Molecular‐level visualization of ion transport and separation dynamics in complex environments is crucial for advancing energy systems, water purification, and critical materials recovery. Achieving this requires imaging platforms that combine structural sensitivity, chemical specificity, and real‐time operation. Digital off‐axis holography (DOAH) provides high‐throughput, label‐free quantitative phase imaging but inherently lacks chemical selectivity. Integrating DOAH with complementary spectroscopic channels such as fluorescence or hyperspectral imaging introduces the needed molecular specificity, while also creating challenges in multimodal data fusion, synchronization, and computational throughput. Artificial intelligence (AI) offers a powerful route to address these limitations by uniting physics‐based reconstruction with data‐driven interpretation. In this perspective, we outline a framework for intelligent multimodal holography and demonstrate its potential using a preliminary AI‐driven test case. Raw DOAH holograms of lanthanide solutions subjected to magnetic field gradients were analyzed using multi‐agent AI workflows that autonomously selected reconstruction tools, extracted NMF components, and generated scientific claims consistent with the expected paramagnetic and diamagnetic behavior. This demonstration shows how AI‐enabled reasoning can deliver real‐time chemical–structural interpretation directly from raw holograms. Together, these advances define a path toward adaptive, intelligent holography platforms capable of supporting in situ chemical separations, dynamic ion transport analysis, and next‐generation interfacial science.
Ricchiuti et al. (Sun,) studied this question.