Machine Learning for Selecting High-Energy Phosphate Cathode Materials.

ABSTRACT: The limited energy density inherent in cathode materials remains a marked barrier to the widespread adoption of sodium-ion batteries. Despite considerable research efforts, the precise influence of atomic and crystalline configurations on energy density is not yet fully understood, creating a knowledge gap that hinders the rational design of advanced cathode materials. In this study, we propose a machine learning approach to systematically identify promising cathode materials with high energy densities. Our model highlights the critical roles of entropy and equivalent electronegativity, among other properties such as molecular mass, electron affinity, and average ionic radius. Based on these insights, we successfully synthesized Na₃Mn_0.5V_0.5Ti_0.5

SUBMITTER: Dang Y 

PROVIDER: S-EPMC12304885 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature