MOTIFNet: Automating the Analysis of Amphiphile and Block Polymer Self-Assembly from SAXS Data

Accurately classifying morphology and assessing stability in soft matter self-assembly often require specialized analysis of small-angle X-ray scattering (SAXS) data, creating an obstacle to automation. To address this, we introduce MOTIFNet, a simplified sparse mixture of experts (MoE) model with top-1 routing. By combining temporal convolution and self-attention, MOTIFNet effectively processes SAXS time series data, enabling morphology classification, SAXS pattern prediction, and the estimation of order-disorder transition (ODT) probabilities. This model advances automated characterization, accelerating experimentation and high-throughput studies in soft matter self-assembly.