M-CODE: Materials Categorization via Ontology, Dimensionality and Evolution

2026-02-16

AI Summary

This work addresses the lack of effective, standardized methods for classifying and managing complex structural features—such as surfaces, interfaces, defects, and dimensional variations—in materials science. To this end, we propose M-CODE, a compact ontological framework that uniquely integrates structure dimensionality, complexity (spanning from pristine to processed states), and evolutionary pathways into a unified classification system. By leveraging reusable conceptual modules and provenance-aware transformations, M-CODE enables semantic-rich, extensible material descriptions. Built upon JSON Schema and Python/TypeScript type systems, the accompanying open-source toolchain facilitates data generation, validation, and community-driven collaboration, thereby significantly enhancing the reproducibility and interoperability of materials data.

Technology Category

• Knowledge Representation and Reasoning: Ontologies
• Machine Learning: Feature Construction/Reformulation
• Data Mining & Knowledge Management: Representing, Reasoning, and Using Provenance, Trust

Application Category

• Semantics and Knowledge: Scalable techniques for the creation, curation, publication, maintenance, and consumption of large, Web-based, structured, reusable, knowledge graphs and ontologies
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Web Mining and Content Analysis: Bridging structured and unstructured data

Abstract

The rapid advancement of artificial intelligence in materials science requires data standards and data management practices that can capture the complexity of real-world structures, including surfaces, interfaces, defects, and dimensionality reduction. We present M-CODE - Materials Categorization via Ontology, Dimensionality and Evolution - a compact categorization system that links materials-science-specific terminology to a set of reusable concepts as building blocks and provenance-aware transformations. M-CODE classifies structures by dimensionality, structural complexity (from pristine to compound pristine, defective, and processed), and variants that capture common structure creation and evolution approaches. A practical implementation of the categorization is provided in an open-source codebase that includes JSON schemas, examples, and Python and TypeScript types/interfaces, designed to support reproducible dataset generation, validation, and community contributions.

Problem

• materials science
• data standards
• structural complexity
• dimensionality
• defects

Innovation

• M-CODE
• ontology
• dimensionality
• structural complexity
• provenance-aware data