxFF for Molecular Dynamics Simulations of Lithium-Ion Batteries: An interactive reparameterization protocol

TL;DR

This paper presents an interactive reparameterization protocol to improve ReaxFF force field accuracy for molecular dynamics simulations of lithium-ion battery solid-electrolyte interphase, focusing on lithium fluoride properties.

Contribution

The work introduces a Python-based automated protocol for reparameterizing ReaxFF, enhancing its accuracy in modeling LiF and lithium diffusivity in battery simulations.

Findings

ReaxFF accurately models LiF's solid state

Significant improvement in lithium diffusivity prediction

ReaxFF's sensitivity to training set limits its interpolation ability

Abstract

Lithium-ion batteries (LIBs) are crucial for the green economy, powering portable electronics, electric vehicles, and renewable energy systems. The solid-electrolyte interphase (SEI) is vital for LIB operation, performance, and safety. SEI forms due to thermal instability at the anode-electrolyte interface, with electrolyte reduction products stabilizing it as an electrochemical buffer. This article aims to enhance the parametrization of the ReaxFF force field for accurate molecular dynamics (MD) simulations of SEI in LIBs. Focus is on Lithium Fluoride (LiF), an inorganic salt with favorable properties in the passivation layer. The protocol heavily relies on Python libraries for atomistic simulations, enabling robust automation of reparameterization steps. The proposed configurations and dataset enable the new ReaxFF to accurately represent the solid nature of LiF and improve mass transport property prediction in MD simulations. Optimized ReaxFF surpasses previous force fields by adjusting lithium diffusivity, resulting in a significant improvement in room temperature prediction by two orders of magnitude. However, our comprehensive investigation reveals ReaxFF's strong sensitivity to the training set, challenging its ability to interpolate the potential energy surface. Consequently, the current ReaxFF formulation is suitable for modeling specific phenomena by utilizing the proposed interactive reparameterization protocol and constructing a dataset. This work is an important step towards refining ReaxFF for precise reactive MD simulations, shedding light on challenges and limitations in force field parametrization. The demonstrated limitations underscore the potential for developing more advanced force fields through our interactive reparameterization protocol, enabling accurate and comprehensive MD simulations in the future.