AutoREC: A software platform for developing reinforcement learning agents for equivalent circuit model generation from electrochemical impedance spectroscopy data

TL;DR

AutoREC is an open-source Python platform that uses reinforcement learning to automatically generate equivalent circuit models from electrochemical impedance spectroscopy data, reducing manual effort and enabling autonomous analysis.

Contribution

The paper introduces AutoREC, a novel RL-based software platform for automated ECM generation from EIS data, with demonstrated high success rates and generalization capabilities.

Findings

RL agent achieved over 99.6% success rate on synthetic datasets.

AutoREC demonstrated strong generalization to unseen experimental data.

The platform enables adaptive, data-driven ECM generation for automated electrochemical workflows.

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.