An actor–critic algorithm to maximize the power delivered from direct methanol fuel cells
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OpenAlex topics: Fuel Cells and Related Materials · Electrocatalysts for Energy Conversion · Hybrid Renewable Energy Systems
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References (36)
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1. Yao, Z. et al. Machine learning for a sustainable energy future. Nat. Rev. Mater. 8, 202–215 (2022).10.1038/s41578-022-00490-5 ↗
2. Attia, P. M. et al. Closed-loop optimization of fast-charging protocols for batteries with machine learning. Nature 578, 397–402 (2020).10.1038/s41586-020-1994-5 ↗
3. Jordan, M. I. & Mitchell, T. M. Machine learning: trends, perspectives, and prospects. Science 349, 255–260 (2015).10.1126/science.aaa8415 ↗
4. Rosen, A. S. et al. Machine learning the quantum-chemical properties of metal–organic frameworks for accelerated materials discovery. Matter 4, 1578–1597 (2021).10.1016/j.matt.2021.02.015 ↗
5. Yao, Z. et al. Inverse design of nanoporous crystalline reticular materials with deep generative models. Nat. Mach. Intell. 3, 76–86 (2021).10.1038/s42256-020-00271-1 ↗
6. Jiao, K. et al. Designing the next generation of proton-exchange membrane fuel cells. Nature 595, 361–369 (2021).10.1038/s41586-021-03482-7 ↗
7. Zhu, G. et al. Rechargeable Na/Cl2 and Li/Cl2 batteries. Nature 596, 525–530 (2021).10.1038/s41586-021-03757-z ↗
8. Wu, Y., Jiang, Z., Lu, X., Liang, Y. & Wang, H. Domino electroreduction of CO2 to methanol on a molecular catalyst. Nature 575, 639–642 (2019).10.1038/s41586-019-1760-8 ↗
9. Davidson, D. J. Exnovating for a renewable energy transition. Nat. Energy 4, 254–256 (2019).10.1038/s41560-019-0369-3 ↗
10. Feng, Y., Liu, H. & Yang, J. A selective electrocatalyst–based direct methanol fuel cell operated at high concentrations of methanol. Sci. Adv. 3, e1700580 (2017).10.1126/sciadv.1700580 ↗
11. Martín, A. J., Mitchell, S., Mondelli, C., Jaydev, S. & Pérez-Ramírez, J. Unifying views on catalyst deactivation. Nat. Catal. 5, 854–866 (2022).10.1038/s41929-022-00842-y ↗
12. Poerwoprajitno, A. R. et al. A single-Pt-atom-on-Ru-nanoparticle electrocatalyst for CO-resilient methanol oxidation. Nat. Catal. 5, 231–237 (2022).10.1038/s41929-022-00756-9 ↗
13. Wang, J. et al. Toward electrocatalytic methanol oxidation reaction: longstanding debates and emerging catalysts. Adv. Mater. 35, 2211099 (2023).10.1002/adma.202211099 ↗
14. Xu, H. et al. A cobalt-platinum-ruthenium system for acidic methanol oxidation. Chem. Mater. 36, 6938–6949 (2024).10.1021/acs.chemmater.4c01008 ↗
15. Timoshenko, J. et al. Steering the structure and selectivity of CO2 electroreduction catalysts by potential pulses. Nat. Catal. 5, 259–267 (2022).10.1038/s41929-022-00760-z ↗
16. Xu, L. et al. In situ periodic regeneration of catalyst during CO2 electroreduction to C2+ products. Angew. Chem. Int. Ed. 61, e202210375 (2022).10.1002/anie.202210375 ↗
17. Zhang, X.-D. et al. Asymmetric low-frequency pulsed strategy enables ultralong CO2 reduction stability and controllable product selectivity. J. Am. Chem. Soc. 145, 2195–2206 (2023).10.1021/jacs.2c09501 ↗
18. Rabissi, C., Brightman, E., Hinds, G. & Casalegno, A. In operando investigation of anode overpotential dynamics in direct methanol fuel cells. Int. J. Hydrogen Energy 41, 18221–18225 (2016).10.1016/j.ijhydene.2016.08.140 ↗
19. Rabissi, C., Brightman, E., Hinds, G. & Casalegno, A. In operando measurement of localised cathode potential to mitigate DMFC temporary degradation. Int. J. Hydrogen 43, 9797–9802 (2018).10.1016/j.ijhydene.2018.04.043 ↗
20. Bresciani, F., Casalegno, A., Bonde, J. L., Odgaard, M. & Marchesi, R. A comparison of operating strategies to reduce DMFC degradation: a comparison of operating strategies to reduce DMFC degradation. Int. J. Energy Res. 38, 117–124 (2014).10.1002/er.3115 ↗
21. Kusne, A. G. et al. On-the-fly closed-loop materials discovery via Bayesian active learning. Nat. Commun. 11, 5966 (2020).10.1038/s41467-020-19597-w ↗
22. Szymanski, N. J. et al. An autonomous laboratory for the accelerated synthesis of novel materials. Nature 624, 86–91 (2023).10.1038/s41586-023-06734-w ↗
23. Severson, K. A. et al. Data-driven prediction of battery cycle life before capacity degradation. Nat. Energy 4, 383–391 (2019).10.1038/s41560-019-0356-8 ↗
24. Seo, J. et al. Avoiding fusion plasma tearing instability with deep reinforcement learning. Nature 626, 746–751 (2024).10.1038/s41586-024-07024-9 ↗
25. Mnih, V. et al. Human-level control through deep reinforcement learning. Nature 518, 529–533 (2015).10.1038/nature14236 ↗
26. Silver, D. et al. Mastering the game of go with deep neural networks and tree search. Nature 529, 484–489 (2016).10.1038/nature16961 ↗
27. Silver, D. et al. Mastering the game of go without human knowledge. Nature 550, 354–359 (2017).10.1038/nature24270 ↗
28. Schulman, J., Wolski, F., Dhariwal, P., Radford, A. & Klimov, O. Proximal policy optimization algorithms. Preprint at https://arxiv.org/abs/1707.06347 (2017).
29. Haarnoja, T., Zhou, A., Abbeel, P. & Levine, S. Soft actor–critic: off-policy maximum entropy deep reinforcement learning with a stochastic actor. In Proc. 35th International Conference on Machine Learning 1861–1870 (PMLR, 2018).
30. Hessel, M. et al. Rainbow: combining improvements in deep reinforcement learning. In Thirty-second AAAI Conference on Artificial Intelligence (AAAI-18) 3215–3222 (AAAI, 2018).10.1609/aaai.v32i1.11796 ↗
31. Mnih, V. et al. Playing Atari with deep reinforcement learning. Preprint at https://arxiv.org/abs/1312.5602 (2013).
32. Panzer, M. & Bender, B. Deep reinforcement learning in production systems: a systematic literature review. Int. J. Prod. Res. 60, 4316–4341 (2022).10.1080/00207543.2021.1973138 ↗
33. Degrave, J. et al. Magnetic control of tokamak plasmas through deep reinforcement learning. Nature 602, 414–419 (2022).10.1038/s41586-021-04301-9 ↗
34. Shorten, C. & Khoshgoftaar, T. M. A survey on image data augmentation for deep learning. J. Big Data 6, 60 (2019).10.1186/s40537-019-0197-0 ↗
35. Kort-Kamp, W. J. M. et al. Machine learning-guided design of direct methanol fuel cells with a platinum group metal-free cathode. J. Power Sources 626, 235758 (2025).10.1016/j.jpowsour.2024.235758 ↗
36. Sweigart, A. PyAutoGUI documentation. Read the Docs https://pyautogui.readthedocs.org (2020).