Exploring the Possibility of Machine Learning for Predicting Ionic Conductivity of Solid-State Electrolytes

Unlike conventional liquid electrolytes, solid-state electrolytes (SSEs) have gained increased attention in the domain of all-solid-state lithium-ion batteries (ASSBs) due to their safety features, higher energy/power density, better electrochemical stability, and a broader electrochemical window. SSEs, however, face several difficulties, such as poorer ionic conductivity, complicated interfaces, and unstable physical characteristics. Vast research is still needed to find compatible and appropriate SSEs with improved properties for ASSBs. Traditional trial-and-error procedures to find novel and sophisticated SSEs require vast resources and time. Machine learning (ML), which has emerged as an effective and trustworthy tool for screening new functional materials, was recently used to forecast new SSEs for ASSBs. In this study, we developed an ML-based architecture to predict ionic conductivity by utilizing the characteristics of activation energy, operating temperature, lattice parameters, and unit cell volume of various SSEs. Additionally, the feature set can identify distinct patterns in the data set that can be verified using a correlation map. Because they are more reliable, the ensemble-based predictor models can more precisely forecast ionic conductivity. The prediction can be strengthened even further, and the overfitting issue can be resolved by stacking numerous ensemble models. The data set was split into 70:30 ratios to train and test with eight predictor models. The maximum mean-squared error and mean absolute error in training and testing for the random forest regressor (RFR) model were obtained as 0.001 and 0.003, respectively.