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CBE Seminar: “Machine-learning-assisted Atomistic Modeling and Design of Complex Ionic Conductors for Next-Generation Energy Storage” (KyuJung Jun, MIT)

February 27 at 10:15 AM - 11:15 AM

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 inform mechanistic understanding in any complex ion-conducting medium. Bringing these efforts together, I will discuss how these 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.

KyuJung Jun

Postdoctoral Associate

KyuJung Jun is a postdoctoral associate in the Department of Materials Science and Engineering at MIT. He earned his Ph.D. in Materials Science and Engineering from the University of California, Berkeley, and holds a Bachelor’s degree in Nuclear Engineering with a minor in Materials Science and Engineering from Seoul National University. His research focuses on the computational discovery and mechanistic understanding of fast Li-ion conductors across diverse chemical spaces, including in organic crystals, polymers, and molecular systems, for electrochemical energy storage systems. He leverages quantummechanical and classical models, machine learning-accelerated simulations, trajectory analysis algorithms, and thermodynamics to uncover design principles for fast Li-ion conductors aimed at advancing safe and efficient energy storage devices. For his contributions in this area, he was awarded the MRS Graduate Student Research Silver Award (2022) and the ECS Battery Division Student Research Award (2023).

Details

Date:
February 27
Time:
10:15 AM - 11:15 AM
Event Category:

Organizer

Chemical and Biomolecular Engineering
Phone
215-898-8351
Email
cbemail@seas.upenn.edu
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Venue

Glandt Forum, Singh Center for Nanotechnology
3205 Walnut Street
Philadelphia, PA 19104 United States
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