Data refinement for enhanced ionic conductivity prediction in garnet-type solid-state electrolytes

The demand for advanced energy storage drives an urgency to accelerate material discovery in solid-state electrolytes. In pursuit of this aim, this study presents an innovative methodology that integrates materials science insights with machine learning techniques to improve the ionic conductivity prediction in garnet-based solid electrolytes. Utilizing an expanded dataset comprising 362 data points, and exploiting easily obtainable presynthesis inputs, our approach incorporates rigorous data preprocessing inspired by materials science and machine learning methodologies. Through systematic feature selection and hyperparameter tuning, the model achieved an improved R-squared value of 0.85. This study highlights the efficacy of the proposed approach and underscores the potential of machine learning in streamlining materials discovery and design for next-generation solid-state batteries.