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

TL;DR

This paper presents a validated computational framework combining machine learning and experimental data to enable realistic atomistic modeling of complex metallosilicates under operando conditions.

Contribution

It introduces an end-to-end approach that integrates simulation, machine learning, and experimental validation for complex materials modeling.

Findings

Successfully reproduces experimental densities and spectra.

Validates the framework on mesoporous SiO2-Al2O3 materials.

Enables analysis of catalytic active sites and vibrations.

Abstract

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.