Discovery device for ceramic electrolyte materials included in composite electrolytes, method for discovering ceramic electrolyte materials, and recording medium for performing the same

Ceramic electrolyte material discovery device for screening LLZO dual-doping candidates using machine learning

This technology is a system and method for high-speed screening of optimal filler materials for polymer/oxide composite electrolytes. It extracts materials with dual-doped Li, La, and Zr sites in an LLZO (Li7La3Zr2O12) structure from a database and predicts their properties using machine learning (RF, LGBM).

Existing research on LLZO-based filler materials faces challenges due to the vast number of possible combinations of doping elements, which requires significant time and cost for experimental approaches and makes it difficult to identify the optimal composition to overcome the performance limitations of composite electrolytes.

By doing so, this technology can contribute substantially to securing the commercial competitiveness of secondary battery electrolytes.

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Key Features:

• A candidate extraction unit that extracts dopant candidates meeting specific criteria from a pre-established material database
• A machine learning screening unit that screens the extracted dopant candidates using a pre-trained machine learning model
• A configuration that explores materials with dual-doped lithium, lanthanum, and zirconium sites in an LLZO structure
• A discovery device configured to automatically select ceramic electrolyte materials suitable as filler materials for composite electrolytes

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