Identification and Synthesis of Novel Superionic Conductor Solid Electrolytes

Proper and efficient analysis of complex data from different quantum calculated material databases is extremely important for discovery of novel battery materials. This could revolutionize the battery field by selecting or designing effective materials for high performance and robust batteries. Our team will develop an innovative process to quickly identify top candidates for Lithium superionic conductor solid electrolytes from quantum calculated material databases using recently developed model based on machine learning approach. The identified candidates will be synthesized and the ionic conductivities will be measured using experimental methods. The logistic regression model and few screening criteria will be used to identify top candidates for Lithium superionic conductor. Few of the identified candidates will be synthesized and characterized using experimental method. If required, the descriptors used in the logistic regression model will be modified on the basis of results from data analysis and experimental investigation, so that final Lithium solid electrolyte is robust enough. The developed process will guide us to identify and synthesize novel Sodium superionic solid electrolyte materials from existing data using machine learning algorithms during Phase II part of study.