Self-Learning Molecular Design for High Lithium-Ion Conductive Ionic Liquids using Maze Game

A self-learning artificial intelligence system for an autonomous molecular search was recently utilized in place of laborious material development processes by humans. In this approach, because the evaluation of unsuitable or unrealistic candidates considerably decreases the search efficiency, prior knowledge of the chemistry and engineering requirements should be embedded into the molecular-generative algorithm. However, when using naive rule-based restrictions, one must implement the complex rule logic into the code each time, depending on the materials and potential applications. Herein, we propose a molecular-generative method using a maze game to control the allowable constituent fragments of molecules, which improves the flexibility and consistency to implement the rules. We performed an autonomous search for optimized cation structures of high Li-ion conductive ionic liquids evaluated by molecular dynamics simulations, in its practically reasonable scope defined by the maze game. From the search, we discover that acyl ammonium cations are favorable for high Li-ion conductivity because of the high association between the cations and Li ions. These results broaden our existing insight owing to the ability to explore beyond our practical experiences.