Chapter 14: A Holistic Treatise on Artificial Intelligence (AI) and Machine Learning (ML) Tools for Green Supercapacitors

Rapidly finding an environmentally friendly solution to store and use renewable energy to address environmental issues like global warming is the main aim of contemporary energy research. Electrochemical energy storage technologies, such as supercapacitors, fuel cells, and rechargeable and flow batteries, have recently gained much attention. Supercapacitors possess notable advantages such as higher capacitance, fast charging, high power densities, and long cycle life over common capacitors and batteries. The performance-related properties, such as cyclic stability or specific capacity of these supercapacitors made from environmentally sustainable green materials, mainly depend on their intrinsic features, such as types of electrolytes, electrode materials, additives, and working conditions. The prediction and optimization of these parameters with existing atomistic approaches are highly nontrivial. Artificial Intelligence (AI) and Machine Learning (ML) algorithms can be utilized to find a correlation between these inherent features and supercapacitor performances. AI and ML find applications in various areas, including design and optimization, degradation process analysis, failure detection, prediction, and the correlation between macroscale performance, and micro/nanoscale material attributes of these green supercapacitor systems. In this chapter, we present a thorough analysis of recent developments and applications of AI and ML along with computational tools in building green supercapacitors for energy storage.