Average Voltage Prediction of Battery Electrodes Using Transformer Models with SHAP-Based Interpretability

Mary Vinolisha Antony Dhason, Indranil Bhattacharya (), Ernest Ozoemela Ezugwu and Adeloye Ifeoluwa Ayomide
Additional contact information
Mary Vinolisha Antony Dhason: Department of Electrical and Computer Engineering, Tennessee Technological University, Cookeville, TN 38505, USA
Indranil Bhattacharya: Department of Electrical and Computer Engineering, Binghamton University—State University of New York, Binghamton, NY 13902, USA
Ernest Ozoemela Ezugwu: Department of Electrical and Computer Engineering, Tennessee Technological University, Cookeville, TN 38505, USA
Adeloye Ifeoluwa Ayomide: Department of Electrical and Computer Engineering, Tennessee Technological University, Cookeville, TN 38505, USA

Energies, 2025, vol. 18, issue 17, 1-24

Abstract: 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 aim to mitigate these concerns by employing Machine Learning (ML) techniques. In this study, we propose a Transformer-based deep learning model for predicting the average voltage of battery electrodes. Transformers, known for their ability to capture complex dependencies and relationships, are adapted here for tabular data and regression tasks. The model was trained on data from the Materials Project database. The results demonstrated strong predictive performance, with lower mean absolute error (MAE) and mean squared error (MSE), and higher R 2 values, indicating high accuracy in voltage prediction. Additionally, we conducted detailed per-ion performance analysis across ten working ions and apply sample-wise loss weighting to address data imbalance, significantly improving accuracy on rare-ion systems (e.g., Rb and Y) while preserving overall performance. Furthermore, we performed SHAP-based feature attribution to interpret model predictions, revealing that gravimetric energy and capacity dominate prediction influence, with architecture-specific differences in learned feature importance. This work highlights the potential of Transformer architectures in accelerating the discovery of advanced materials for sustainable energy storage.

Keywords: voltage prediction; transformer ML; electrode potential; data-driven voltage prediction (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/1996-1073/18/17/4587/pdf (application/pdf)
https://www.mdpi.com/1996-1073/18/17/4587/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:18:y:2025:i:17:p:4587-:d:1737326

Access Statistics for this article

Energies is currently edited by Ms. Cassie Shen

More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().