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Open Access

ARTICLE

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

Muhammad Ishtiaq1, Yeon-Ju Lee2, Annabathini Geetha Bhavani3, Sung-Gyu Kang1,*, Nagireddy Gari Subba Reddy2,*
1 Department of Materials Engineering and Convergence Technology, Gyeongsang National University, 501 Jinju-Daero, Jinju, 52828, Republic of Korea
2 School of Materials Science and Engineering, Engineering Research Institute, Gyeongsang National University, 501 Jinju-Daero, Jinju, 52828, Republic of Korea
3 Department of Chemistry, SRM Institute of Science and Technology, Delhi-NCR Campus, Delhi-Meerut Road, Modinagar, Ghaziabad, 201204, Uttar Pradesh, India
* Corresponding Author: Sung-Gyu Kang. Email: email; Nagireddy Gari Subba Reddy. Email: email
(This article belongs to the Special Issue: M5S: Multiphysics Modelling of Multiscale and Multifunctional Materials and Structures)

Computers, Materials & Continua https://doi.org/10.32604/cmc.2026.075957

Received 11 November 2025; Accepted 05 January 2026; Published online 19 January 2026

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 of machine learning (ML) algorithms were developed and compared to predict the crystal systems of Li-Mn-Si-O cathode materials using density functional theory (DFT) data obtained from the Materials Project database. The dataset comprised 211 compositions characterized by key descriptors, including formation energy, energy above the hull, bandgap, atomic site number, density, and unit cell volume. These features were utilized to classify the materials into monoclinic (0) and triclinic (1) crystal systems. A comprehensive comparison of various classification algorithms including Decision Tree, Random Forest, XGBoost, Support Vector Machine, k-Nearest Neighbor, Stochastic Gradient Descent, Gaussian Naïve Bayes, Gaussian Process, and Artificial Neural Network (ANN) was conducted. Among these, the optimized ANN architecture (6–14-14-14-1) exhibited the highest predictive performance, achieving an accuracy of 95.3%, a Matthews correlation coefficient (MCC) of 0.894, and an F-score of 0.963, demonstrating excellent consistency with DFT-predicted crystal structures. Meanwhile, Random Forest and Gaussian Process models also exhibited reliable and consistent predictive capability, indicating their potential as complementary approaches, particularly when data are limited or computational efficiency is required. This comparative framework provides valuable insights into model selection for crystal system classification in complex cathode materials.

Keywords

Machine learning; crystal structure; classification; cathode materials: batteries

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