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

2025-05-29

AI Summary

Low-throughput morphological characterization and poorly understood processing–structure relationships hinder the rational design of block copolymer (BCP) thin films. Method: We developed a high-throughput machine learning framework integrating grazing-incidence small-angle X-ray scattering (GISAXS) and atomic force microscopy (AFM). A convolutional neural network (CNN) enables automated AFM morphology classification (97% accuracy), while multivariate regression models GISAXS-derived structural features (R² > 0.75). SHAP (SHapley Additive exPlanations) analysis is introduced for the first time to quantify the influence of processing parameters. Contribution/Results: This work presents the first multimodal (GISAXS + AFM) AI-driven framework for BCP morphological characterization. It identifies additive concentration as the most critical processing parameter, enabling high-throughput grain-size quantification and parameter importance ranking. The framework significantly enhances both predictive accuracy and interpretability in establishing structure–processing correlations for BCP thin films.

Technology Category

• Machine Learning: Multimodal Learning
• Natural Language Processing: Lexical Semantics and Morphology
• Humans and AI: Brain-Sensing and Analysis

Application Category

• Web Mining and Content Analysis: Machine learning and data science for the Web
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Semantics and Knowledge: Methods, algorithms and applications for the development of semantic models, knowledge graphs and other forms of structured data models with machine-interpretable semantics

Abstract

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 ($R^2$>0.75), while AFM-based property predictions were less accurate ($R^2$<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.

Problem

• Develop ML framework to analyze BCP thin film morphology
• Predict morphological features from processing parameters
• Enhance interpretability of ML models using SHAP analysis

Innovation

• ML framework analyzes GISAXS and AFM data
• CNN classifies AFM images with 97% accuracy
• SHAP explains additive ratio's impact on morphology