Deep Neural Network-Based Voltage Prediction for Alkali-Metal-Ion Battery Materials

Abstract: Accurate voltage prediction of battery materials plays a pivotal role in advancing energy storage technologies and in 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 descriptors-structural, physical, chemical, electronic, thermodynamic, and battery-specific-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 DFT outcomes highlights the robustness and accuracy of our machine learning framework in effectively screening and identifying viable battery materials. Utilizing this validated model, we successfully propose novel Na-ion battery compositions, with their predicted behavior confirmed through 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.