Multi-Algorithm Machine Learning Framework for Predicting Crystal Structures of Lithium Manganese Silicate Cathodes Using DFT Data

Computers, materials & continua/Computers, materials & continua (Print) 2026 Open access

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Authors: Muhammad Ishtiaq, Yeon-Ju Lee, Annabathini Geetha Bhavani, Sung-Gyu Kang, N.S. Reddy
DOI: 10.32604/cmc.2026.075957
Cited by: 0

Abstract

Lithium manganese silicate (Li-Mn-Si-O) cathodes are key components of lithium-ion batteries, and their physical and mechanical properties are strongly influenced by their underlying crystal structures. In this study, a range... | Find, read and cite all the research you need on Tech Science Press

Fields of Study & Subjects

Machine Learning in Materials Science Advancements in Battery Materials Advanced Chemical Physics Studies

Publication details

Journal / source
Computers, materials & continua/Computers, materials & continua (Print)
Volume
87
Issue
1
Pages
1–10
Publication date
1 January 2026
Language
EN