Speakers
Description
Small-angle X-ray scattering (SAXS) is widely used to determine the structure of nanoparticles and biomolecules in solution. A central challenge is reconstructing the pair distance distribution function, $p(r)$, from the measured scattering intensity, $I(q)$. The $p(r)$ function describes the distribution of distances between scattering centers within a particle and provides valuable information about its size and overall shape. Traditionally, $p(r)$ is obtained using indirect Fourier transform methods, which rely on iterative optimization and regularization.
In this project, we investigate whether machine learning can learn the relationship between $I(q)$ and $p(r)$ directly. Using a large dataset of simulated SAXS data generated with Shape2SAS, we trained and compared a multilayer perceptron (MLP) and a one-dimensional convolutional neural network (1D CNN) to predict $p(r)$ from scattering curves. We also examined how shape complexity and model architecture affect reconstruction accuracy.
Both models successfully recovered physically meaningful pair distance distributions, while the 1D CNN consistently produced the most accurate predictions, particularly for more complex particle shapes. Our results demonstrate that machine learning can rapidly recover structural information from SAXS data and highlight its potential as a complementary tool for SAXS analysis.