MAMBO: a lightweight ontology for multiscale materials and applications

TL;DR

MAMBO is a lightweight, modular ontology designed to organize knowledge in molecular materials research, facilitating data integration and supporting advanced data-driven methods like machine learning for materials discovery.

Contribution

It introduces MAMBO, a novel ontology that bridges gaps in existing models, enabling systematic knowledge organization across molecular and higher-scale materials research.

Findings

Supports integration of computational and experimental workflows

Enables extensions to broader knowledge domains

Facilitates data-driven materials design and discovery

Abstract

Advancements of both computational and experimental tools have recently led to significant progress in the development of new advanced and functional materials, paralleled by a quick growth of the overall amount of data and information on materials. However, an effective unfolding of the potential of advanced and data-intensive methodologies requires systematic and efficient methods for the organization of knowledge in the context of materials research and development. Semantic technologies can support the structured and formal organization of knowledge, providing a platform for the integration and interoperability of data. In this work, we introduce the Materials and Molecules Basic Ontology (MAMBO), which aims at organizing knowledge in the field of computational and experimental workflows on molecular materials and related systems (nanomaterials, supramolecular systems, molecular aggregates, etc.). Linking recent efforts on ontologies for materials sciences in neighboring domains, MAMBO aims at filling gaps in current state-of-the-art knowledge modelling approaches for materials development and design targeting the intersection between the molecular scale and higher scale domains. With a focus on operational processes, lightweight, and modularity, MAMBO enables extensions to broader knowledge domains and integration of methodologies and workflows related to both computational and experimental tools. MAMBO is expected to advance the application of data-driven technologies to molecular materials, including predictive machine learning frameworks for materials design and discovery and automated platforms.