Automated chromatography yielded an extensive library of materials with high-quality selfassembly across a wide range of volume fractions (f F = 0.02 -0.80). Representative scattering patterns from six distinct morphologies (disordered (DIS), BCC, σ, HEX, GYR, and LAM) are plotted in Figure , where the blue curve represents the scattering intensities after noise filtration via a twice differentiable Gaussian process with a Matérn kernel. We have innovated an approach that integrates experimental uncertainties in filtering and denoising with minimal computational demand, which is crucial for processing experimental data. In X-ray diffraction, certain peaks may be absent or suppressed due to factors like poor resolution or limited long-range order. For example, the σ pattern typically requires synchrotron-level capabilities to resolve its intricate diffraction pattern often comprised of circa 48 distinct peaks, whereas using traditional bench-top SAXS instruments would impart significant peak broadening despite analyzing the same nanostructure. Excessive smoothing can obscure these critical small or overlapping peaks, whereas insufficient smoothing might misinterpret noise as peaks. The smoothing level in Gaussian process regression is controlled by the range and nugget parameter of the covariance, which are determined using the maximum likelihood estimate from all DIS samples in our dataset. Comprehensive details on the formulae for smoothing and peak detection are provided in the SI. show the first primary scattering peak location against the ratio between the first and second peaks and the ratio between the second and third peaks. These plots illustrate that physics-informed features, such as ratios between peak locations, facilitate a more effective structural determination than traditional methods which analyze the entire scattering curve.