Machine Learning in Proton Exchange Membrane Water Electrolysis -- Part I: A Knowledge-Integrated Framework

The integration of Machine Learning (ML) with domain-specific knowledge is a pivotal advancement in predictive modeling [1, 2]. This combination has brought a new level of precision and insight to fields within engineering and environmental sciences [3, 4]. While the synergy has notably improved accuracy and decision-making processes [5, 6], the challenge of seamlessly blending domain knowledge with ML algorithms continues to evolve. To bridge this gap, the Ladder of Knowledge-integrated Machine Learning has been introduced [7]. This framework aims to optimize the utilization of domain-specific insights, offering a comprehensive approach to integrating prior knowledge information into ML applications. Inspired by the long debate between holistic and reductionist approaches in ML [8], the framework aims firstly to synergize multidisciplinary domain knowledge with data-driven processes in two principal dimensions: firstly, by identifying and understanding the complementary nature of uncertainties in data, knowledge-based methodologies, and data-driven methods; secondly, by exploring knowledge decomposition from various perspectives and aligning these insights with our paradigm. Finally, building upon the previous two foundations in the specific domain context, the ladder unfolds across three progressive levels of integrating domain expertise into ML approaches [7]. In the pursuit of sustainable energy solutions, Proton Exchange Membrane Water Electrolyzers (PEMWEs) stand out for their high energy efficiency and minimal environmental impact [9] in hydrogen production.