Integrating Density Functional Theory with Deep Neural Networks for Accurate Voltage Prediction in Alkali-Metal-Ion Battery Materials

Abstract

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 fully connected deep neural network (FCDNN) 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 FCDNN 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.