Harnessing machine learning to decode and optimize bioelectrochemical systems: Principles, progress and future directions.

Machine learning in bioelectrochemical systems has surged since 2021, with performance prediction and optimization dominating, and microbial fuel cells leading at 42.4% of applications.

• Why it matters: BES are structurally intricate and mechanistically complex, making understanding and optimization difficult; ML is positioned to decode nonlinear mechanisms despite needs for interpretability and generalization.
• What they did: A systematic literature review shows ML BES work dating to 2006, with application domains spanning microbial community analysis, component design, performance optimization, and real-time monitoring, using models like neural networks (30.9%).
• The result: ML shows significant potential for improving BES design, manufacture, operation, and application, but limited transferability remains due to data scarcity and heterogeneity, motivating more data and targeted models.

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