MOTIFNet: Automating the Analysis of Amphiphile and Block Polymer Self-Assembly from SAXS Data
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.
Cite
Text
Li et al. "MOTIFNet: Automating the Analysis of Amphiphile and Block Polymer Self-Assembly from SAXS Data." NeurIPS 2024 Workshops: AI4Mat, 2024.Markdown
[Li et al. "MOTIFNet: Automating the Analysis of Amphiphile and Block Polymer Self-Assembly from SAXS Data." NeurIPS 2024 Workshops: AI4Mat, 2024.](https://mlanthology.org/neuripsw/2024/li2024neuripsw-motifnet/)BibTeX
@inproceedings{li2024neuripsw-motifnet,
title = {{MOTIFNet: Automating the Analysis of Amphiphile and Block Polymer Self-Assembly from SAXS Data}},
author = {Li, Daoyuan and Cui, Shuquan and Mahanthappa, Mahesh and Bates, Frank and Lodge, Timothy and Siepmann, Joern Ilja},
booktitle = {NeurIPS 2024 Workshops: AI4Mat},
year = {2024},
url = {https://mlanthology.org/neuripsw/2024/li2024neuripsw-motifnet/}
}