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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2502.08292 |
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| _version_ | 1866912503494082560 |
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| author | Taghavian, Hamed Vanoppen, Viktor Berg, Erik Broqvist, Peter Sjölund, Jens |
| author_facet | Taghavian, Hamed Vanoppen, Viktor Berg, Erik Broqvist, Peter Sjölund, Jens |
| contents | Metal anodes provide the highest energy density in batteries. However, they still suffer from electrode/electrolyte interface side reactions and dendrite growth, especially under fast-charging conditions. In this paper, we consider a phase-field model of electrodeposition in metal-anode batteries and provide a scalable, versatile framework for optimizing its chemical parameters. Our approach is based on Bayesian optimization and explores the parameter space with a high sample efficiency and a low computation complexity. We use this framework to find the optimal cell for suppressing dendrite growth and accelerating charging speed under constant voltage. We identify interfacial mobility as a key parameter, which should be maximized to inhibit dendrites without compromising the charging speed. The results are verified using extended simulations of dendrite evolution in charging half cells with lithium-metal anodes. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_08292 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Navigating chemical design spaces for metal-ion batteries via machine-learning-guided phase-field simulations Taghavian, Hamed Vanoppen, Viktor Berg, Erik Broqvist, Peter Sjölund, Jens Materials Science Computational Physics Metal anodes provide the highest energy density in batteries. However, they still suffer from electrode/electrolyte interface side reactions and dendrite growth, especially under fast-charging conditions. In this paper, we consider a phase-field model of electrodeposition in metal-anode batteries and provide a scalable, versatile framework for optimizing its chemical parameters. Our approach is based on Bayesian optimization and explores the parameter space with a high sample efficiency and a low computation complexity. We use this framework to find the optimal cell for suppressing dendrite growth and accelerating charging speed under constant voltage. We identify interfacial mobility as a key parameter, which should be maximized to inhibit dendrites without compromising the charging speed. The results are verified using extended simulations of dendrite evolution in charging half cells with lithium-metal anodes. |
| title | Navigating chemical design spaces for metal-ion batteries via machine-learning-guided phase-field simulations |
| topic | Materials Science Computational Physics |
| url | https://arxiv.org/abs/2502.08292 |