Tailoring Materials Design for Aqueous Energy Storage and Conversion through Electrochemical Reconstruction

techniques for probing dynamic processes. Furthermore, we outline the growing role of artificial intelligence (AI)-driven methods in facilitating material discovery and process optimization, emphasizing the underlying principles of scientific synthesis and analysis. Besides, we discuss the remaining challenges and offer new insights, aiming to inspire future research in machine-learning-assisted design and fabrication of advanced materials for energy storage and conversion.