Multimodal Machine Learning for Integrating Heterogeneous Analytical Systems

2026-01-31

AI Summary

This study addresses the challenge of integrating and interpreting heterogeneous, multiscale characterization data of carbon nanotube (CNT) thin films arising from microstructural variations. To this end, the authors propose an interpretable multimodal machine learning framework—the first application of multimodal explainable AI to multiscale materials characterization. The approach fuses multimodal data, including morphological features extracted from SEM images via binarization, skeletonization, and network analysis, along with Raman spectroscopy, specific surface area, and surface resistivity measurements. By combining UMAP for dimensionality reduction and XGBoost for nonlinear regression, the framework enables end-to-end performance prediction. Under leave-one-out cross-validation, the model achieves optimal predictive accuracy. Feature importance analysis reveals that surface resistivity is primarily governed by inter-junction transport length, crystallinity, and network connectivity, while specific surface area is dominated by junction density and pore size, thereby elucidating the microstructure–property relationships in CNT films.

Technology Category

• Machine Learning: Multimodal Learning
• Computer Vision: Multi-modal Vision
• Intelligent Robots: Multimodal Perception & Sensor Fusion

Application Category

• Web Mining and Content Analysis: Mining multimedia, multimodal, multilingual, cross-lingual Web data
• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• 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

Understanding structure-property relationships in complex materials requires integrating complementary measurements across multiple length scales. Here we propose an interpretable"multimodal"machine learning framework that unifies heterogeneous analytical systems for end-to-end characterization, demonstrated on carbon nanotube (CNT) films whose properties are highly sensitive to microstructural variations. Quantitative morphology descriptors are extracted from SEM images via binarization, skeletonization, and network analysis, capturing curvature, orientation, intersection density, and void geometry. These SEM-derived features are fused with Raman indicators of crystallinity/defect states, specific surface area from gas adsorption, and electrical surface resistivity. Multi-dimensional visualization using radar plots and UMAP reveals clear clustering of CNT films according to crystallinity and entanglements. Regression models trained on the multimodal feature set show that nonlinear approaches, particularly XGBoost, achieve the best predictive accuracy under leave-one-out cross-validation. Feature-importance analysis further provides physically meaningful interpretations: surface resistivity is primarily governed by junction-to-junction transport length scales, crystallinity/defect-related metrics, and network connectivity, whereas specific surface area is dominated by intersection density and void size. The proposed multimodal machine learning framework offers a general strategy for data-driven, explainable characterization of complex materials.

Problem

• multimodal machine learning
• heterogeneous analytical systems
• structure-property relationships
• complex materials
• carbon nanotube films

Innovation

• multimodal machine learning
• interpretable AI
• heterogeneous data fusion
• structure-property relationships
• carbon nanotube characterization