AI-Driven Discovery and Modeling of Electrochemical Materials

This chapter delves into the transformative role of Artificial Intelligence (AI) in accelerating the discovery and modeling of electrochemical materials, with a focus on enhancing their performance, sustainability, and industrial relevance. By leveraging AI techniques such as machine learning, deep learning, and reinforcement learning, researchers are addressing key challenges in material optimization, catalyst selection, and performance prediction. The chapter highlights how AI enables rapid screening, predictive modeling, and real-time adaptability, which are critical for advancing technologies such as hydrogen production, battery systems, and electrochemical sensors. Case studies from industry, including AI-integrated electrolyzers, flow battery management, and AI-guided catalyst platforms, illustrate real-world applications and the progression of technology readiness. Furthermore, the integration of AI innovations with the United Nations Sustainable Development Goals (SDGs) is discussed, including direct links to clean energy, climate action, and responsible production. Commercialization pathways, such as patent trends, startup ecosystems, and AI-as-a-Service (AIaaS) models, are examined to contextualize the industrial deployment of these innovations within the frameworks of Industry 4.0 and the hydrogen economy. Concluding insights underscore AI’s growing role in shaping next-generation electrochemical systems through data-driven design, cross-sector collaboration, and sustainable innovation.