Advances in the Development of Sol-Gel Materials Combining Small-Angle X-ray Scattering (SAXS) and Machine Learning (ML)

The requirements for new materials are increasing with each application, which, in most cases, means an enhancement the complexity of development process. Nanoporous sol-gel-based materials, especially aerogels, promising candidates thermal superinsulation, electrodes energy conversion and storage or high-end adsorbers. Their synthesis processing route is complex, relationship between material/processing parameters resulting structural physical properties not straightforward. Using small-angle X-ray scattering (SAXS) allows fast characterization both gel aerogel; combining these results respective aerogels using data as inputs machine learning (ML) algorithms provide approach to predict on basis a dataset. This data-driven strategy may be feasible speed up Thus, study aimed proof concept ML-based model derivation from material, process SAXS such solid-phase conductivity (λs) silica Here, we used different subsets predictors according states (wet dry) evaluate performance.