Authors:Ali Jaberi (1), Yonatan Kurniawan (2), Robert Black (1), Shayan Mousavi M. (1), Kabir Verma (3), Zoya Sadighi (1), Santiago Miret (4), Jason Hattrick-Simpers (2) ((1) Clean Energy Innovation Research Center, National Research Council Canada, Mississauga, ON, Canada, (2) Department of Material Science and Engineering, University of Toronto, Toronto, ON, Canada, (3) Cheriton School of Computer Science, University of Waterloo, Waterloo, ON, Canada, (4) Lila Sciences, San Francisco, CA, USA)
Abstract:This paper introduces AutoREC, an open-source Python package for developing reinforcement learning (RL) agents to automatically generate equivalent circuit models (ECMs) from electrochemical impedance spectroscopy (EIS) data. While ECMs are a standard framework for interpreting EIS data, traditional identification is typically based on manual trial-and-error, which requires domain experts and limits scalability, particularly in autonomous experimental pipelines such as self-driving laboratories. AutoREC addresses this challenge by formulating ECM construction as a sequential decision-making problem within a Markov Decision Process framework. It implements a Double Deep Q-Network with prioritized experience replay, along with a dedicated dead-loop mitigation strategy, to efficiently explore a complex action space for circuit generation. To demonstrate the capabilities of the platform, we trained an RL agent using AutoREC and evaluated its strengths and limitations across diverse datasets, while also discussing possible strategies to mitigate these limitations in future agent designs. The trained agent achieved a success rate exceeding $99.6\%$ on synthetic datasets and demonstrated strong generalization to unseen experimental EIS data from batteries, corrosion, oxygen evolution reaction, and CO$_2$ reduction systems. These results position AutoREC as a promising platform for adaptive and data-driven ECM generation, with potential for integration into automated electrochemical workflows.
| Subjects: | Machine Learning (cs.LG); Materials Science (cond-mat.mtrl-sci) |
| Cite as: | arXiv:2604.27266 [cs.LG] |
| (or arXiv:2604.27266v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2604.27266 arXiv-issued DOI via DataCite (pending registration) |
From: Yonatan Kurniawan [view email]
[v1]
Wed, 29 Apr 2026 23:42:27 UTC (10,914 KB)
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