Yilin Xie
Papers
1
Total Citations
3
H-Index
1
About
Yilin Xie is a leading researcher in energy storage and machine learning, with a focus on advancing battery degradation analysis. Their most-cited work, "MELODI: An explainable machine learning method for mechanistic disentanglement of battery calendar aging" (2025), introduces a novel framework that combines interpretable AI with electrochemical modeling to unravel the complex, overlapping mechanisms behind battery calendar aging. This contribution is pivotal for improving battery lifespan predictions and design, offering transparency where traditional black-box models fall short. With 3 citations already in its early publication year, the work signals growing influence in the field. Xie’s research bridges computational science and materials engineering, providing tools that enable researchers to pinpoint degradation causes—such as lithium inventory loss or electrode structural decay—with unprecedented clarity. Their approach not only accelerates battery development for electric vehicles and grid storage but also sets a benchmark for explainable AI in materials science. For students and researchers, Xie’s work exemplifies how machine learning can be harnessed to solve pressing energy challenges while maintaining scientific rigor and interpretability.
Research Focus
Key Achievements
Top Papers
- 1