Machine learning approach accelerates search for solid state electrolytes

Abstract

In the current aera of rapid development in the field of electric vehicles and electrochemical energy storage, solid-state battery technology is attracting much research and attention. Solid-state electrolytes, as the key component of next-generation battery technology, are favored for their high safety, high energy density, and long life. However, finding high-performance solid-state electrolytes is the primary challenge for solid-state battery applications. Focusing on inorganic solid-state electrolytes, this work highlights the need for ideal solid-state electrolytes to have low electronic conductivity, good thermal stability, and structural and phase stability. Traditional experimental and theoretical computational methods suffer from inefficiency, thus machine learning methods become a novel path to intelligently predict material properties by analyzing a large number of inorganic structural properties and characteristics. Through the gradient descent-based XGBoost algorithm, we successfully predicted the energy band structure and stability of the materials, and screened out only 194 ideal solid-state electrolyte structures from more than 6000 structures that satisfy the requirements of low electronic conductivity and stability simultaneously, which greatly accelerated the development of solid-state batteries.