About this Abstract |
| Meeting |
2027 TMS Annual Meeting & Exhibition
|
| Symposium
|
Hume-Rothery Symposium: Data-Driven Materials Discovery and Phase Stability
|
| Presentation Title |
Computational Design of Battery Guided by Degradation Mechanism |
| Author(s) |
Hyungjun Kim |
| On-Site Speaker (Planned) |
Hyungjun Kim |
| Abstract Scope |
The transition toward sustainable energy and electrified transportation has made high-performance secondary batteries essential to modern technology. Yet the development of next-generation energy storage materials remains constrained by conventional trial-and-error experimentation, which cannot keep pace with the coupled multiphysics phenomena—such as chemical degradation, mechanical failure, and thermal instability—that govern battery performance. Here, we present a computation-driven design framework that couples multiscale simulation with artificial intelligence to accelerate the discovery and optimization of advanced battery materials.
First, we employ multiscale modeling spanning atomistic to continuum scales to clarify degradation pathways, revealing how structural distortion and misalignment across particle length scales generate inhomogeneous stress and drive capacity loss. These insights inform rational strategies for improving both ionic transport and mechanical durability.
Second, we combine high-throughput screening with explainable machine learning to navigate vast material spaces, identifying key physical descriptors that govern stability and guiding the design of next-generation electrode materials. |
| Proceedings Inclusion? |
Planned: |
| Keywords |
Computational Materials Science & Engineering, Energy Conversion and Storage, Modeling and Simulation |