Average Voltage Prediction of Battery Electrode Materials Using Transformer Deep Learning Architecture

Batteries are ubiquitous, with their presence ranging from electric vehicles to portable electronics. Research focused on increasing average voltage, improving stability, and extending cycle longevity of batteries is pivotal for the advancement of battery technology. These advancements can be accelerated through research into battery chemistries. The traditional approach, which examines each material combination individually, poses significant challenges in terms of resources and financial investment. Physics-based simulations, while detailed, are both time-consuming and resource-intensive. Researchers are aiming to mitigate these concerns by employing Machine Learning (ML) techniques. We propose the use of a Transformer ML model for predicting the average voltage of battery electrode materials. A Transformer is a Deep Learning (DL) architecture based on the attention mechanism; it dynamically focuses on different parts of the input sequence, thus capturing complex relationships and dependencies among input features. The absence of recurrent units allows the model to require less training time compared to other DL models. We have adapted the Transformer model for tabular data and regression tasks. Our dataset was sourced from the Materials Project Database. To ensure the model's compatibility with our voltage prediction regression task, we successfully trained and tested it using four features across 99 samples. Our ongoing research focuses on incorporating more relevant features and expanding the training and testing of the model to the entire dataset. Our work demonstrates the effectiveness of ML, especially the Transformer model, in predicting the average voltage of battery electrode materials.