Neutron‐Based Mechanistic Mapping Across the Li–S Battery Landscape: Operando Methods, Standardization, and AI‐Assisted Data Fusion

ABSTRACT Neutron techniques provide exceptional sensitivity to light elements, buried interfaces, and bulk transport, making them uniquely suited for probing lithium–sulfur (Li–S) batteries, yet their application remains fragmented and insufficiently standardized. This Review develops a unified mechanistic mapping framework that links neutron observables to Li–S descriptors across multiple length and time scales, spanning fundamental and application‐relevant regimes. Imaging and tomography, small‐angle scattering (SANS), diffraction, reflectometry, quasielastic neutron spectroscopy (QENS), neutron depth profiling (NDP), and prompt‐gamma activation analysis (PGNAA) are integrated to resolve sulfur redox reactions, polysulfide transport, electrolyte and separator behavior, and lithium anode degradation under operando conditions. Practical guidelines are summarized for operando cell architectures, isotopic‐contrast design using H/D and 6 Li substitution, optimization of neutron flux and q‐range, and uncertainty‐aware data treatment. To address limitations in temporal resolution, chemical selectivity, and cross‐study comparability, the Review introduces an AI‐assisted, model‐based data‐fusion framework combining physics‐informed preprocessing with Bayesian optimization and neural‐network modeling. This approach enables quantitative reconstruction of sulfur‐phase evolution, interphase growth, and Li 2 S morphology under realistic conditions. A concluding roadmap identifies priority measurements, standardized metadata schemes, and benchmarks to accelerate reproducible Li–S research across global neutron facilities.