■ Venue :  W1-1, #2430 (주흘회의실)     

■ Abstract : 

Fast solid-state Li-ion conductors are a crucial class of materials with the potential to enable all-solid-state batteries, offering enhanced safety and energy density. However, these materials remain rare, and progress in developing novel solid electrolytes has been hindered by a lack of clear descriptors for superionic conductivity and a limited understanding of ion transport mechanisms across diverse conductors, from inorganic crystals to polymers. Building on recent advances in computing power, machine-learning algorithms, material representations, and analysis tools, my research directly addresses these challenges, guiding experimental efforts to discover new superionic conductors. In this talk, I will present three of my representative efforts in this direction. First, I will discuss how identifying structural features of superionic conductors enabled high-throughput screening, leading to the discovery of over 20 novel inorganic superionic conductors. Second, I will share how my research has resolved a long-standing debate on the lithium transport mechanism—known as the ‘paddlewheel effect’ in plastic crystal phases—by providing temporally and spatially resolved correlation insights. Third, I will introduce new algorithms that I have developed to decompose Onsager transport coefficients, allowing us to identify and quantify the contributions of various transport mechanisms in lithium polymer electrolytes, with potential applications to any complex ion-conducting medium. Bringing these efforts together, I will discuss how these innovative correlation analysis tools, machine learning interatomic potentials, and generative models represent a breakthrough in achieving both high accuracy and computational efficiency, opening up unprecedented opportunities to model and understand complex dynamic phenomena that were previously inaccessible with traditional ab initio calculations or classical models.