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

2026-04-29

AI Summary

This study addresses the limitations of traditional equivalent circuit models (ECMs), which rely on manual trial-and-error by experts and thus hinder scalability in autonomous experimentation. To overcome this, the work formulates ECM generation as a Markov decision process and introduces a reinforcement learning–based sequential decision-making approach. A cycle-mitigation strategy is specifically designed to handle the complex action space inherent in circuit construction. Leveraging a Double DQN algorithm with prioritized experience replay, alongside a dedicated circuit-generation environment and training framework, the trained agent achieves over 99.6% modeling success on synthetic data. Moreover, it demonstrates strong generalization capabilities across diverse real-world electrochemical impedance spectroscopy (EIS) datasets, enabling fully data-driven, automated ECM construction.

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.

Problem

• equivalent circuit model
• electrochemical impedance spectroscopy
• automated model generation
• reinforcement learning
• self-driving laboratories

Innovation

• reinforcement learning
• equivalent circuit model
• electrochemical impedance spectroscopy
• Double Deep Q-Network
• automated electrochemistry