# Machine Learning Framework for Characterizing Processing-Structure Relationship in Block Copolymer Thin Films

arXiv: https://arxiv.org/abs/2505.23064 · HTML: https://mlsanity.com/paper/2505.23064/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 86 references that are in the map.
- First posted on arXiv 29 May 2025.

## Builds on (most cited first, 6 of 6)

- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [A Unified Approach to Interpreting Model Predictions](https://mlsanity.com/paper/1705.07874/) (2017)
- [Polymer Informatics: Current Status and Critical Next Steps](https://mlsanity.com/paper/2011.00508/) (2020)
- [MLExchange: A web-based platform enabling exchangeable machine learning workflows for scientific studies](https://mlsanity.com/paper/2208.09751/) (2022)
- [Machine Learning for Analyzing Atomic Force Microscopy (AFM) Images Generated from Polymer Blends](https://mlsanity.com/paper/2409.11438/) (2024)
- [Hexagonal patterns in a simplified model for block copolymers](https://mlsanity.com/paper/1305.6540/) (2013)

## Abstract (arXiv)

The morphology of block copolymers (BCPs) critically influences material properties and applications. This work introduces a machine learning (ML)-enabled, high-throughput framework for analyzing grazing incidence small-angle X-ray scattering (GISAXS) data and atomic force microscopy (AFM) images to characterize BCP thin film morphology. A convolutional neural network was trained to classify AFM images by morphology type, achieving 97% testing accuracy. Classified images were then analyzed to extract 2D grain size measurements from the samples in a high-throughput manner. ML models were developed to predict morphological features based on processing parameters such as solvent ratio, additive type, and additive ratio. GISAXS-based properties were predicted with strong performances (R2 > 0.75), while AFM-based property predictions were less accurate (R2 < 0.60), likely due to the localized nature of AFM measurements compared to the bulk information captured by GISAXS. Beyond model performance, interpretability was addressed using Shapley Additive exPlanations (SHAP). SHAP analysis revealed that the additive ratio had the largest impact on morphological predictions, where additive provides the BCP chains with increased volume to rearrange into thermodynamically favorable morphologies. This interpretability helps validate model predictions and offers insight into parameter importance. Altogether, the presented framework combining high-throughput characterization and interpretable ML offers an approach to exploring and optimizing BCP thin film morphology across a broad processing landscape.