Understanding Lithium Polysulfide Behavior

Understanding lithium polysulfide phase behavior is essential to optimizing how they may be used for energy storage applications. Lithium sulfur batteries have great potential to be an environmentally friendly, high-capacity battery, but are severely limited by their poor charge. The mechanism for this charge loss is understood: the battery loses effective reduceable mass during cycling, as lithium sulfides travel from the anode and gradually coat the cathode. during use. Previous studies have shown that an embedded metal-organic framework (MOF) is able to improve charge retention, however the mechanism is not yet well understood. Here, we investigate how lithium and sulfur atoms interact at various ratios and concentrations with the goal to train a machine-learning model that can simulate lithium polysulfides in MOFs. We performed molecular dynamics (MD) using the extended Tight Binding (xTB) semi-empirical formulism. We use biased MD, specifically meta-dynamics, to improve the sampling of a wider area in lithium polysulfide configurational space. We have found many energetically similar lithium polysulfide configurations. We also calculated energies of solvation and have preliminary data on the transition states that drive lithium polysulfide configurational changes. We intend to utilize this data to develop a machine learning algorithm for the purpose of accelerating simulations of lithium polysulfides. In this work, we hope to understand why lithium and sulfur atoms interact differently in the presence of a MOF, what features of these MOFs best suit our needs, and how may we best exploit them.