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2026
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| Online Access: | https://doi.org/10.5281/zenodo.19987300 |
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| author | Kurniawan, Yonatan Jaberi, Ali Black, Robert Mousavi Masouleh, Seyed Shayan Verma, Kabir Sadighi, Zoya Miret, Santiago Hattrick-Simpers, Jason |
| author_facet | Kurniawan, Yonatan Jaberi, Ali Black, Robert Mousavi Masouleh, Seyed Shayan Verma, Kabir Sadighi, Zoya Miret, Santiago Hattrick-Simpers, Jason |
| contents | <h1>Data Supporting the AutoREC Paper</h1> <p>This repository contains the data, trained models, and evaluation results used to support the results reported in the AutoREC paper.</p> <h2>Contents</h2> <ul> <li>dataset/: Contains the EIS datasets used in the paper, including:<br> <ul> <li>training_dataset.pkl: Synthetic dataset used for model training.</li> <li>test_dataset.pkl: Synthetic dataset excluded from training and used for testing.</li> <li>battery_dataset.pkl: Experimental battery dataset.</li> <li>experimental_dataset.pkl: Additional experimental datasets, ordered as CO2 reduction, corrosion, and OER systems.</li> </ul> </li> <li>models/: Contains the trained AutoREC agent used to generate the results reported in the paper. The folder includes:<br> <ul> <li>dqn_model.keras: Keras model file for the trained DQN agent.</li> <li>config.yaml: YAML file containing the training configuration and hyperparameters used for the DQN agent.</li> <li>training_reward.pkl: Pickle file containing the reward history during DQN training.</li> <li>no_deadloop/: Contains the corresponding files for a DQN agent trained without dead-loop mitigation.</li> </ul> </li> <li>eval_results/: Contains the RL agent evaluation results for the EIS datasets, including:<br> <ul> <li>training.pkl: Evaluation results for the training dataset.</li> <li>test.pkl: Evaluation results for the test dataset.</li> <li>battery.pkl: Evaluation results for the battery dataset.</li> <li>experimental.pkl: Evaluation results for the additional experimental datasets.</li> <li>training_trials_0-9900/: Evaluation results for the training dataset across different training checkpoints.</li> <li>test_trials_0-9900/: Evaluation results for the test dataset across different training checkpoints.</li> </ul> </li> </ul> <h2>Code repository</h2> <p>The AutoREC source code is available in the <a href="https://github.com/AUTODIAL/AutoREC">AutoREC GitHub repository</a>.</p> <p>The data in this repository are compatible with commit <a href="https://github.com/AUTODIAL/AutoREC/commit/c63e63d19b46747d4cf2e97c76ad9286e500ac8f">c63e63d</a>.</p> <h2>Contact</h2> <p>For questions related to the data or calculation results, please contact:</p> <ul> <li>Yonatan Kurniawan: <a href="mailto:yonatan.kurniawan@utoronto.ca" rel="noopener">yonatan.kurniawan@utoronto.ca</a></li> </ul> |
| format | Recurso digital |
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| institution | Zenodo |
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| publishDate | 2026 |
| publisher | Zenodo |
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| spellingShingle | Data supporting the results in the AutoREC paper Kurniawan, Yonatan Jaberi, Ali Black, Robert Mousavi Masouleh, Seyed Shayan Verma, Kabir Sadighi, Zoya Miret, Santiago Hattrick-Simpers, Jason <h1>Data Supporting the AutoREC Paper</h1> <p>This repository contains the data, trained models, and evaluation results used to support the results reported in the AutoREC paper.</p> <h2>Contents</h2> <ul> <li>dataset/: Contains the EIS datasets used in the paper, including:<br> <ul> <li>training_dataset.pkl: Synthetic dataset used for model training.</li> <li>test_dataset.pkl: Synthetic dataset excluded from training and used for testing.</li> <li>battery_dataset.pkl: Experimental battery dataset.</li> <li>experimental_dataset.pkl: Additional experimental datasets, ordered as CO2 reduction, corrosion, and OER systems.</li> </ul> </li> <li>models/: Contains the trained AutoREC agent used to generate the results reported in the paper. The folder includes:<br> <ul> <li>dqn_model.keras: Keras model file for the trained DQN agent.</li> <li>config.yaml: YAML file containing the training configuration and hyperparameters used for the DQN agent.</li> <li>training_reward.pkl: Pickle file containing the reward history during DQN training.</li> <li>no_deadloop/: Contains the corresponding files for a DQN agent trained without dead-loop mitigation.</li> </ul> </li> <li>eval_results/: Contains the RL agent evaluation results for the EIS datasets, including:<br> <ul> <li>training.pkl: Evaluation results for the training dataset.</li> <li>test.pkl: Evaluation results for the test dataset.</li> <li>battery.pkl: Evaluation results for the battery dataset.</li> <li>experimental.pkl: Evaluation results for the additional experimental datasets.</li> <li>training_trials_0-9900/: Evaluation results for the training dataset across different training checkpoints.</li> <li>test_trials_0-9900/: Evaluation results for the test dataset across different training checkpoints.</li> </ul> </li> </ul> <h2>Code repository</h2> <p>The AutoREC source code is available in the <a href="https://github.com/AUTODIAL/AutoREC">AutoREC GitHub repository</a>.</p> <p>The data in this repository are compatible with commit <a href="https://github.com/AUTODIAL/AutoREC/commit/c63e63d19b46747d4cf2e97c76ad9286e500ac8f">c63e63d</a>.</p> <h2>Contact</h2> <p>For questions related to the data or calculation results, please contact:</p> <ul> <li>Yonatan Kurniawan: <a href="mailto:yonatan.kurniawan@utoronto.ca" rel="noopener">yonatan.kurniawan@utoronto.ca</a></li> </ul> |
| title | Data supporting the results in the AutoREC paper |
| url | https://doi.org/10.5281/zenodo.19987300 |