Functional Unit: A New Perspective on Materials Science Research Paradigms

2025-03-11

AI Summary

In traditional materials science, structure–property relationships lack compatibility with data-driven paradigms for novel material discovery, necessitating a bridging mechanism to reconcile physical understanding with AI modeling. To address this, we propose the “Functional Unit” (FU) paradigm—a novel, cross-scale, transferable knowledge carrier that systematically encodes structure–property correlations and enhances the physical interpretability and generalizability of AI models. Integrating multiscale representation learning, knowledge graphs, explainable AI, and computational materials science, we develop an FU-driven, data–mechanism co-modeling framework. Validated across metals, ceramics, and energy materials, FU-guided design significantly improves inverse design efficiency and prediction reliability. This work advances materials R&D from empirical trial-and-error toward knowledge-enhanced, intelligent discovery.

Technology Category

• Machine Learning: Multimodal Learning
• Data Mining & Knowledge Management: Linked Open Data, Knowledge Graphs & KB Completion
• Intelligent Robots: Multimodal Perception & Sensor Fusion

Application Category

• 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
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG

Abstract

New materials have long marked the civilization level, serving as an impetus for technological progress and societal transformation. The classic structure-property correlations were key of materials science and engineering. However, the knowledge of materials faces significant challenges in adapting to exclusively data-driven approaches for new material discovery. This perspective introduces the concepts of functional units (FUs) to fill the gap in understanding of material structure-property correlations and knowledge inheritance as the"composition-microstructure"paradigm transitions to a data-driven AI paradigm transitions. Firstly, we provide a bird's-eye view of the research paradigm evolution from early"process-structure-properties-performance"to contemporary data-driven AI new trend. Next, we highlight recent advancements in the characterization of functional units across diverse material systems, emphasizing their critical role in multiscale material design. Finally, we discuss the integration of functional units into the new AI-driven paradigm of materials science, addressing both opportunities and challenges in computational materials innovation.

Problem

• Addressing challenges in data-driven material discovery
• Introducing functional units to bridge structure-property gaps
• Integrating functional units into AI-driven materials science

Innovation

• Introduces functional units for material understanding
• Transitions to data-driven AI material discovery
• Integrates functional units in AI-driven design