Data-Driven Approaches to the Electrochemistry of Multi-Component Cathode Materials

The increasing demand in electrical energy storage requires the discovery of high energy density cathode materials for lithium-ion batteries. Disordered rocksalts with Li-excess (DRX) materials are promising candidates as these materials do not require a specific cation chemistry to favor any particular ordering, and can be synthesized with a very wide variety of elements. However, the computational modeling for DRX is difficult as it can be composed from a wide variety of chemistry with site disorder.<br/><br/>In this presentation, we will demonstrate a state-of-art approach to the modeling and prediction of electrochemistry (discharge voltage profile) of DRX materials via a data-driven approach. We applied a deep neural network (DNN) trained directly on a large amount of experimental results. The DNN is trained with an end-to-end learning scheme, that includes the redox information appropriately regularized. The DNN can interpolate and make predictions for compounds that have not yet been tested, which can accelerate the exploration of DRX and other electrode materials.