.png){kind=icon}
.png){kind=icon}
Return
Machine learning for redox potential prediction and application on a Na-ion battery system
.png){kind=icon}
OA
AI
M
F
S
S
S
S
M
DOI：10.1016/j.solidstatesciences.2026.108336.png)
.png){kind=icon}
.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
• GeoCGNN predicts redox potential in Na-ion battery cathode materials. • Two direct and one indirect ML approaches for redox potential are compared. • Over 500,000 layered metal (Mn,Ni,Ti) oxide compositions are screened. • Compositions with high redox potential and low formation energy are identified. • Revised prediction models correct outputs for non-relaxed crystal structures.
Keywords:
Machine learning
Battery material
Electrochemical energy storage
Sodium ion battery
Graph neural network
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
3.3
Papers:
6.0K
Citations:
9.2K
Organization
E
Cited Papers