2 papers
cond-mat.mtrl-sci2026
Multimodal Machine Learning for Integrating Heterogeneous Analytical Systems
Shun Muroga, Hideaki Nakajima, Taiyo Shimizu +2
Understanding structure-property relationships in complex materials requires integrating complementary measurements across multiple length scales. Here we propose an interpretable…
cond-mat.mtrl-sci2025
Explainable Multimodal Machine Learning for Revealing Structure-Property Relationships in Carbon Nanotube Fibers
Daisuke Kimura, Naoko Tajima, Toshiya Okazaki +1
In this study, we propose Explainable Multimodal Machine Learning (EMML), which integrates the analysis of diverse data types (multimodal data) using factor analysis for feature ex…