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Poster
in
Workshop: AI4Mat-2024: NeurIPS 2024 Workshop on AI for Accelerated Materials Design

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

Daoyuan Li · Shuquan Cui · Mahesh Mahanthappa · Frank Bates · Timothy Lodge · Joern Ilja Siepmann

Keywords: [ Small-Angle X-ray Scattering (SAXS) ] [ Mixture of Experts (MoE) ] [ Self-Attention ] [ Self-Assembly ] [ Order-Disorder Transition (ODT) ] [ Temporal Convolutional Network (TCN) ]


Abstract:

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.

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