2025/03/17 by Sk Mujaffar Hossain, Hossain, Sk Mujaffar, Namitha Anna Koshi +7
Engineering · Materials Science · #Advanced Battery Technologies Research #Advancements in Battery Materials #Chemical Physics (physics.chem-ph) #FOS: Physical sciences #Machine Learning in Materials Science #Materials Science (cond-mat.mtrl-sci)
paper · pdf · doi:10.48550/arxiv.2503.13067
openalex publication_date 2025/03/17 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Accurate prediction of the voltage of battery materials plays a pivotal role in the advancement of energy storage technologies and the rational design of high-performance cathode materials. In this work, we present a deep neural network (DNN) model, built using PyTorch, to estimate the average voltage of cathode materials across Li-ion, Na-ion, and other alkali-metal-ion batteries. The model is trained on an extensive dataset from the Materials Project, incorporating a wide range of specific structural, physical, chemical, electronic, thermodynamic, and battery descriptors, ensuring a comprehensive representation of material properties. Our model exhibits strong predictive performance, as corroborated by first-principles density functional theory (DFT) calculations. The close alignment between the DNN predictions and the DFT outcomes highlights the robustness and accuracy of our machine learning framework to effectively select and identify viable battery materials. Using this validated model, we successfully proposed novel Na-ion battery compositions, with their predicted behavior confirmed by rigorous computational assessment. By seamlessly integrating data-driven prediction with first-principles validation, this study presents an effective framework that significantly accelerates the discovery and optimization of advanced battery materials, contributing to the development of more reliable and efficient energy storage technologies.