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