An experimentally validated end-to-end framework for operando modeling of intrinsically complex metallosilicates

Structurally and chemically complex materials such as amorphous metallosilicates underpin major catalytic and separation technologies, yet their intrinsic complexity challenges reliable atomistic modeling under realistic conditions. Consequently, simulations that connect composition to material properties remain largely inaccessible for these materials. Here, we enable quantitative operando atomistic modeling of intrinsically complex materials through an experimentally validated end-to-end computational framework. The approach combines separation of simulation domains, lightweight machine-learning potentials trained on high-fidelity data, and large-scale de novo in silico synthesis that mimics experimental procedures. We apply the framework to realistic mesoporous SiO$_2$(Al$_2$O$_3$)$_{x/2}$ (0 $\leq x \leq$ 0.4) and validate the results experimentally. Simulations quantitatively reproduce multiple experimental observables, including bulk densities, pair distribution functions, infrared spectra, and hydroxyl densities. Beyond prediction, the framework enables analysis of acid sites and vibrations for catalytic and adsorption processes. By integrating simulation and experiment within a unified workflow, we advance the realism and reliability of atomistic modeling for intrinsically complex materials.