Deconstruction Analysis and Regeneration Optimization of Solid Electrolyte Components for Energy Storage Based on the Finite Data Statistics.

ABSTRACT: Solid-state electrolytes have a superior theoretical energy density, higher safety, and longer cycle stability. However, researching solid-state electrolyte materials with high performance remains difficult. Machine learning methods can accurately predict performance, speed up the screening process, reduce expensive and time-consuming experimental trials, and have significant advantages in the optimization and fabrication of solid-state electrolyte materials. This research constructed a solid-state electrolyte database based on existing data, trained a random forest (RF) algorithm model to achieve performance prediction of Na superionic conductor (NASICON) solid-state electrolytes by using only the component information, and analyzed the effect of different components. The optimization res

SUBMITTER: Jian B 

PROVIDER: S-EPMC12824757 | biostudies-literature | 2026 Jan

REPOSITORIES: biostudies-literature