Data science accelerates energy device development

29 March 2024, Version 1
This content is a preprint and has not undergone peer review at the time of posting.


Data science has become increasingly prevalent in the development of energy devices, offering significant advancements in predicting future behaviors and identifying optimal process parameters in a resource-saving manner. This perspective begins by examining the role of data science and ML in enhancing accelerated aging tests across solar, battery and fuel cells. We present a generalizable data-driven workflow for processing aging test data and predicting the lifespan of different device types. In this perspective, we discuss two strategies to improve our understanding of device failures: integrating physics-based parameters and utilizing interpretable machine learning (ML) techniques. Following a brief review on ML-assisted process optimization, we propose an interpretable closed-loop platform towards digital manufacturing for thin-film solar and Li-ion battery production. Finally, we discuss the current challenges and research gaps in applying data science for accelerated energy device development, aiming to spark further investigation in this field.


Renewable energy devices
Data science
Accelerated aging tests
Lifetime prediction
Physics-based parameters
Interpretable machine learning
Bayesian optimization
Digital manufacturing
Closed-loop optimization


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