Montmorillonite plays a central role in environmental barrier systems because its hydrated interlayers, high cation-exchange capacity (CEC), and structural flexibility strongly influence heavy-metal retention. However, predictive evaluation across varying humidity histories, cation identities, and solution chemistries remains limited when based solely on empirical trends. In this study, experimentally established X-ray diffraction (XRD) observations from the literature—describing discrete hydration states (0 W/1 W/2 W), mixed-layer interstratification, and the cation-dependent classification of swelling behaviour—are incorporated as structural constraints within a physics-informed machine-learning (ML) framework. A curated dataset (N = 600), augmented with descriptors reflecting hydration energy, ionic radius, layer-charge proxies, CEC, pH, and ionic strength, is used to train gradient-boosting and ensemble models. Model performance on held-out data reaches R² ≈ 0.68, which is consistent with nonlinear interactions between hydration-controlled descriptors. Molecular-dynamics (MD) simulations are employed to validate predicted basal-spacing trends and to capture the Å-scale interlayer reorganizations—such as heterogeneous water-sheet distributions and cation-specific coordination environments—that underlie continuous swelling beyond discrete hydration states. By aligning ML attributions with established structural behavior from XRD studies and validating them against MD-resolved microscopic configurations, the resulting framework provides a rapid, interpretable, and physically consistent approach. Physical knowledge is incorporated through structure-aware feature design and class-consistent priors rather than through explicit governing equations, making the approach best described as physics-guided machine learning for predicting heavy-metal uptake by montmorillonite and guiding the design of robust clay-based barrier materials.
Walid Oueslati (Fri,) studied this question.