Multimodal Machine Learning for Data-Driven Materials Selection: A Review of Foundations, Advances, and Future Directions

Abstract

Data-driven material selection is progressively changing how materials are evaluated in engineering, manufacturing, and product design. With the growing diversity of heterogeneous data sources—ranging from microscopic images and physicochemical properties to simulation outputs and textual data—multimodal machine learning (MML) has become a key technology for fusing different types of information and supporting reliable multi-objective decision-making. However, despite increasing research interest, there is still no unified and conceptually structured review on the application of MML methods in data-driven material selection. This paper provides a thorough and structured overview of the field, organized using a novel multidimensional classification framework based on data types, integration levels, learning paradigms, and decision-making tasks. Guided by this framework, we critically evaluate the strengths, limitations, and general applicability of existing methods, and identify current trends and research gaps. Beyond qualitative synthesis, we quantify the cited corpus (N = 94) by publication year, method family, and application area, and collate an empirical fusion-evidence table that reports each study’s quantified gain together with its boundary conditions. We further analyze the main challenges and present a forward-looking research agenda covering self-supervised learning, knowledge-enhanced models, and interpretable human–AI collaboration. This review aims to offer a conceptual framework and a solid reference for building intelligent multimodal material selection systems.