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Deep kernel Bayesian optimisation for closed-loop electrode microstructure design with user-defined properties

delete2025-09-10
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AI
A
Andrea Gayon-Lombardo *
E
Ehecatl Antonio del Rio‐Chanona
C
Catalina A. Pino-Muñoz
N
Nigel P. Brandon
DOI：10.1016/j.egyai.2025.100608delete
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摘要

摘要

En 中文
• 集成训练生成对抗网络（GAN）和贝叶斯优化的闭环微观结构设计。 • 高斯过程（GP）作为代理模型，将生成器的潜在空间映射到电极性能。 • 同时最大化相关的形貌和传输性能。 • 约束优化在增强微观结构性能的同时保持负载。 • 通过潜在空间优化进行定制电极的微观结构设计。
Keyword:
Gaussian processes
Deep kernel Bayesian optimisation
Lithium-ion battery cathode
Multi-phase porous electrodes
Microstructure design
Specific surface area
Relative diffusivity
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Energy and AI
IF:
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论文数:
903
被引数:
3.1K

机构

I
Imperial College London
学者数:
8.3W
论文数: 7.3W
被引数: 11.1W
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